Dysbiosis of intestinal microbiota may be linked to pathogenesis of obesity and metabolic disorders.  This study compared the gut microbiome of obese Thai children with that of healthy controls and examined their relationships with host lifestyle, adiposity, and metabolic profiles.  This cross-sectional study enrolled obese children aged 7–15. Body composition was evaluated using bioelectrical impedance analysis. Stool samples were analyzed by 16S rRNA sequencing using the Illumina MiSeq platform. Relative abundance and alpha- and beta-diversity were compared with normal-weight Thai children from a previous publication using Wilcoxon rank-sum test and ANOSIM. Relationships of gut microbiota with lifestyle activity, body composition, and metabolic profiles were assessed by canonical correlation analysis (CCA) and Spearman correlation. z-score of 3.2 ± 1. The abundance of Bacteroidetes and Actinobacteria were found to be lower in obese children compared to nonobese children. Alpha-diversity indices showed no differences between groups, while beta-diversity revealed significant differences in the family and genus levels. CCA revealed significant correlations of the relative abundance of gut microbial phyla with sedentary lifestyle and certain metabolic markers. Univariate analysis revealed that Actinobacteria and Bifidobacterium were positively correlated with HDL-C and negatively correlated with body weight and screen time. Additionally, Actinobacteria was also negatively associated with fasting insulin and HOMA-IR. Lactobacillus showed positive correlation with acanthosis nigricans and adiposity. Cooccurrence analysis revealed 90 significant bacterial copresence and mutual exclusion interactions among 43 genera in obese children, whereas only 2 significant cooccurrences were found in nonobese children.  The study enrolled 164 obese children with a male percentage of 59%. Mean age was 10.4 ± 2.2 years with a BMI  The composition and diversity of gut microbiota in obese Thai children were different from those of their normal-weight peers. Specific gut microbiota were associated with lifestyle, adiposity, and metabolic features in obese children. An interventional study is needed to support causality between specific gut microbiota and obesity. Obesity is a major health concern with a dramatic increase in both developed and developing countries. According to the World Health Organization (WHO), over 1.6 billion adults are overweight, and more than 400 million adults are obese . Obese cWhile it is generally accepted that the development of obesity is caused by gene-environment interactions, physiological and environmental predispositions underlying obesity, and associated metabolic disorders are still unclear. Recent studies have suggested that intestinal microbiota is possibly linked to host-microbe interactions regarding energy harvest and regulation and plays a crucial role in the pathogenesis of obesity , 5. The Lactobacillus [Faecalibacterium prausnitzii, and obesity [Bifidobacterium was associated with leanness [Differences in gut microbial communities are correlated with obesity, which can lead to a reduction in the diversity of gut microbiota , 6. Prevbacillus  and Faec obesity . In contleanness , 11. Preleanness . It was leanness , 12. Morleanness , 8, 12. leanness , 13. To In the present study, we aimed to determine the composition and diversity of gut microbiota in obese Thai children compared to those of nonobese children. A secondary aim was to identify associations of composition and diversity of the microbiota with children's body composition, metabolic profiles, and lifestyle activity.n = 17) and in rural area  were 10.5 ± 0.7 years, 45.6 ± 12.2 kg, 145.4 ± 11.1 cm, and 21.3 ± 4.0 kg/m2, respectively, and 9.8 ± 0.6 years, 34.4 ± 8.0 kg, 139.5 ± 7.4 cm, and 17.4 ± 2.9 kg/m2, respectively. Males were 81% and 43% of nonobese children in Bangkok and Buriram, respectively. Microbiome analysis in the previous study was completed on the variable region, V1–V2, of 16S rRNA sequencing using Illumina MiSeq v3 technology .This investigation was a cross-sectional study conducted from August 2017 to July 2020. The inclusion criteria were (1) children aged 7 to 15 years and (2) BMI > median + 2 SD based upon WHO growth reference . The excDietary intake was evaluated by a dietician using 24-hour dietary recall. The daily energy, percentage of energy distributions, fiber intake, and nutrients intake were calculated using INMUCAL-Nutrients Version 3  . Physica2), and BMI z-scores were calculated based on WHO 2007 growth reference using the WHO AnthroPlus program [Anthropometric measurements, including body weight, height, waist, and hip circumferences, were collected by trained personnel. BMI was calculated as weight in kilograms divided by height squared in meters  was measured by the hexokinase method . Fasting insulin (FI) was measured using Immulite/Immulite 1000 Insulin assay . Serum total cholesterol, HDL-cholesterol (HDL-C), and triglycerides were measured by enzymatic colorimetric assay . LDL-cholesterol (LDL-C) was measured by homogeneous liquid selective detergent . Serum alanine aminotransferase (ALT) was determined according to the standard of International Federation of Clinical Chemistry . Homeostatic model assessment for insulin resistance (HOMA-IR) was calculated from (FI × FPG)/22.5, where FI and FPG were fasting insulin concentration (mU/l) and fasting plasma glucose (mmol/l), respectively .g for 1 min. Supernatant was collected for DNA isolation using QIAamp fast DNA Stool Mini Kit  according to the manufacturer's instructions. DNA integrity and concentration were quantitated using the Qubit dsDNA HS assay  on a Qubit 4 Fluorometer . The V3–V4 hypervariable region of the 16S ribosomal RNA (rRNA) gene was PCR-amplified using specific primers [Participants were instructed to collect fecal samples in their households. Sterile stool collection kits, containers, and temperature-controlled packages were provided. A fresh stool was placed into a half of 50 ml sterile container and double-sealed in a zip-lock bag. The sample was stored in the freezer compartment of a home refrigerator, delivered to the laboratory within 24 hours, and stored at −80°C until use. Fecal samples were resuspended in InhibitEX Buffer , incubated at 70°C for 5 min, and centrifuged at 20,000 × The DNA amplicon of the bacterial 16s rRNA gene was PCR-amplified using a specific primer targeting the V3–V4 region. The PCR products were run on agarose gel electrophoresis followed by gel extraction, and purification using the NucleoSpin Gel and PCR Clean-Up Kit (Macherey-Nagel). The Illumina's indices were added to both ends of the PCR products to allow for multiplexing samples. Indexed PCR products were then cleaned using Agencourt AMPure XP beads (Beckman Coulter Inc.). DNA concentration was quantitated using a Qubit dsDNA HS Assay Kit (Life Technologies). The exact size of indexed libraries was checked with QIAxcel capillary electrophoresis  before library pooling, and sequencing using paired-ends (2 × 301 bp) on Illumina MiSeq with Illumina V3 Reagent Kit.Readings from Illumina paired-end sequencing were trimmed, merged, filtered, and subsequently assigned to taxonomic classes using the QIIME2 platform release 2020.8 . Initialsilva-132-99-nb pretrained Naïve Bayes model available on QIIME2. This classifier was trained using full-length 16S rRNA sequences from SILVA database [Representative sequences that passed the quality filtering were assigned to taxonomic classes using the database . The conAnalysis of composition of microbiomes (ANCOM)  was usedphylogeny module in QIIME2. UniFrac distances between samples were calculated using the diversity-lib module in QIIME2.The sample-taxa frequency table was re-summarized at the phylum and genus levels by summing read counts belonging to the same phylum or genus together. Operational taxonomic units (OTUs) with unassigned phylum or genus were discarded from further analyses at the respective levels . Shannon, Simpson, and Chao1 alpha-diversity indices were calculated for each sample based on their mathematical definitions . To evalP value cutoff at 0.05 was applied.For cooccurrence analysis, rare genera with average abundance of less than 0.1% were excluded because they are typically identified with very few reads, and their abundances may not be sufficiently estimated by our sequencing data. This filter reduced the number of genera from 296 to 72, respectively. Cooccurrence of two genera across 164 patient samples was calculated using Jaccard index which is the ratio of the number of samples that contain both genera to the number of samples that contain at least one of the genera. A hypergeometric model  was thenP value of less than 0.05 was considered as statistically significant. Spearman rank correlations were calculated between the abundances of each taxon and the values of each clinical variable across 164 samples. P values for each correlation value were estimated using a permutation test  against the null hypothesis in which there was no association between the two variables. The overall false discovery rate was controlled within 5% by Benjamini–Hochberg procedure. Partial Spearman correlations were calculated for comparisons between taxa and metabolic profiles adjusted for effects from age, sex, and BMI using the partial_corr function in the pingouin Python library. Canonical correlation analyses (CCA) between microbial abundances and clinical variable groups were performed using CCA function in scikit-learn Python library [n-th canonical correlation and all subsequent correlations are zero. CCA were performed on the clinical and metabolic variable groups.Descriptive data analysis was performed using SPSS Statistics version 23 . The normality of data was tested using the Shapiro–Wilk test. Normally distributed data were presented as means with SD, while non-normally distributed data were reported as medians with quartiles . A  library . Signifi library  which te2, and 41.7 ± 5.1%, respectively. Mean energy intake was 1,450 ± 537.9 kcal/day. Dietary fiber intake was about 2.8 g/1000 kcal. Caloric distribution was 48% carbohydrate, 16% protein, and 36% fat. The participants were classified into participating in high (49%), moderate (40%), and low (8%) intensity exercise regimens. Only 3% of participants did not report doing any exercise. The questionnaire on lifestyle patterns revealed that the median time spent for screen time, such as watching television or using electronic devices, was 7  hours/day during weekdays, and 8  hours/day during weekends. Median sedentary activity was 4  hours/day. The prevalence of high SBP, hypercholesterolemia, high LDL-C, low HDL-C, and hypertriglyceridemia was 9%, 37%, 42%, 10%, and 11%, respectively.A total of 164 obese children were enrolled in this study. The demographic data are shown in P=0.012 and P=0.003)  showed no significant differences between obese and nonobese children. For beta-diversity evaluation, PCoA based on Bray–Curtis dissimilarity tests was performed and showed no significant differences at the phylum levels between obese and nonobese children . There w Figures . It shouP < 0.001, Wilcoxon rank-sum test). In contrast, obese children showed significantly greater abundance of the average proportion of Proteobacteria and Fusobacteria than the nonobese children at the significant level (P < 0.001). No significant difference in Firmicutes was observed between populations. The ratio of Firmicutes to Bacteroidetes (F/B) was not significantly different in obese children compared to nonobese children. However, the ANCOM test demonstrated that there were significant differences between obese and nonobese children in all bacterial phyla (P < 0.0001).The relative abundance of the bacterial composition by the phylum and family levels is illustrated in Bifidobacterium was significantly higher in the nonobese compared to the obese children (P < 0.0001), while the abundance of Blautia and Lactobacillus was significantly lower in the nonobese children (P=0.0035 and P=0.0053)  . The relr = 0.64, P < 0.0001)  . No correlations between any bacterial phyla and adiposity were observed. After adjusting for the confounding effects from age, sex, and BMI, Actinobacteria showed the strongest correlations with metabolic profiles including HDL-C , FI , and HOMA-IR . Firmicutes was not correlated with BMI z-score or any metabolic profiles.Any significant correlation of gut microbial phyla, family, and genus with patient-level variables was examined by the univariate analysis. At the phyla level, Actinobacteria was negatively associated with body weight and weekday screen time  but negatively associated with body weight , total energy intake , and screen time  . Lactobacillus and Collinsella showed significant positive correlations with acanthosis nigricans . The relationship of gut microbiota at the genus level with body composition indices showed that Lactobacillus was significantly and positively associated with fat mass (FM) , fat mass index (FMI) , body fat percentage , trunk FMI , and visceral fat area (VFA) .At the genus level, =0.0002) . BlautiaP < 0.001, Wilcoxon rank-sum test). The Lachnospiraceae NK4A136 group showed copresence with many other bacteria in obese children. In contrast, the cooccurrence network at the genus level for nonobese children showed only two statistically significant bacterial cooccurrences of Ruminococcaceae UCG-002 and Eubacterium coprostanoligenes group, and of Erysipelatoclostridium and Tyzzerella .To explore the interactions between gut microbiota in obese and nonobese children, a cooccurrence analysis was performed at the genus level. This analysis revealed 90 statistically significant bacterial copresence (positive) and mutual exclusion (negative) interactions among 43 genera in obese children . An abunzzerella . CompareThis present study determined the composition and diversity of gut microbiota in obese Thai children, compared to nonobese Thai children, and examined microbial associations with clinical and metabolic parameters.This study examined 164 stool samples from obese children and adolescents. The results showed that gut microbial alpha-diversity, an index reflecting the variety of microbial species, from gastrointestinal tracts of obese children showed no difference compared to nonobese children. This finding was consistent with previous studies , 31. HowThe results of the gut microbiota composition analysis showed that the predominant phylum was Firmicutes followed by Bacteroidetes, Proteobacteria, Actinobacteria, and Fusobacteria in obese children, similar to studies in Italy and Japan , 33. StuBifidobacterium was present in significantly lower abundance in obese children, as found in a previous study [Lactobacillus and Blautia showed higher abundance in obese children, as in the study from Mexico [Our study revealed a difference in gut bacterial composition in obese children compared to nonobese children, who had both Firmicutes and Bacteroidetes as the predominant taxa. A difference in Firmicutes abundance between obese and nonobese children was not found in our study, as in previous studies , 34. Altus study . Howeverm Mexico , 38.The relationship of gut microbiota with lifestyle and metabolic profiles was observed in this study. Healthy dietary habits and physical activity have been shown to impact obesity and metabolic outcomes . Recent CCA results found significant correlations of certain gut microbial phyla with sedentary activity and metabolic profiles. These findings could give crucial insight into the host-microbe relationship. Therefore, we further explored the association between specific gut microbiota and those variables by univariate analysis. A positive correlation of Actinobacteria and HOMA-IR was consistent with a previous study . This prBifidobacterium was positively correlated with HDL-C, which was compatible with Actinobacteria as it belongs to this phylum. However, Bifidobacterium was negatively correlated with body weight, total energy intake, and screen time. These results were consistent with a previous study showing that Bifidobacterium decreased in obese individuals compared to normal-weight people [Bifidobacterium spp. and caused inflammation, obesity, insulin resistance, while the number of Bacteroides and Clostridium spp. increased with such a diet [Bifidobacterium and total energy intake that might indirectly result from high-fat intake by obese children.The associations at the genus level showed that t people . The resh a diet , 43. TheBifidobacterium. One study reported that electronic device usage close to bedtime may disrupt sleep patterns, a factor associated with obesity [Regarding lifestyle activity, the use of electronic devices is becoming a major driver in the reduction of physical activity in the context of pediatric obesity . The pre obesity . This un obesity .Lactobacillus was correlated with acanthosis nigricans and adiposity including FM, FMI, body fat percentage, trunk FMI, and VFA with an increased relative abundance of this genus in obese children. These results were consistent with previous studies finding that Lactobacillus was associated with weight gain in humans [Lactobacillus spp. reported weight gain with the use of L. rhamnosus. Some studies have shown an association between L. reuteri and obesity [L. reuteri might improve the ability to absorb and process nutrients in the gut [Lactobacillus species were associated with weight loss both in obese humans and in obese animals [Lactobacillus species produce different effects on weight that may be host-specific. Further studies are needed to clarify the role of Lactobacillus in human energy harvest and weight regulation.We found that n humans , 46, 47  obesity , 49. One the gut . In cont animals , 52, indCollinsella were associated with acanthosis nigricans in obese children in our study. This genus was demonstrated to be positively correlated with insulin [Collinsella might imply the possibility of insulin resistance in obese children which would support our findings. Blautia was associated with fat distribution of total energy intake in obese children. A previous study found that this genus was related to a high-fat diet causing obesity in mice [Bacteria of the genus  insulin  and elev insulin . High le in mice .Lachnospiraceae NK4A136 group, a member of family Lachnospiraceae and phylum Firmicutes, showed the highest number of positive interactions, which suggested that this genus member has mutualistic relationships with other bacteria. The previous report showed that the members of the Lachnospiraceae family were correlated with type 2 diabetes and obesity [Prevotellaceae UCG-003 and Tyzzerella and one between Coprococcus and Ruminococcus gnavus. Similar to previous reports [Cooccurrence analysis of gut microbiota revealed a significant difference between obese and nonobese children. We found that obese children had denser clustering of interactions between gut microbial genera that were similarly observed in previous studies , 31, 32. obesity , 57. Mor obesity . Lachnos obesity . Althoug obesity . A previ obesity . In lipi obesity . In obes reports , 31, 32,This study is the largest investigation documenting the gut microbiota composition in obese children. Since it is limited to only Thai children, the study is less influenced by ethnicity and some environmental factors. In addition to comparing the microbiota composition and diversity between obese and nonobese children, the relationships of microbial composition to clinical data, dietary intake, lifestyle activity, body composition, and metabolic profiles analyzed through both CCA and univariate analysis were determined. We also presented the results of a cooccurrence analysis of the gut microbiota in obese and nonobese children that can lead to an increased understanding of mutualistic relationships between bacterial genera. The limitations of this study include a small sample of nonobese children from the other study with some limitations due to the difference in hypervariable regions analyzed. Some children lived in the same area, but some did not. In addition, since the study employed a cross-sectional study design, support for any notion of causality is limited. Therefore, an interventional study to normalize dysbiosis of gut microbiota as a therapeutic target of pediatric obesity and its complications is warranted.Bifidobacterium. Lactobacillus also showed a positive relationship with insulin resistance, acanthosis nigricans, and adiposity. Restoring gut microbial dysbiosis could be part of a therapeutic strategy to target pediatric obesity. Any interventions to modulate beneficial microbes and their metabolites could be advantageous.The composition of gut microbiota in obese Thai children differed from that in nonobese children. The beta-diversity also showed significant differences in gut microbial community structure between obese and nonobese children. Our study found significant mutualistic relationships between bacterial genera in the gut microbiomes of obese children that are completely absent in nonobese children. In obese children, Actinobacteria had a negative association with clinical features linked to obesity, sedentary lifestyle, and metabolic profiles related to insulin resistance, which was compatible with the specific genus


The cell identity of malignant cells and how they acquire it are fundamental for our understanding of cancer. Here, we report that esophageal squamous cell carcinoma (ESCC) cells display molecular features equally similar but distinct to all three types of normal esophageal epithelial cells, which we term as confused cell identity (CCI). CCI is an independent prognostic marker associated with poor prognosis in ESCC. Further, we identify tropomyosin 4 (TPM4) as a critical CCI gene that promotes the aggressiveness of ESCC in vitro and in vivo. And TPM4 creates CCI through activating the Jak/STAT-SOX2 pathway. Thus, our study suggests an unrecognized feature of ESCC cells, which might be of value for clinic prognosis and potential interference. The hallmarks of cancer are the basic features of and fundamental principles underlying malignancies, which have been gradually revealed over the last half-century through extensive studies on the process of tumorigenesis and by comparing tumor cells with their normal counterparts.11 One of the best examples might be acute myeloid leukemia (AML), which has been suggested to be transformed from hematopoietic stem and progenitors with differentiation-blocking mutations.13 And thus, it is generally believed that cancer cells which are more like stem cells are more aggressive than those with differentiation features.15 However, there are remarkable exceptions, such as multiple myeloma, which is generally thought to be developed from mast cells, the terminally differentiated B lymphocytes.16 And recently, it has been argued that the so-called cancer stem cells and differentiated cells could two-way transit to each other, which, together with other arguments, challenges the developmental view of tumorigenesis.19According to the traditional developmental view of tumorigenesis, it has long been proposed that tumorigenesis is an unusual event of normal development and malignant cells arise from the stem and/or progenitor cells with differentiation block.20 LKB1 mutations can promote the transit of lung adenocarcinoma into squamous cell carcinoma (SCC).21 And it has been proposed that a high-plasticity cell state might underlie lung adenocarcinoma with poor outcomes and chemoresistance.23 Similarly, p53 and Rb1 loss increase the lineage plasticity of antiandrogen resistant prostate cancer into neuroendocrine-like cancer.26 Thus, in the latest version, the unlocked phenotypic plasticity was added as a new hallmark of cancer.2Recently, there is accumulating evidence suggesting that the lineage identity of cancer cells might be dynamic. For example, a subset of non-small cell lung cancer (SCLC) with estimated glomerular filtration rate mutations developed resistance of tyrosine kinase inhibitors and transformed into SCLC with RB1 loss.29 The molecular and cellular pathologies of SCC, one of the most common epithelial cancers with worse outcomes than others, are less understood. This type of cancer seems to be differentiated toward squamous epithelial cells to some extent and thus distinguishes it from epithelial stem cells. On the other hand, patients with low-grade differentiation have a poor prognosis.29 To resolve these paradoxes and gain a better understanding of the cellular identity and the underlying molecular mechanisms of SCC, we analyzed the cellular identity of esophageal SCC (ESCC) at single-cell resolution in the background of normal epithelial cells with a discovery cohort of seven tumor samples and one distal normal sample from five pathologically confirmed ESCC patients. We then validated the results in a cohort of 60 patients analyzed by single-cell RNA-seq (scRNA-seq) and 9 cohorts of a total of 534 patients analyzed by bulk RNA-seq or microarray assays.However, the cell identity of most types of cancers, especially those derived from epithelial cells, which consist of more than 90% of human malignancies, remains unclear.30 Total 44,206 cells were obtained, including 16,657 normal and malignant squamous epithelial cells. The squamous epithelial cells, glandular epithelial cells, immune cells, endothelial cells, and other tumor microenvironmental cells were separated and identified with classic markers     Fig. . More im02) Fig. . Further DK Fig. . And the DK Fig. . Similar45) Fig. . To testThen we explored the molecular mechanism underlying CCI in ESCC. The CCI score was positively correlated with many cancer pathways, including those related to cancer stemness, metastasis, and epithelial cell proliferation , which, by itself, had the highest diagnostic value in ESCC patients revealed by the multivariant Cox regression analysis  were recruited from West China School of Medicine/West China Hospital of Sichuan University  between 2018 and 2019. None of the subjects underwent radiotherapy, chemotherapy, or immunotherapy prior to surgery. ESCC tumor tissue and normal adjacent tissue (≧5 cm from the tumor margin) were obtained after Lugol’s iodine solution staining. The pathological diagnosis was made by pathologists in the Department of Pathology, West China Hospital, Sichuan University, who confirmed that there were no tumor cells in normal tissues and no necrosis in tumor tissues, and the proportion of tumor cells in tumor tissues was more than 60%. Written informed consent was obtained from every subject and this study was approved by the Institutional Review Board of WCSM/WCH.Individuals with ESCC  for at least 48 h.TE10 (#CL-0453) were purchased from Procell Corp and cultured in RPMI-1640 medium with 10% FBS supplemented, 10% heat-inactivated FBS  and 1% penicillin–streptomycin . All cell lines were tested negative for mycoplasma. TE10 cell lines were infected with the SpCas9 lentiviral vector with sgRosa26-CAG-Cas9-iRES-GFP mice hereafter referred to as Cas9 , Trp53/−− mice were obtained from The Jackson Laboratory . Recipient nude mice were purchased from Hfkbio (Cat# BALB/cA-nu Mice). All animal experiments were performed in accordance with relevant guidelines and regulations and were approved by the West China Hospital, Sichuan University. Mice were deeply anesthetized by gas inhalation isoflurane before surgery or euthanasia.All mice used in this study were maintained in the SPF level mouse facility at State Key Laboratory of Biotherapy of Sichuan University. All the animal experiment protocols have been approved by the institutional Animal Care and Use Committees of Sichuan University. g, 5 min and washed once with phosphate-buffered saline (PBS) subsequently. After the liquid in the centrifuge tube was removed, the Matrigel  was used to resuspend the cell pellet. After being mixed on ice evenly, 30 μl of the mixture was added to the bottom of the 48-well plates to form a hemispherical shape. After solidification, organoid medium was applied  medium supplemented with GlutaMax , N2 Supplement , B27 Supplement , 1 mM N-acetylcysteine , 10 mM Nicotinamide , 50 ng/mL human epidermal growth factor , and 100 ng /mL fibroblast growth factor-10 , 10 μm Y27632 , 500 nM A83-01 , 10% conditioned medium containing Wnt3a (homemade), Noggin and R-spondin (homemade). After 3–4 days, the esophageal organoids would be seen. For passage, organoids were dissociated by TrypLETM Express  at 37 °C for 10 min. The process disrupted the spherical organoids into cell aggregates or single cell that were then embedded in fresh Matrigel.Six to eight weeks old male mouse was sacrificed for culture. Esophagus tissue was separated and rinsed with cold phosphate-buffered saline buffer  supplemented with antibiotics  with vigorous shaking. After several times washing transferred the tissue to a new 15 ml centrifuge tube and minced with fine scissors. Then 5 ml 0.25% trypsin–EDTA  was added to digest the tissues in a 37 °C water bath for 1 h. When most of the tissue dissolved, the digestion reaction was stopped by a serum containing DMEM medium. Filtered the mixture through a 70-μm cell strainer  to obtain a single-cell suspension, then centrifuged at 400TM PCR system (Applied Biosystems).Five-hundred microlitre DNA lysis buffer  was used to lysis cells at 55 °C for at least 2 h. The supernatant was combined with 500μL isopropanol, mixed, and centrifuged at 13,000 rpm for 10 min. Removed the supernatant and then washed with 1 mL 75% ethanol once. Volatilized at 55 °C for about 5 min, the DNA pellet was then dissolved in double-distilled water. One-hundred nanogram DNA was used for each genotyping PCR reaction. PCR reactions were performed on a ProFlexTPM4 and Tpm4 cDNA were cloned into the LentiCRISPR-v2 puro (RRID: Addgene_52961) lentiviral vector, the full-length KrasG12Dand cMyc cDNA was cloned into the MSCV- cDNA -IRES-luciferase retroviral vector, and the guide RNAs were cloned into the vectors as previously described. All plasmids’ sequences were confirmed by Sanger sequencing. Subsequently, these resulting plasmids were transfected with the helper plasmids psPAX2 (RRID: Addgene_12260), pMD2.G (RRID: Addgene_12259) into HEK293T  cells by the calcium phosphate transfection method. The virus in the supernatant at 36 and 48 h were respectively collected. And all the sequences of sgRNA used in this study were listed in Supplementary Table The full-length TM for 15–30 min at 37 °C in a 15 ml conical tube. Pipetted several times every 5 min until most of them became single cells and then centrifuged at 400g for 5 min. The cells were resuspended with lentiviral supernatant and centrifuged for 1 h at 800g followed by another 2 h incubating at 37 °C. After that, cells were harvested and seeded with Matrigel in a 48-well plate. Two days later, 1 μg ml−1 puromycin  was added to select the positive cells for several passages. To validate the efficiency of CRISPR in purified clones, their genome was extracted for PCR reaction and then T7E1 enzymes were performed. Sanger sequencing was used to validate the mutation status finally.Organoids were disassociated with TrypLE2) × 0.5. For AZ960 treatment, after transplantation 1 week, mice were injected intraperitoneally with AZ960  at a dose of 40 mg/kg or vehicle daily.After one week of cells/organoids transplantation, the length and width of the tumor were measured using calipers every 3 days. Tumor size was calculated with the formula like this:  in PBS intraperitoneally injected.After transplanted subcutaneously or orthotopically with organoids, mice would be periodically imaged to detect the luciferase fluorescence signal intensity with IVIS Spectrum (PerkinElmer) system. The mice were anesthetized with isoflurane after 250 μl 15 mg/ml Pten, Smad4, and overexpression of Myc and KrasG12D in the esophageal organs of Trp53−/−; Cas9 mice (TPSCK). The organoid model of this genotype could form ESCC at four weeks in immunodeficient mice. The H&E and IHC staining had confirmed the pathological characteristic of ESCC generated from TPSCK organoids. Of note, organoids should be dissociated to approximately single cells using TrypLETM mentioned above before subcutaneous and orthotopic transplantation. For the AZ960 treatment experiment, one well of TPSCK/ TPSCK Tpm4 tumor organoids were injected subcutaneously into the flank of nude mice . For tumor formation of orthotopic implantation from primary TPSCK/TPSCK Tpm4 overexpressed organoids, 2 wells of organoids were collected with 30 μl of the mixture (1:1 Matrigel/medium ratio) for orthotopically injecting into the esophagus of nude mice . The nude mice were anesthetized with isoflurane, and an incision was made through the left upper abdominal pararectal line and peritoneum. The stomach and esophagus were carefully exposed. With fixing the stomach by forceps, the cells were slowly injected into the junction of the esophagus and stomach with an insulin needle. The abdominal wall and skin were closed with 5-0 Ethicon sutures (ET-661G). The entire process was performed on a clean bench, and the wound was disinfected with iodophor after the operation.For the organoid mouse model, the tumor-forming background we selected was the mutation of 2O, pH 8.3). Then the PDVF membranes were blocked with 2% milk  with TBST buffer  for 1h before incubation with primary antibodies for 2 h at room temperature or 4 °C overnight. After washing with TBST, membranes were incubated with secondary antibody for 1 h and then washed three times with TBST. Cells need to be treated with 50 ng/mL IFN-γ 48 h in advance for phosphorylated protein detection. The antibodies, including anti-Jak2 , anti-pJak2 , anti-STAT3 , anti-pSTAT3 , anti-TPM4(Abcam Cat# 181085), anti-SOX2 , anti-GAPDH  were used in this study.Cells were lysed in lysis buffer . Cell lysate in SDS loading buffer (50 mM Tris-HCl (pH 6.8), 10% glycerol, 2% SDS, 1% 2-mercaptoethanol, 0.1% bromophenol blue) was boiled and analyzed by SDS-PAGE (Bio-Rad) and transferred to 0.45 μm polyvinylidene fluoride (PVDF) membranes with transfer buffer , pSTAT3 (Abcam Cat# ab267373), Sox2 and Ki67  overnight at 4 °C after process of antigen retrieval and block with 2% goat serum. After being washed with PBST, the sections were incubated with secondary antibodies at room temperature for 1 hour. Then DAB was applied for signal detection , followed by staining with hematoxylin for seconds and rinsed with water for 1 min. Then the sections were blocked with the Permount mounting medium. For immunofluorescence staining, deparaffinization and rehydration procedures were as described above, antibodies of P63 , CK13 , CK14  and CK5  were used. After secondary antibodies were added, the slides were mounted by mounting solution with DAPI . And the images were taken with fluorescence microscopy .For organoid growth assay, 2000 single cells were cultured in a 96-well plate embedded in 10 μl Matrigel . And the number of organoids was counted after 5 days of culture.For drug response, 1000 single cells were cultured in a 96-well plate embedded in 10 μl Matrigel and then subjected to different concentrations of AZ960  or DMSO. After treatment for 48 h, the number of organoids was counted in each well. The number of organoids in the experimental group normalized by the DMSO group was the cell viability.t-test (two-tailed) was used to evaluate the gene expression, tumor volume, organoid number. Expression differences among three groups were analyzed by ANOVA. The statistical analysis was performed using GraphPad Prism (RRID: SCR_002798) version 9. Data are represented as mean ± SEM. All results are considered statistically significant at P < 0.05.The unpaired Student’s TRIzol reagent  was used to extract the total RNA in each sample. The library was constructed by Illumina Stranded mRNA Sample Preparation Kit  followed by the manufacturer’s protocol. And the Illumina NovaSeq 6000 was used to sequence with paired-end 150 bp.p-value < 0.05. The GO and KEGG (RRID: SCR_012773) enrichment was performed by clusterprofliler. The normalized data, generated by DESeq, was processed by log-transformation for subsequent visualization and quantification of specific signatures.The RNA-seq data were aligned by STAR (v2.6) (RRID: SCR_004463) with genome reference mm10 for mice and hg19 for humans. The quantification of transcripts in each sample was implemented by R packages, GenomicAlignments. DESeq2 (RRID: SCR_000154) was used to identify the differentiated expressed genes with |log2-FC| > 1 and The library was prepared by using the 10× genomics platform, Chromium Single Cell 3′ Reagent Kits v3, following the manufactory’s protocol. The average 7000 total cells (from 7000 to 9000) of each sample were added to the individual channel with an average recovery of 5525 cells. The Gel Beads were used to partition cells in the Chromium instrument. After library generating, Illumina NovaSeq 6000 was used to sequence with paired-end 150 bp for each sample.The cellranger (v2) was used to align the clean reads with genome reference hg19. The Seurat (v3) pipeline was used to quantify and visualize single-cell RNA data. The poor-quality cells whose detectable gene number is lower than 200 or higher than 7,500, and whose mitochondrial genes expression proportion is higher than 20%, would be removed. The poor-quality genes that detected lower than 3 cells also would be removed.The vst model was used to identify 4000 high variable genes for subsequent processing. To reduce the systematic and random batch effects between samples, the embedding of harmony was used to cluster the population and reduce dimension. And the 30 statistically significant PCA components were used for harmony embedding calculation. The t-SNE and UMAP maps were generated from the Seurat formula, RunTSNE, and RunUMAP, respectively. The phase maps were generated from phateR. Each subpopulation was identified by the classical signatures, and top markers could be available. The inferCNV was used to identify the malignant squamous epitheliums cells compared with immune cells and other epitheliums in an individual sample.The monocle (v3) was used to inference the normal squamous epithelium development lineage. The principal graph of phate and UMAP map was learned by reversed graph embedding firstly. Then, the formula order_cells, assigned the pseudo-time value in each cell based on their projection. Besides, slingshot also was used to confirm the predicted tumor lineage with 300 approximate points.54 The percentages of cell cycle status had been summarized and visualized by ggplot2.All the enrichment gene-sets were downloaded from GSEA (v7 database). Gene-sets variation analysis (GSVA) in omics data was performed on the pseudo-bulk transformation data, which normalized single-cell data into 100 bins to accelerate the speed and save the memory of calculation. The proliferation scores of the epithelium subpopulation were calculated and quantified based on the GSVA scores from proliferation-related pathways. Besides, the cell cycle status in the normal single-cell esophageal squamous epitheliums cells was calculated and quantified by CellCycleScoring with g2m genes and s specific genes.55 and GSE160269.30 And the CCI signatures were identified by CCI SE exclusively upregulated genes with fold-change > 0.2, base-mean > 0.8, and pct.1 > 0.8 compared with normal SE.The signatures of subpopulations were detected by FindAllMarkers with 0.25 thresholds and 0.25 minimum fractions. And the BS, BK, and DK signatures were identified with pct.2 < 0.4. The progression signatures of ESCC were identified from commonly upregulated in stage II/III patients compared with stage I patients in TCGA-ESCATo quantify the expression levels of specific pathways or gene-sets on transcriptome data, the normalized matrix was used to calculate, as following formula processingMi means gene value in sample/single-cell i, N means the number of pathways/gene-sets, and SSi means signature score in sample/single-cell i.After BS, BK, and DK signatures were quantified in each sample/single cell, ggtern was used to quantify the distribution of them on BS, BK, and DK dimensions and project the distribution of dots/samples on the ternary maps. The two-dimension kernel density of distribution on the ternary map was estimated by stat_density_tern with geom = ‘polygon’ and 500 bins. To better compare the distribution on the ternary map in different conditions, the same measurement scale of three axes should be reserved in each sample/single cell. Hence, the limits were set to the same number in each sample/single cell. And all visualization was complemented by package ggtern and ggplot2.θ1–θ3 to descript the variation among three axes with sample S , respectively. Each angle exists between the one axis and the segments included the origin point and sample S . Based on the regular cosine law, we could generate the cosine value of each angle, as the following formula:To make the qualitative ternary map quantitative, the CCI scores were created to define the distribution of each sample/single cell among BS, BK, and DK dimensions. Firstly, we defined three angles a, b, c represented the BS, BK, and DK signatures value of sample S. And the θ1, θ2, θ3 represented the angles generated from sample S.The f, CCI scores would be generated in each sample/single cell.Next, we calculate the inverse value of standard deviation among three cosine values, and after multiplying by a constant To better identify the CCI signature genes, we designed a workflow for analyze. Firstly, we analyzed the distribution of the CCI scores of ESCC cells. The area under the curve (AUC) of CCI scores distribution had been calculated. Of note, the AUC curve was significantly fitted to the GLM binomial distribution. And the first intersection point between the AUC curve and predicted curve would be the cut-point of CCI scores for ESCC cells on the single-cell ternary diagram for calculating the CCI signature genes. The ESCC cells with their CCI scores higher than the cut point were used for identifying the CCI signature genes. Then we calculated the CCI signature genes by comparing these CCI positive cells with other squamous epithelium cells with the standards of fold-change > 0.25, base-mean > 0.8, and pct.1 > 0.8. And 195 genes were identified as the CCI signature genes for ESCC (Supplementary Table 56 17 paired normal and ESCC samples; GSE23400_1,57 53 paired normal and ESCC samples; GSE23400_2,58 51 paired normal and ESCC samples; GSE70409,59 17 paired normal and ESCC samples; GSE53624,36 119 paired normal and ESCC samples. The probe id in GSE53624 had been converted to gene name by GPL18109 platform, and others had done by feature data of getGEO results.The array data of transcriptome were downloaded by GEOquery, including GSE20347,37 had been used in this study. In TCGA-ESCA cohorts, the 81 ESCC patients were extracted to analyze. Besides, the transcriptome data, survival information, and clinical classification of ESCC patients, from TCGA-ESCC and GSE53624 cohorts, were used to validate the clinical correlation between CCI and ESCC diagnosis. The Cox regression and the Kaplan-Meier survival curves were calculated and visualized by R package, survminer and survival. The determination of the cut-points for numerical variables in survival plots was based on the maxstat package, which also was integrated into the survminer package.The TPM data of bulk RNA-seq and the data of proteomics from GSE149609The single-cell data of normal esophageal tissue was downloaded from HCA projects and the human cell landscape. And squamous epitheliums were extracted following the origin annotation. After the same scRNA data pipeline analysis, the HCA and our normal sample were combined to reduce the dimension, and the UMAP map of normal esophageal squamous epitheliums was generated, and the expression levels of classical genes and gene-sets were projected and summarized.The single-cell data of ESCC were downloaded from GSE160269. The squamous epitheliums were identified following the origin annotation. The clinical information of ESCC patients was used to divide patients into two groups including stage I and stage II/III.Supplementary TableSupplementary Figures


BRCA wild type and HRP populations. We further analyzed molecular signals related to sensitivity of Vigil treatment. Tissue from patients enrolled in the randomized double-blind trial of Vigil vs. placebo as maintenance in frontline management of advanced resectable ovarian cancer underwent DNA polymorphism analysis. Data was generated from a 981 gene panel to determine the tumor mutation burden and classify variants using Ingenuity Variant Analysis software (Qiagen) or NIH ClinVar. Only variants classified as pathogenic or likely pathogenic were included. STRING application (version 1.5.1) was used to create a protein-protein interaction network. Topological distance and probability of co-mutation were used to calculated the C-score and cumulative C-score (cumC-score). Kaplan–Meier analysis was used to determine the relationship between gene pairs with a high cumC-score and clinical parameters. Improved relapse free survival in Vigil treated patients was found for the TP53m-BRCAwt-HRP group compared to placebo (21.1 months versus 5.6 months p = 0.0013). Analysis of tumor mutation burden did not reveal statistical benefit in patients receiving Vigil versus placebo. Results suggest a subset of ovarian cancer patients with enhanced susceptibility to Vigil immunotherapy. The hypothesis-generating data presented invites a validation study of Vigil in target identified populations, and supports clinical consideration of STRING-generated network application to biomarker characterization with other cancer patients targeted with Vigil.Thus far immunotherapy has had limited impact on ovarian cancer. Vigil  has shown clinical benefit to prolong relapse-free survival (RFS) and overall survival (OS) in the  BRCA wild type (BRCAwt) but more than one-fifth of cases are attributable to mutations in tumor suppressor genes, with 65–85% of the mutations being in germline BRCA genes (gBRCA)   –39. Paths was subtracted from one so that intuitively a small weight corresponds to strong evidence of a biological interaction between a gene pair and multiplied by 10 to shift the values to the desired scale [d by summing over the weighted edges that connect them and systematically finding the shortest weighted path. Genes with distance ≤3.8 was defined as the bottom quarter. Intuitively, when a gene pair has a low topological distance, d, the genes may interact biologically.The score, ed scale . DijkstrP) of a co-mutation was defined as the total number of the 91 patients who have a mutation in both of the genes in the given gene pair divided by a measure of the total number of times both genes are mutated individually, given by:The independent concepts of patient mutation profiles and the STRING Network are integrated by C-score. The probability of co-mutation for every pair of genes in the overall gene set was calculated. The probability  and m(j) are the cumulative mutations of genes i and j, respectively. The range of P is between 0 and 1 where P = 0 indicates that genes i and j never co-mutate and P = 1 means the genes will always co-mutate.. Where \C, to quantify the likelihood that the genes interact functionally, termed “putative genetic interactions” [The probability of co-mutation and the topological distance between genes in the STRING network were then combined to calculate a C-score, denoted actions” . The C-si is denoted, Cs. The p-value was then calculated by taking the total number of times the simulated C-score was greater than or equal to the actual C-score and dividing by the number of trials performed  [Further, the cumulative C-score for a gene ≠jC . A gene  10,000) .\documenA list of pathways associated with each gene was extracted and binned into seven color coded categories that included DNA repair, chromosomal organization and transcription, regulation of translational and post-translational modification, immunity, other pathways, other cancer genes, and undefined.versus low to examine and compare RFS and OS differences using the ‘survival’ and ‘survminer’ packages in R (Version 3.6.2).RFS and OS relationships of patients with varying mutational statuses in (i) hub genes, (ii) gene pairs with small topological distances and (iii) gene pairs with high cumulative C-scores were explored. Finally, patients were stratified by tumor mutational burden high p = 0.052) and OS (HR = 0.514 90% CI 0.252–1.051 p = 0.06) was not significant, but a trend to the benefit of Vigil was suggested. However, this could have been related to reduced placebo response in patients with high TMB. Low TMB did not impact RFS or OS in patients receiving Vigil compared to placebo (HR = 1.011 90% CI 0.405–2.524 and HR = 1.153 90% CI 0.244–5.461 respectively). High TMB also did not disrupt significance of BRCAwt correlation with RFS previously reported [BRCAwt TMB high patients median RFS was 13.7 months in Vigil treated patients compared to 8 months for placebo (HR = 0.427 90% CI 0.232–0.784). Similarly, overall survival in BRCAwt TMB high patients was not reached in Vigil patients compared to 41.4 months in placebo . Within Vigil treated patients there was no impact of TMB on RFS (HR = 1.289 90% CI 0.639–2.602) or OS (HR = 1.266 90% CI 0.448–3.578). Comparison of high TMB effect involved in Vigil compared to placebo related to RFS (HR = 0.598 90% CI 0.354–1.011 reported . In BRCAn = 77) and edges (n = 371) represent biological interaction or association between any two of the identified pathogenic genes. Pathway analysis was conducted by inputting each gene in the STRING network into the WikiPathways Application in Cytoscape [To identify potential gene interactions that were associated with extended RFS in patients receiving Vigil, first a protein–protein interaction (PPI) network was constructed using 83 genes that were identified as having pathogenic mutations in the study population . The 83 ytoscape .Fig. 1STTP53, CTNNB1, PIK3CA, BRCA1, NF1, BRCA2, ARID1A, ATRX, MYCNOS, and MUTYH. TP53 had the largest degree of all genes in the network as seen in Fig. A hub gene is defined to be a gene with a high degree (or number of connections) of associations to other genes in the network. Here, a hub gene is considered to be a gene with degree ≥12, which is the top quartile of genes based on the range of degree of genes in the network. Ten of the 23 genes in the distance matrix , median RFS was 18.69 months with Vigil (n = 33) and 8.35 months with placebo (n = 32) . In the BRCA1wt population (n = 79), RFS was 12.75 months for Vigil (n = 42) and 8.38 months for placebo (n = 37) . RFS for the BRCA2wt population (n = 82) was 11.47 months and 8.35 months for Vigil (n = 46) and placebo (n = 36), respectively . Additional hub gene analysis is presented in Supplemental Table For the single gene analysis, patients were grouped by their mutational status and the Kaplan–Meier and log-rank tests were performed to determine whether the mutational status at that loci was associated with RFS in Vigil treated patients relative to placebo. RFS from randomization was the primary endpoint of the VITAL trial and therefore it was used as the test variable  for this analysis. The distance matrix only included 23 genes, representing a subset of the network that was complete . Once the complete network was found, the distance matrix was constructed to visualize the topological distance of each corresponding gene pair.USH2A and EPPK1, with a distance of 15.19 and between genes BRCA1 and BRCA2 with a distance of 0.02, respectively. We further analyzed gene pairs with a small topological distance. Using a cutoff of 3.8 which was calculated by taking the range, 15.19 minus 0.02, and dividing by four, denoted the bottom quartile, we arrived at 139 gene pairs comprised of 23 genes , the genes with highest cumC-score in order were BRCA1, BRCA2 and TP53. This indicated high connectivity within the network both from a co-mutation standpoint and a topological distance perspective. C-score significance analysis was performed, however due to the limitations of small sample size and small gene sets the results were not significant. Future analysis with larger sample size and gene panels is warranted.After computing the C-Score for all gene pairs in the Distance Matrix and ordering genes from highest to lowest cumulative C-Score . The impact of these combinations on relapse-free survival in the Vigil treatment group compared to placebo is displayed in Table TP53m-BRCAwt group experienced a median RFS of 19.35 months, compared to 11.71 months in co-mutant and 10.48 months in co-wild type. When compared between treatment arms, the TP53m-BRCAwt group had a median RFS of 19.35 months in the Vigil arm compared to 7.85 months in placebo  for the combination of BRCA and our identified hub genes, displayed in Table The function or dysfunction of TP53m-BRCAwt population. A score of <42, as defined by Myriad Genetics was used to identify patients who were HRP and a score of ≥42 indicated patients were HRD. RFS in the TP53m-BRCAwt and HRP group was improved to 21.1 vs. 5.6 months  in Vigil vs. placebo patients  in BRCA1/2wt, HRP cancer patients as potential biomarkers for Vigil treatment, and possibly indicators of novel added therapeutic management, may be fruitful. In our approach, the STRING database was utilized to construct an unbiased network to describe the functional similarity between genes, thereby providing mechanistic understanding of the potential effect of wild type or mutant variants. This approach circumvents a potential limitation of DNA variant data and may provide more effective target population identification, given our current limited understanding of comprehensive molecular signal expression pathways and relationship to clinical benefit impact. In this manner, one can describe the genes of high importance by computationally analyzing properties of the malignant network, such as the topological distance between genes, C-scores, and hub genes. Through these analyses in the HRP ovarian population treated with Vigil, three gene variants stood out across all analytic methods: BRCA1, BRCA2, and TP53. Similar methodology has been used by others involving various cancer types and have demonstrated molecular collaboration with clinical outcomes [Our network-based analysis of pathogenic gene mutations points us towards a potential optimally responsive population to Vigil, specifically, patients with a HRP malignant cell profile including outcomes , 43, 45.cumC-score is the aggregate of one gene’s interaction with every other gene in the network. Gene pairs identified by C-scores often involve central cancer genes which correlate with increased tumorigenesis and sensitivity/resistance to anticancer therapeutics [cumC-score [TP53, BRCA1, and BRCA2 as genes with the highest cumC-scores of the genes present in patient samples. The high cumC-score suggests that particular variants of these genes correspond to drug response, as cumC-scores correlate with sensitivity and resistance [TP53m and BRCAwt correlated with increased RFS benefit to Vigil, although further prospective analysis, now underway, will be required for verification. Additional topological features of the STRING network which may be of interest and worthy of further investigation include betweenness centrality (BC), eccentricity, closeness centrality, and eigenvector centrality. A node with a high BC may represent a bottleneck in the network [The combination of STRING-generated topological distance and sample-derived probability of co-mutation is manifested as the C-score for two genes, and the apeutics . Due to mC-score . Here, wsistance . Indeed, network . This maBRCA and TP53 as central ovarian cancer genes. Previous work demonstrated that hub gene data provided clinical insight to differences in OS. In one previous study, 4 of 16 identified hub genes in the studied sample  were associated with decreased OS of patients with ovarian cancer [TP53m, BRCA1wt, BRCA2wt as core hub genes with RFS advantage in Vigil treated patients, potentially indicating a broader genetic network for target population of Vigil. Moreover, this approach supports a strategic shift in targeted therapeutic development towards targeting related network genomic variants.Analysis of hub genes similarly identified n cancer . Authorsn cancer , 49. TheBRCA must be intact for Vigil to function optimally, while the pathways impacted by TP53 may be dysregulated. Similarly, the integrity of the homologous repair pathway and its associated genes (HRP genotype) may also be important for optimal Vigil results. This suggests a cancer homeostasis formed by the combination of gene variants that creates an optimal environment for drug sensitivity or resistance. We hypothesize that the interaction of pathways generated by functional HR or BRCA proteins and disrupted TP53 protein creates the ideal molecular setting for Vigil therapy responsiveness. Mutation in TP53 is likely an early oncogenic event and likely results in clonal cell TP53 neoantigen expression. Coupled with proficient homologous recombination of BRCA1/2 wild-type gene patients may achieve low TMB, but also low intratumor heterogeneity (ITH). While low TMB potentially results in decreased CD4+/CD8+ T cells infiltration, this signaling pattern and its associated low ITH may provide for more effective and consolidated T cell response towards clonal neoantigens. Furthermore, treatment with Vigil may bypass the effect of low TMB by increasing circulating CD4+/CD8+ T cells and providing T cell neoantigen education, which would likely be particularly effective in this population [TP53m is associated with increased TMB and tumor aneuploidy level (TAL), which have conflicting impacts on immune responsivity. TMB tends to correlate with sensitivity to certain immunotherapies particularly checkpoint inhibitors [TP53’s impact on immunogenicity is also tissue dependent, which may be determined by differential gene expression within tissue types [TP53 genotype’s association with improved response to Vigil immunotherapy in the ovarian cancer BRCAwt population. We did not observe an independent beneficial effect of high TMB with Vigil although evidence of detrimental effect of high TMB was suggested in placebo patients, and reported by others [BRCAwt patients was not adversely effected by TMB [Our results also support that the pathways impacted by pulation . Converspulation , 28. As hibitors , 51. TALhibitors , 53. TP5ue types . This phy others . Althougd by TMB .In conclusion, despite sample size limitation, we demonstrate proof of support for use of DNA analytical methods to separate resistant and sensitive populations to Vigil. These techniques create a robust approach to analyze how the nodal network relationship between genes affects clinical response to Vigil when used as maintenance therapy in advanced stage III/IV resectable disease patients. These results are hypothesis generating and warrant further investigation. Moreover, these results further support novel use of network-based analysis to identify other more sensitive gene targets and potentially additional novel targeted therapeutic combinations with Vigil and possibly other immunotherapeutics.Supplemental Material


In July and August 2021, the SARS-CoV-2 Delta variant dominated in Europe.Using a multicentre test-negative study, we measured COVID-19 vaccine effectiveness (VE) against symptomatic infection.Individuals with COVID-19 or acute respiratory symptoms at primary care/community level in 10 European countries were tested for SARS-CoV-2. We measured complete primary course overall VE by vaccine brand and by time since vaccination.Overall VE was 74% (95% CI: 69–79), 76% (95% CI: 71–80), 63% (95% CI: 48–75) and 63% (95% CI: 16–83) among those aged 30–44, 45–59, 60–74 and ≥ 75 years, respectively. VE among those aged 30–59 years was 78% (95% CI: 75–81), 66% (95% CI: 58–73), 91% (95% CI: 87–94) and 52% (95% CI: 40–61), for Comirnaty, Vaxzevria, Spikevax and COVID-19 Vaccine Janssen, respectively. VE among people 60 years and older was 67% (95% CI: 52–77), 65% (95% CI: 48–76) and 83% (95% CI: 64–92) for Comirnaty, Vaxzevria and Spikevax, respectively. Comirnaty VE among those aged 30–59 years was 87% (95% CI: 83–89) at 14–29 days and 65% (95% CI: 56–71%) at ≥ 90 days between vaccination and onset of symptoms.VE against symptomatic infection with the SARS-CoV-2 Delta variant varied among brands, ranging from 52% to 91%. While some waning of the vaccine effect may be present , protection was 65% at 90 days or more between vaccination and onset. Coronavirus disease COVID-19) has caused considerable morbidity and mortality with more than 66 million cases and 1.2 million deaths reported in the World Health Organization European Region as at 5 September 2021 9 has cau. From miRecent discussions around recommendations for booster doses, for various populations, highlight the need to measure vaccine effectiveness (VE) by time since vaccination. Many studies have reported a decrease in VE against infection with increasing time since vaccination -14, and We conducted a study in symptomatic individuals swabbed for a COVID-19 test at primary and care/community level in July and August 2021, assuming that any identified COVID-19 cases were infected with the Delta variant given its dominance in Europe during this period. We estimated VE against SARS-CoV-2 symptomatic infection, by vaccine brand, age group and time since vaccination to understand if there may be waning of vaccine-induced protection over time.Ten of 14 primary care/community study sites participated in this analysis: Croatia (HR), France (FR), Ireland (IE), the Netherlands (community testing: NL-CO), Portugal (PT), Romania (RO), three regions in Spain (ES), the Navarre region in Spain (NA), as well as England (EN) and Scotland (SC) in the United Kingdom (UK) .Supplementary Table S1 for characteristics of the study sites).We used the test-negative design to estimate VE against symptomatic infection . Cases wSupplementary Table S1).Demographical, clinical  and information on COVID-19 vaccination  was collected, via questionnaire, electronic medical records and vaccine registry linkage  tested individuals exclusively by RT-PCR, five study sites  included individuals tested by RT-PCR or rapid antigen test. Four study sites  had sequencing results of the whole or partial genome of viruses confirmed by RT-PCR. The sites HR, IE, RO attempted to sequence all viruses, and NA sequenced a proportion of viruses, selected independently of vaccination status. Phylogenetic analysis was performed to identify the Pango lineage based on the classification v.3.1.16 2021.The study period included swab dates in July and August 2021, as this is when the Delta variant was dominant in participating study sites ,20.We included individuals aged 30 years and older who belonged to their country’s age-specific target group for vaccination at time of swabbing. Where known, we excluded individuals with contraindications for vaccination, those living in a residential care facility, controls who had previously tested positive for SARS-CoV-2 and controls who had tested positive for seasonal coronaviruses. We further excluded those who were swabbed more than 10 days (RT-PCR tests) or 5 days (rapid antigen tests) after symptom onset. We excluded those with a time interval between doses not recommended by the vaccine manufacturer  and those with onset within 1–13 days of the first (if COVID-19 Vaccine Janssen) or second dose (for two-dose vaccines) of COVID-19 vaccine.We defined persons as completely vaccinated 14 days after receiving either the second of two recommended doses of a two-dose vaccine or a single dose of COVID-19 Vaccine Janssen. Persons were considered unvaccinated if they did not receive any COVID-19 vaccine or were vaccinated on or after the day of onset of symptoms. Persons partially vaccinated or those with an additional dose were excluded from the analysis.For the brand-specific analysis, we restricted the data to the period when the brand was available in the country by age group. For two-dose vaccines, we only included people vaccinated with the same brand for both doses.We compared the odds of vaccination between cases and controls. We used logistic regression and calculated VE as 1 minus the OR expressed as a percentage. We included study site as a fixed effect and date of swab  in both crude and adjusted VE analyses, as time is an integral part of the test-negative design and study site is an integral part of a multicentre study.We further adjusted for age group, sex and presence of at least one of the six commonly collected COVID-19-relevant chronic conditions . In SC, the question “Did you receive a letter asking you to shield?” was used as a proxy for “presence of chronic condition”, assuming those not answering the question were not shielding.In NA where symptom onset date was not available and in two study sites with ≥ 20% of missing information (IE and PT), we imputed it as 3 days before the swab date, 3 days being the median between onset and swab date at the sites where information was available.For the age-specific analyses for all vaccines, we stratified the data into the following age groups: 30–44, 45–59, 60–74 and ≥ 75 years. For the analyses among those with a chronic condition, vaccine brand and by time since vaccination, we stratified the data into the age groups 30–59 and ≥ 60 years.We modelled VE by time since vaccination using an interaction between vaccination and time since vaccination as a restricted cubic spline with four knots at 0 and 20 days and at the 45th and 90th centile, based on a priori knots and adaptations of Harrell’s percentiles . We alsoWe conducted sensitivity analyses excluding the study site without systematic selection of individuals for swabbing (NL-CO), excluding the largest study site (NA), varying the imputed onset dates between 2 and 5 days, including only those swabbed within 7 days of symptom onset (RT-PCR tests), including only those tested by RT-PCR, and varying the number and position of knots in the time since vaccination analysis.Supplementary Figure S1 details the flow of exclusions). Among the cases and controls, 11,312 were completely vaccinated. Vaccination roll-out continued as cases and controls were selected . Among cases, 84%  and among controls, 89%  presented with symptoms < 120 days after complete vaccination. The median days between last vaccination and symptom onset among those younger than 60 years was 50 days (interquartile range (IQR): 36–72) for cases and 42 days (IQR: 27–62) for controls. The median days between last vaccination and symptom onset among people 60 years and older was 82 days (IQR: 57–107) for cases and 78 days (IQR: 52–103) for controls.Cases and controls received their second dose (for two-dose schedules) or first dose (for COVID-19 Vaccine Janssen) of COVID-19 vaccination up to 213 days before symptom onset , and 31% (vs 23% of cases) presented with at least one chronic condition  .Among controls, 7,893 of 11,557 (68%) and 936 of 2,725 (34%) were tested by rapid antigen test. In NL-CO 45 of 859 (5%), in ES 80 of 204 (39%), in FR 61 of 165 (37%), in NA 8,632 of 11,797 (73%) and in PT 11 of 83 (13%) were tested by rapid antigen test.A total of 84% of controls had completed the primary COVID-19 vaccination schedule at the time of swabbing, compared with 59% of cases. Comirnaty was the most commonly used vaccine, with 66% of vaccinated controls and 60% of vaccinated cases receiving two doses.Of the 2,190 cases presenting at the four study sites with sequencing results available, 131 (6%) were sequenced and 119 (91%) of these were infected with the SARS-CoV-2 Delta variant and its sublineages. All 20 viruses from HR, eight of nine viruses from IE, 88 of 99 viruses from NA and all three viruses from RO belonged to the Delta variant and its sublineages.The overall complete dose VE against symptomatic SARS-CoV-2 infection was 74% (95% CI: 70–77) among persons aged 30 years and older, adjusting for swab date, study site, age group, sex and chronic condition. The adjusted VE was 74% (95% CI: 69–79), 76% (95% CI: 71–80), 64% (95% CI: 48–75) and 63% (95% CI: 16–83) for those aged 30–44, 45–59, 60–74 and 75 years and older, respectively . The VE The overall VE for those aged 30–59 years with presence of chronic condition was 63% (95% CI: 50–73) and without 77% (95% CI: 73–80) . The VE for those aged 60 years and older with presence of chronic condition was 58% (95% CI: 27–76) and without 66% (95% CI: 49–78). Among those aged 30–59 years, the VE was 69% (95% CI: 56–78) and 80% (95% CI: 77–83), 50% (95% CI: 11–72) and 68% (95% CI: 60–75), 94% (95% CI: 83–98) and 90% (95% CI: 86–94), and 24% (95% CI: -25–53) and 57% (95% CI: 45–66), for those with and without presence of chronic condition for Comirnaty, Vaxzevria, Spikevax and COVID-19 Vaccine Janssen, respectively . The samComirnaty VE among those aged 30–59 by days between vaccination and onset of symptoms was 87% (95% CI: 83–89) at 14–29 days and 65% (95% CI: 56–71%) at ≥ 90 days  . Using oComirnaty VE among those aged 60 years and older by days between vaccination and onset of symptoms was 65% (95% CI: 37–80) at 30–59 days and 64% (95% CI: 44–77%) at ≥ 90 days . The samVaxzevria VE among those aged 30–59 years by days between vaccination and onset of symptoms was 72% (95% CI: 52–83) at 14–29 days and 65% (95% CI: 48–76) at 60–89 days . SpikevaExcluding the study site with self-selected participants (NL-CO), varying the imputed onset dates by 2 and 5 days, and restricting to those swabbed within 7 days of symptom onset, changed the VE point estimates by less than 3%. Excluding the study site providing the most cases and controls (NA) resulted in lower sample size and not all stratified VE could be estimated, particularly for older age groups. VE point estimates among those aged 30–59 years differed by ≤ 8% for all estimates. For those 60 years and older, we could only estimate VE for Comirnaty, which differed by 12%, although confidence intervals overlapped .Supplementary Table S2 and S3). The absolute difference between overall VE estimates was 15% in the age group 30–44 years and 3% among those aged 45–59 years. After restricting to those tested by RT-PCR, we could no longer estimate VE among those aged 60 and older stratified into two age groups; the VE differed by 8% in that group. The VE estimates by age group (30–59 and ≥ 60 years) and vaccine brand differed by ≤ 10%, except for COVID-19 Vaccine Janssen among 30–59-year-olds, where the difference was 26%, but confidence intervals overlapped. The difference in VE estimates stratified by chronic condition differed by more than 10% among those with presence of chronic condition  and among those without chronic condition for COVID-19 Vaccine Janssen (Supplementary Table S2 presents VE estimates by age group and by vaccine product restricted to those tested by RT-PCR only). The Comirnaty VE by time since vaccination had the same trends among individuals tested by RT-PCR only, although the final estimates were lower .The VE point estimates in sensitivity analyses after excluding individuals with rapid tests were consistently lower  changed the Akaike information criterion (AIC) by < 3 points among those aged 30–59 years and < 4 points among those 60 and older, and the functional forms remained the same.In this multicentre test-negative design study, including 14,282 individuals, we measured VE of Comirnaty, Vaxzevria, Spikevax and COVID-19 Vaccine Janssen against symptomatic SARS-CoV-2 infection in July and August 2021, thus restricting the analysis to a period where Delta variant circulation dominated.The VE was greater for mRNA vaccines than for the other vaccines among those aged 30–59 years, with Spikevax at 91%, Comirnaty at 78%, and Vaxzevria and COVID-19 Vaccine Janssen VE at 66% and 52%, respectively. Among those aged 60 years and older, VE was 67% (Comirnaty), 83% (Spikevax) and 65% (Vaxzevria). The sample size was too small to measure VE in this age group for COVID-19 Vaccine Janssen and confidence intervals overlapped for all other vaccine brands.Owing to the observational nature of this study, comparison between vaccine brands should be made with caution, as different population groups (with varying levels of risk and exposure) may have been offered different vaccines at different times. To account for this, we attempted a further stratification of brand- and age-specific VE by time since vaccination, to better compare brand-specific VE, but greater sample size is needed for a full comparison. Nevertheless, our results suggest a reduction greater than 50% in symptomatic SARS-CoV-2 infection by all vaccine brands across all adults 30 years and older in July and August 2021 during dominant circulation of the Delta variant. However, those vaccinated earlier may belong to specific groups with different risks of exposure and susceptibility to the virus than those vaccinated late. Measuring age group and brand-specific VE by time since vaccination by vaccine cohort , although not possible in this study, would provide additional information.The Comirnaty VE among those aged 60 years and older was lower than in our previous publication covering people aged 65 and older for the study period December 2020 to May 2021  . The difOur estimates are also similar to the VE against symptomatic infection in other observational studies during circulation of the Delta variant. In the UK, while the VE point estimate of Comirnaty was higher (88% compared with our VE of 67–78%), the VE for Vaxzevria was similar (67% vs 65–66%) . In QataWe observed a lower VE point estimate among those presenting at least one chronic condition compared with those without, both for 30–59-year-olds and those aged 60 years and older. Precision was low in the older age group, and the difference (14%) was only statistically significant among those aged 30–59 years. Brand-specific VE by chronic conditions among those aged 30–59 years suggest that for most brands, VE is higher among those without chronic conditions. Brand-specific VE by presence of chronic condition was not available for other age groups because of limited sample size. Small differences in VE against infection between those with and without underlying chronic conditions has been suggested in a Danish study , and speThere was some evidence of decline in VE with time since vaccination among those aged 30–59 for Comirnaty (ranging from 87% at 14–29 days to 65% at ≥ 90 days). Modelling suggested the VE declined to 61% after 200 days. This is very similar to the results from a recent meta-analysis on duration of effectiveness of COVID-19 vaccines, showing a decline of 25 percentage points over 6 months . Among tUsing a modelling approach to measuring VE by days since vaccination provides added value compared with the stratified approach, as we do not lose information by transforming a continuous variable into a categorical one. However, the modelling approach may not be adequate in the tails of the model, because of sparse data, as can be seen by the very wide confidence intervals around the model among those aged 60 years and older. This may explain why the stratified estimates did not match the ones in the model up to 60 days between vaccination and symptom onset.The decline of Comirnaty VE against infection for younger adults in the UK and of both mRNA vaccines for adults in Canada was smaller (both 12% compared with our 22% over a similar time since vaccination period) ,28. A laTo disentangle the decline in VE by time since vaccination from variant-related reduction in VE, effects of vaccine brands given at different times and differences in age-specific VE, it is important to examine the data and measure VE by time since vaccination by age group and vaccine brand within a period of stable variant circulation. While this is possible to a certain extent in our study, sample size becomes limited and some results may be unreliable. We will endeavour to repeat these analyses going forward with larger sample size.Study design-specific issues and the mechanism of protection of the vaccine can hide or exacerbate changes in VE by time since vaccination . A falseIn this study, five of 10 study sites used rapid antigen tests within their surveillance system and we included individuals in the analysis who had a rapid antigen test performed within 5 days of symptom onset. In a sensitivity analysis, VE estimates excluding those individuals with rapid antigen tests resulted in general in lower point estimates, although confidence intervals overlapped for all results. A particular difference in VE point estimates, however, was the overall VE among those aged 30–44 at 59% when excluding individuals with rapid antigen tests, compared with 74% when including them. These results suggest that there may be differences in testing behaviour in certain population groups that could be differential by case and vaccination status. Going forwards in the pandemic, recommendations for testing will change, overall and according to age and risk group, and it will be important to understand on an ongoing basis who is tested how and why. This is a challenge for researchers, particularly at primary care level.Our study is subject to several limitations. While many study sites sequence the whole or partial genome of viruses, the proportion sequenced overall did not allow a variant-specific VE analysis. To overcome this limitation, we restricted the study period to July and August 2021, a period in which we know the Delta variant dominated in the European sites participating in our study. Among the 130 viruses sequenced, 91% belonged to the SARS-CoV-2 Delta variant, indicating that the vast majority of viruses in the study were likely to belong to the Delta variant. The proportion of viruses sequenced was small in our study, the main reason for this was the difficulty of being able to match the clinical-epidemiological data with the virological data.The vaccine coverage was high in July and August 2021 in many European countries, particularly among older age groups. Apart from affecting our results in terms of precision, individuals who are unvaccinated may be different from vaccinated individuals in terms of exposure to the virus, thus violating a key precondition of VE studies . InclusiWe used a multicentre study design and aimed to reduce heterogeneity with study sites adapting the same generic protocol. The study site NA dominated in terms of sample size, as it provided data from all symptomatic individuals tested using their comprehensive surveillance in electronic medical records/registries. Excluding NA from the analysis reduced precision, however, the point estimates for all analyses remained < 10% different in terms of absolute VE, with the exception of Comirnaty VE among people 60 years and older, where excluding NA resulted in a point estimate greater by 12%, but owing to sample size, confidence intervals overlapped.Our study has several strengths. Selection bias can be a problem with observational studies, particularly in analyses involving electronic medical records. In our study, in all study sites but one, all or a systematic sample of individuals were selected for inclusion. The swabbing procedure was known, reducing the risk of selection bias. In addition, the test-negative design aims to minimise selection bias by adjusting for healthcare-seeking behaviour. Omitting the one study with self-selected individuals changed the VE by < 2% overall, by age group and vaccine brand.Our study provides evidence of 52–91% protection of a completed SARS-CoV-2 primary vaccination schedule against the Delta variant among those aged 30 years and older for four vaccine brands. For some vaccine brands, the results indicate a lower VE among those with a chronic condition among those aged 30–59. While our study suggests some waning of the vaccine effect with increased time since vaccination, we observed sustained protection of more than 60% up to 200 days after vaccination for Comirnaty. Further studies are needed to better analyse the observed decline in VE. We will continue the analysis of VE by days since vaccination and onset within the I-MOVE-COVID-19 and ECDC network study as more data come in. High complete vaccine coverage in the primary series of vaccination is important as it provides good direct protection of individuals against symptomatic SARS-CoV-2 infection as illustrated by this and other studies, as well as providing protection against severe disease.


Increasing evidence highlighted that chronic inflammation involved in the development of metabolic syndrome (MetS) and Type 2 diabetes mellitus (T2DM). This prospective study was aimed to assess the association between MetS and novel pro-inflammatory indicators like monocyte–to–high-density lipoprotein and monocyte–to–apolipoprotein A1 ratios (MHR and MAR) in Chinese newly diagnosed T2DM.A total of 605 Chinese newly diagnosed T2DM with complete and available data were enrolled in this study. Demographic and anthropometric information were collected. Laboratory assessments were determined by standard methods. MetS was based on the Chinese Diabetes Society definition. Multiple binomial logistic regression model was used to estimate the independent variables of MHR and MAR for MetS. Receiver operating characteristic (ROC) curve was conducted to assess the optimal cutoff value of MHR and MAR in identifying MetS.P < 0.05). The AUCs of MHR and MAR identifying MetS were 0.804 (95% CI: 0.768–0.839) and 0.840 (95% CI: 0.806–0.873) respectively (P < 0.001). The optimal cutoff values of MHR and MAR were 3.57 × 108/mmol  and 3.95 × 108/g , respectively.Overall, the prevalence of MetS was 60.2%. The correlation analysis showed that MHR and MAR were closely correlated with metabolic risk factors like body mass index, waist circumference, triglycerides, high-density lipoprotein cholesterol, systolic blood pressure, diastolic blood pressure, uric acid, and insulin resistance. MHR and MAR were also significantly associated with higher odds of MetS after adjustment for other confounders, the odds ratios (ORs) (95%CI) were 1.50 (1.14–1.97) and 2.26(1.79–2.87) respectively. Furthermore, MHR and MAR were also seemed to have higher area under the curve (AUC) for MetS than ApoA1 and monocyte alone from the ROC curve analysis (MHR and MAR were significantly associated with MetS. These two novel pro-inflammatory indicators may be useful markers for MetS in Chinese newly diagnosed T2DM. Type 2 diabetes mellitus (T2DM) is a kind of metabolic disease characterized by chronic hyperglycemia that often accompanied with other metabolic disorders like obesity, hypertension, and hyperlipidemia. Metabolic syndrome (MetS) has gradually become an increasing worldwide health problem that was associated with increased cardiovascular disease (CVD), stroke, and T2DM . MetS isCirculating monocyte is a cluster of blood cell modulated by immune factors including tumor necrosis factors alpha (TNF-α) and Toll-like receptor (TLR) 2, TLR4, and TLR8 ligands that can interact with thrombocytes and endothelial cells, resulting in exaggerated inflammation and increased oxidative stress . These bThis cross-sectional study was consecutively conducted with newly diagnosed T2DM from the Department of Endocrinology at Longyan First Affiliated Hospital of Fujian Medical University who fulfilled the study criteria between January, 2021 and December, 2021. The T2DM was defined according to the World Health Organization (WHO) 2019 criteria: (1) fasting plasma glucose ≥126 mg/dl or 2-h postprandial ≥200 mg/dl during oral glucose tolerance test (OGTT) or HbA1C ≥6.5% or participants with classic symptoms of hyperglycemia or hyperglycemic crisis with random plasma glucose ≥200 mg/dl and (2) with negative diabetic autoimmune antibodies and excluded other specific types of diabetes. Previous unknown hyperglycemia status and c were considered as newly diagnosed T2DM. Participants were excluded if they met the following criteria: (1) presence of acute diseases that can interfere glucose metabolism; (2) presence of acute or chronic infection, obvious liver or renal dysfunction, anemia, hemolytic diseases, and bleeding that can interfere circulating monocyte count; (3) treatment with medications that can interfere circulating monocyte count; (4) currently receiving lipid-lowering therapies; (5) presence of secondary hypertension or a history of tumors; and (6) unwillingness to participate in this study. In this study, we estimated the sample size according to the requirement of multiple binomial logistic regression model; 12–14 variables may be put into the logistic regression model according to the principle of 5–10 events per variable, and the prevalence of MetS is about 50%–70% in newly diagnosed T2DM. Thus, we planned a sampling size of 500–600 participants (2). WC was measured at the natural depression between the iliac crest and 10th rib, which should be the narrowest part of the abdomen. Systolic and diastolic BP (SBP and DBP) were measured by an electronic sphygmomanometer with an appropriate cuff size after the participants take a rest for more than 5 min on at least three different occasions; the mean of three measurements was calculated as final BP.Demographic information was collected by trained interviewers through a standard questionnaire and also obtained by a review of medical records and laboratory data, including gender, age, and history of diseases that can interfere circulating monocyte, current or prior use of drugs, smoking, and drinking. Participants that smoke more than four cigarettes a week for at least 6 months continually or accumulative were considered as smoking according to standardized methodological recommendations of WHO for smoking surveys . ParticiLaboratory assessments were measured by standard methods using fasting venous blood samples that were taken between 8:00 a.m. and 9:00 a.m. after fasting overnight. Blood samples were taken into standardized tubes containing dipotassium ethylenedinitrilotetraacetic acid. Serum levels of the following variables were determined: creatinine, alanine aminotransferase (ALT), uric acid (UA), fasting blood glucose (FBG), serum insulin, HbA1c, diabetic autoimmune antibodies , HDL-c, low-density lipoprotein (LDL-c), TGs, ApoA1, high-sensitivity C-reactive protein (hs-CRP), and complete blood count. Biochemical indexes were measured by an auto-biochemical analyzer (Roche Diagnostics Corporation). ApoA1 levels were measured by the polyethylene glycol–enhanced immunoturbidimetric assay . HbA1c was evaluated by high-performance liquid chromatography with a D10 set (Bio-Rad). Complete blood count was obtained using the Coulter LH 780 Analyzer . Homeostasis model assessment (HOMA-IR) was used to assess insulin sensitively. HOMA-IR was calculated with the following formula: fasting serum insulin (µU/ml) × fasting plasma glucose (mmol/l)/22.5 . MHR or Participants were diagnosed with MetS according to Chinese diabetes guideline for MetS management . ParticiP < 0.05 was considered statistically significant.Data were analyzed by using the SPSS 23.0 software (SPSS Inc. IBM). Descriptive data are expressed as means ± standard deviation (SD). Discrete variables were summarized in frequency tables . Participants were divided into three groups based on tertiles of MHR and MAR. Statistical differences among groups were performed with one-way analysis of variance (ANOVA) followed by Tukey’s test for multiple comparisons. Chi-squared (χ2) test or Fisher’s exact test was used for comparison of categorical variables. Correlation between MHR, MAR, and metabolic parameters was evaluated by Pearson’s or Spearman’s correlation analysis. Multiple binomial logistic regression model was used to estimate the independent variables of MHR and MAR for MetS after adjusting for other covariates. The receiver operating characteristic (ROC) curves were used to assess the identifying value of MHR and MAR for MetS in newly diagnosed T2DM. Optimal cutoff value was based on the greatest value of the Youden’s index. A two-tailed value of P < 0.05). The BMI, WC, TG, SBP, DBP, UA, HOMA-IR, serum insulin, monocyte count, MAR, and MHR were significantly higher, whereas HDL-c and ApoA1 were significantly lower in the MetS group than the non-MetS group (P < 0.05). Moreover, the MHR and MAR were also calculated and divided into three tertiles. Clinical and laboratory characteristics of participants based on tertiles of MHR and MAR were summarized in P < 0.05). In addition, decreasing trends were also observed in HDL-c and ApoA1 across the MHR and MAR tertiles (P < 0.05). Furthermore, participants in higher tertiles of MHR and MAR groups showed the higher prevalence of MetS and hypertension (P < 0.05).Overall, a total of 605 newly diagnosed T2DM with complete and available data were included in the final analysis. Clinical and laboratory characteristics of participants were summarized in P < 0.05). In addition, A positive correlation between MHR and MAR was also observed .The correlations between MHR, MAR, and metabolic parameters were presented in To determine independent variables of MHR and MAR for MetS, binomial logistic regression analysis was also performed (P < 0.05). MAR also showed the highest AUC in identifying MetS. The AUC of MHR and MAR in identifying MetS was 0.804  and 0.840 , respectively  and 3.95 × 108/g , respectively  in identifying MetS compared with ApoA1 and monocyte alone  or MHR and systematic inflammatory diseases. Chen et al. reported that NHR and LHR have strong predictive power for MetS in Chinese population . MHR wasTo our knowledge, this is the first study that confirmed the identifying value of MHR and MAR for MetS in Chinese newly diagnosed T2DM. The other strengths of this study adjusted several potential confounding variables in final analysis and included enough sample size that can represent the Chinese newly diagnosed T2DM population. Meanwhile, some limitations need to be mentioned. First, this study was designed as a cross-sectional study without follow-up, and it cannot directly reflect the associations MHR, MAR, and MetS. Second, the studied population is the Chinese newly diagnosed T2DM, and the optimal cutoff values of MHR and MAR may be not applicable to other races.In conclusion, two novel indicators of MetS for Chinese newly diagnosed T2DM was found in this study. The results showed that MHR and MAR were significantly associated with MetS and seemed to have higher AUC value for MetS than ApoA1 and monocyte alone. All these findings indicated that MHR and MAR could be convenient and reliable predictors to screen for MetS in Chinese newly diagnosed T2DM, whereas more longitudinal studies are needed to further confirm these associations.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the ethical committee of Longyan First Affiliated Hospital of Fujian Medical University. The patients/participants provided their written informed consent to participate in this study.WW took charge of the software and contributed to writing— original draft. WW, ZC, XG, and MT conducted the investigation. MT contributed to data curation and writing-editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.