Christensenellaceae R‐7 group, Ruminococcaceae UCG‐013, Lachnospiraceae UCG‐002, and Family XIII AD3011 group in the rumen but decreased the abundance of Prevotella 1; the above bacteria groups regulate meat quality by regulating rumen metabolites . Overall, the addition of PKM may improve the quality and flavor of the meat by affecting muscle metabolism and microorganisms in the rumen.Palm kernel meal (PKM) has been shown to be a high‐quality protein source in ruminant feeds. This study focused on the effects of feed, supplemented with different amounts of PKM , on the quality and flavor profile of Tibetan sheep meat. Furthermore, the deposition of beneficial metabolites in Tibetan sheep and the composition of rumen microorganisms on underlying regulatory mechanisms of meat quality were studied based on ultra‐performance liquid chromatography coupled with quadrupole time‐of‐flight mass spectrometry as well as 16S rDNA sequencing. The results of the study showed that Tibetan sheep in the ZL‐18 group exhibited superior eating quality and flavor profile while depositing more protein and fat relative to the other groups. The ZL‐18 group also changed significantly in terms of the concentration and metabolic pathways of meat metabolites, as revealed by metabolomics. Metabolomics and correlation analyses finally showed that PKM feed mainly affected carbohydrate metabolism in muscle, which in turn affects meat pH, tenderness, and flavor. In addition, 18% of PKM increased the abundance of  18% of PKM group exhibited superior eating quality and flavor profile while depositing more protein and fat relative to the other group. A total of 23 differential metabolites were identified in PKM‐treated Tibetan sheep meat. Carbohydrate metabolism was typical metabolic pathway altered by PKM feeding. PKM feeding affected key metabolites by altering rumen microorganisms to regulate the relevant metabolic pathways During the initial acclimatization phase, all animals were fed native pasture grass and provided with access to water on a daily basis. After the 14‐day adaptation period, the 120 animals were randomly divided into three treatment groups  and one control group (ZL‐0), as shown in Table longissimus lumborum (LL) was then collected from the 9th to 11th ribs on the back of the carcass. Meanwhile, the rumen contents were collected and filtered to obtain the filtrate. All slaughter and sampling were professionally performed according to uniform standards, with the collected samples subsequently stored at −80°C until required for analyses.The fattening period of lambs was 90 days, with their diet composition at different experimental stages  shown in Table 2.3Live weight was measured and recorded 12 h before slaughter. After slaughter, the skin, head, hoof, tail, and viscera were removed, and the body weight of the plate oil and kidney was retained as the carcass weight. After 24 h of slaughter, the cross‐sectional area of the carcass between the 12th and 13th calcaneus ribs was drawn on the hydrochloric acid paper, and the surface area was calculated by AutoCAD. The result was the eye muscle area. At the same time, the thickness of rib meat between 12th and 13th heel ribs was measured and recorded as rib meat thickness. Backfat thickness was measured by Vernier caliper between the 3rd and 4th ribs at 6–8 cm from the midline .The pH, et al. , after t, et al. , using aBefore determining the edible and nutritional quality of LL muscles, the samples were removed from the −80°C refrigerator, placed in sealed bags, and slowly thawed at 4°C. After complete thawing, the samples were split for subsequent testing. The steam loss rate and cooked meat rate were determined using LL pieces of 5 cm × 1 cm × 1 cm . To measure the shear force, the samples were first cut into 3 cm × 1 cm × 1 cm meat columns in the direction of the muscle fibers, using an iron ruler and a scalpel. A muscle tenderness meter was then used in the direction of the vertical muscle fiber. Finally, the physical properties  were determined using the TPA mode  after cutting the samples into pieces of 1 cm × 1 cm × 1 cm.To assess the nutritional composition of LL muscles, the thawed meats were churned in a meat grinder before determining their moisture, protein, and fat content using the Analytical Chemists Association's  Standards.2.5For amino acid (AA) analysis, an Agilent 1290 Infinity LC ultra‐high‐performance liquid chromatography system  was used along with a 5500 QTRAP mass spectrometer (AB SCIEX). The conditions used for the high‐performance liquid chromatography and mass spectrometry were as described by Zhang, Han, Hou, Raza, Gui, et al. . The HPLTo determine the fatty acid (FA) composition, samples were first separated by gas chromatography performed on an Agilent DB‐WAX column (30 m × 0.25 mm ID × 0.25 mm). Mass spectrometry analysis was then carried out using an Agilent 7890/5975C gas‐mass spectrometer . For both steps, the methods of Zhang, Han, Hou, Raza, Gui, et al.  and Sant2.6Two grams of LL sample were transferred to a 20‐mL headspace vial for GC‐IMS (gas chromatography‐ion mobility spectrometry) analysis using the FlavourSpec® (GAS) flavor analyzer. Two‐gram sample was thawed in a refrigerator at 4°C for 12 h and loaded into a 20 mL headspace vial, which was incubated by preheating at 80°C for 15 min and subsequently pumped into the injector with a 500 μL headspace volume at 85°C. The temperature of the chromatographic column was fixed at 60°C, and the temperature of the drift tube was kept at 45°C. The flow rate of the carrier gas nitrogen (99.999% purity) was 150 mL/min (constant flow rate) and the flow rate of the GC column was 2 mL /min for 2 min. The flow rate was then increased to 20 mL/min for 8 min and further increased to 100 mL/min for 15 min before stopping. VOCs were determined by comparing retention indices (RI) and drift times (Dt) of standards from the NIST and IMS databases. The proportion of each component in the meat samples was estimated by normalized quantitative analysis of the peak volume of each component separately.2.7After thawing LL muscle samples slowly at 4°C, 80 mg was homogenized and vortexed in 200 L of water for 60 s. Methanol acetonitrile solution  was then added in a 1:1 ratio prior to vortexing and sonication at low temperature for 30 min. The samples were then kept at −20°C for 1 h and centrifuged for 20 min at 4°C. The resulting supernatant was dried under vacuum and eventually used for ultra‐performance liquid chromatography and quadrupole time‐of‐flight mass spectrometry (UHPLC‐QTOF‐MS) analysis.In order to separate the metabolites, an Agilent 1290 Infinity LC ultra‐high‐performance liquid chromatography system (UHPLC) was used. In this case, a column temperature of 25°C and a torch flow rate of 0.3 mL/min were applied. For mobile phase A, water and 25 mM of ammonium acetate were used, while mobile phase B consisted of acetonitrile. Samples were placed in a 4°C autosampler throughout the analysis and were also analyzed in a random order in order to avoid the effects of fluctuations in the instrument's detection signal. In addition, to monitor and evaluate the stability of the system as well as the reliability of the experimental data, QC samples were inserted into the sample queue. Primary and secondary spectra of the samples were acquired using the AB Triple TOF 6600 mass spectrometer, with the ESI conditions applied as described by Zhang, Han, Hou, Raza, Wang, et al. .http://www.kegg.jp/).The raw data were converted by ProteoWizard into an mzXML format (MetDDaA and LipDDA identified metabolite structures) and then aligned. They were also corrected for retention time before extracting the peak areas by XCMS. After being normalized to total peak intensity, the processed data were imported into SIMCA‐P  where differential metabolites were identified prior to KEGG pathway analysis  was then used to purify mixed PCR products, with required fragments subsequently quantified by electrophoresis using the QuantiFluorThe raw data obtained by sequencing were cleaned by splicing and filtering the sequences before removing chimeras in order to enhance the accuracy and reliability of the reads. Sequences were then clustered into OTUs  with 97% agreement before being classified based on valid data , muscle metabolism, and rumen microbiota composition of Tibetan sheep which were given different levels of PKM diets were compared using one‐way ANOVA on IBM SPSS 23 software. Results were then expressed as means and standard error of means (SEM), and considered to be statistically significant when 33.1p < .05).Carcass traits are the most important indicators to measure the economic value of a meat sheep, and they are also the most important part of meat sheep performance measurement, including pre‐slaughter weight, carcass weight, slaughter rate, rib meat thickness, backfat thickness, and eye muscle area. As shown in Table 3.2p < .05). However, compared with the other three groups, the pH was significantly higher after 24 h of acid discharge with ZL‐0 group (p < .05). In addition, for the a* values, those of ZL‐18 and ZL‐21 were significantly higher compared with ZL‐15 (p < .05). In terms of the water retention of meat, the cooking loss rate of ZL‐21 was significantly higher than that of the other three groups, while its cooked meat rate was significantly lower than for ZL‐15 (p < .05). Finally, Tibetan mutton displayed better meat tenderness after being fed with PKM diet, with the shear force and chewiness of ZL‐18 being the smallest (p < .05).The edible quality of meat mainly refers to its color, texture, tenderness, and water retention capacity. As shown in Table p < .05), hence indicating that the muscles of Tibetan sheep could effectively deposit protein and fat after being given protein feed.Fat, protein, and water content are usually measured to determine the nutritional quality of meat. As shown in Table 3.3The effects of PKM feed on amino acid composition and content are shown in Table Table p < .05), while those of lauric acid, myristic acid, and palmitic acid were also significantly higher compared with the other three groups (p < .05). Lauric acid accounted for a large proportion of SFA in Tibetan mutton. However, the amount of neuronic acid also increased significantly after feeding PKM (p < .05).In terms of fatty acids, saturated fatty acids and monounsaturated fatty acids contribute mainly to meat flavor, whereas polyunsaturated ones contribute to meat nutrition. There are different types of fatty acids in Tibetan mutton, and in this study, 35  were detected in the samples. As shown in Table 3.4p < .05). Furthermore, ZL‐15 and ZL‐18 also showed significantly higher levels of hepanal D, hepanal M, heptanal D, and heptanal M (p < .05). The addition of 18% PKM to the feed significantly reduced the proportion of isopropanol D and 3‐hydroxy‐2‐butanone M in the muscle, while significantly increasing the proportion of 1‐butanol D, 2‐ethoxyethanol, 2‐propanone, and 2‐pentanone (p < .05). The proportion of 3‐hydroxy‐2‐butanone D and methyl acetate decreased after the addition of 15% PKM to the feed (p < .05). It should further be noted that there was an overall decrease in the proportion of esters in the muscles of the three groups whose feeds were supplemented with palm meal (p < .05).Flavor can determine the quality of sheep meat. To examine the effects of different levels of PKM on the production of VOCs in Tibetan sheep muscle, GC‐IMS was used. A total of 32 volatile substances, including 12 aldehydes, 8 alcohols, 7 ketones, 4 esters, and 2‐ethylfuran, were detected in the four groups of Tibetan lamb. Figure Based on these results, it seemed that PKM supplementation influenced the nutritional composition, volatile components, and muscle quality of Tibetan sheep.3.52Y, Q2) obtained by a sevenfold cross‐validation. It was noted that the OPLS‐DA model, for which stability was good, could distinguish two groups of samples. Indeed, 70 differential metabolites were identified between the two based on OPLS‐DA, with VIP > 1 and p value = .05 selected as thresholds (37 in positive ion mode and 23 in negative ion mode). The ZL‐18 group was also found to have nine up‐regulated metabolites and 14 down‐regulated ones compared with the ZL‐0 group in the negative ion detection mode  to the feed showed optimum effects. Thus, to better understand the changes in muscle metabolites after the addition of palm meal, two groups, namely ZL‐0 and ZL‐18, were selected to compare their metabolite profiles using metabolomic analysis. Figure de Table .p < .05), with 10 being up‐regulated  and 8 being down‐regulated (<0.5 DA score) in the ZL‐18 group  ‐l‐leucine levels were lower  Table . These r3.6As previously mentioned, there were significant differences in metabolite levels between the two groups of samples, and it is not unlikely that changes in rumen microbes impacted muscle metabolism. For this purpose, 16S rDNA sequencing was used to characterize the rumen microbiota of both ZL‐0 and ZL‐18 groups in order to determine the relationship between microbiota and various metabolites. A total of 4016 OTUs were identified, with 1507 and 1108 being specific to ZL‐0 and ZL‐18, respectively Figure . Based oChristensenellaceae R‐7 group, [Ruminococcus] gauvreauii group, Family XIII AD3011 group, Ruminococcaceae UCG‐013, and Lachnospiraceae UCG‐002 was significantly higher than that in the zL‐0 group (p < .05), indicating that these bacteria were found in greater numbers in Tibetan sheep's rumens after PKM was added to their feed. However, the relative abundance of Prevotella 1 in ZL‐0 group was higher (p < .05).Following taxonomic classification, Firmicutes and Bacteroidetes were found to dominate the two groups. Indeed, they are important phyla of Tibetan sheep's rumen Figure . The abu3.745 min were positively correlated with Dl‐a‐hydroxybutyric acid, sarcosine, cis‐aconitate, DL‐proline, isocitric acid, L‐ascorbic acid, and pantothenic acid, while the four fatty acids  content in LL, the protein levels, and the a* were positively correlated with succinate, N‐acetylputrescine, uridine, dopamine, uracil, erucic acid, and DL‐glutamic acid.We ventured to speculate that perhaps by changing the feed composition we could change the rumen colony composition and thus the muscle metabolism, thus controlling the quality of Tibetan sheep meat in a targeted manner. In this context, the relationship among four different species, different metabolites, meat quality, and volatile flavor substances in Tibetan sheep samples was explored. As seen on the thermogram Figure , muscle In the correlation analysis of muscle metabolites with volatile flavor substances Figure , isopropPrevotella 1 was positively correlated with Dl‐a‐hydroxybutyric acid, sarcosine, cis‐aconitate, DL‐proline, isocitric acid, L‐ascorbic acid, and pantothenic acid, while DL‐ glutamic acid, succinate, N‐acetylputrescine, uridine, dopamine, uracil, and erucic acid were negatively correlated. Similarly, Christensenellaceae R‐7 group, [Ruminococcus] gauvreauii group, Family XIII AD3011 group, Lachnospiraceae UCG‐002, and Ruminococcaceae UCG‐013 were correlated with DL‐glutamic acid, succinic acid, N‐acetylputrescine, uridine, dopamine, uracil, and erucic acid, but was negatively correlated with Dl‐a‐hydroxybutyric acid, sarcosine, cis‐aconitate, DL‐proline, isocitric acid, L‐ascorbic acid, and pantothenic acid. In addition, the correlation between muscle phenotypic characteristics and rumen microorganisms is shown above the heatmap . In this work, the Tibetan sheep meat of ZL‐18 group had better eating quality, while the 18% PKM addition promoted the protein and fat deposition in the muscle. On the other hand, PKM feeds had little effect on AA levels in LL, but for FA, PKM feeds caused an increase in the levels of saturated fatty acids in LL. Furthermore, adding PKM to the feed resulted in an increase in muscle aldehydes and a decrease in muscle esters. In muscle metabolism, mainly based on carbohydrate metabolism, LL pH, tenderness, and flavor were altered by up‐regulating the PTS, down‐regulating the TCA cycle pathway, and having both up‐ and down‐regulating amino acid pathways. The rumen bacteria, including key genera such as Conceptualization, Ying Ma, Lijuan Han, Xue Zhang, Shengzhen Hou, Linsheng Gui, Shengnan Sun, Zhenzhen Yuan, Zhiyou Wang, and Baochun Yang; Writing—original draft, Ying Ma and Sayed Haidar Abbas Raza; Writing—review & editing; Lijuan Han, Data curation, Ying Ma, Lijuan Han, and Zhenzhen Yuan; Formal analysis, Xue Zhang, Linsheng Gui, and Baochun Yang; Investigation, Zhiyou Wang, Shengnan Sun, and Shengzhen Hou; Methodology, Ying Ma, Lijuan Han, and Baochun Yang; Project administration, Lijuan Han and Shengzhen Hou; Resources and Software, Baochun Yang and Linsheng Gui; Supervision, Lijuan Han and Shengzhen Hou; Validation, Zhiyou Wang and Shengnan Sun; Formal analysis, Investigation, Methodology Validation, Writing—review & editing, Mohamed M. Hassan, Ruqaih S Alghsham, Waleed Al Abdulmonem, and Samia S. Alkhalil. All authors have read and agreed to the published version of the manuscript.The current work was funded by the Construction of Standardized Production System for improving quality and efficiency of Tibetan Sheep industry (2022‐NK‐169).There is no conflict of interest in the present study. All authors consent to participate in and publish this article. The authors acknowledge Xingang Lv  for the technical support. The authors are thankful for the support of Qinghai University for the use of laboratory facilities. 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.This study was carried out in strict accordance with the animal protection and use guidelines established by the Ministry of Science and Technology of the People's Republic of China. All animal care and handling were approved by the Institutional Animal Care and Use Committee Guidelines of Qinghai University (QUA‐2020‐0709). Moreover, all applicable rules and regulations of the organization and government were followed regarding the ethical use of experimental animals.Figure S1.Figure S2.Table S1.Table S2.Table S3.Click here for additional data file.


Colorectal cancer is one of the leading causes of cancer deaths worldwide. Currently, chemotherapy is the primary way for colorectal cancer, but with severe side effects. Therefore, it is urgent to find safer and more effective adjuvant treatment methods. At present, natural active substances are promising alternatives, as numerous studies have demonstrated possible synergistic anticancer effects in plant‐active polyphenols. In the present study, the combined effect of procyanidins (PC) (from peanut skin) and resveratrol (RES) (from peanut buds) on the synergistic anticancer potential was investigated. CACO‐2 and HCT‐8 cells were served as colorectal cancer models, and HEPG‐2 and HUH‐7 cells were served as liver cancer models to observe the effects of PC and RES alone or in combination on the growth and proliferation of these four types of cancer cells. The results revealed that both PC and RES could inhibit the cells’ proliferation in a manner with concentration‐dependent, but they exerted synergistic anticancer effects only on CACO‐2 cells. PC and RES could synergistically inhibit CACO‐2 cell clone formation, inducing apoptosis of CACO‐2 cells and blocking their cell cycle in G0/G1 phase. Additionally, as observed by the results of Western blot assay, the combined effect of PC and RES also inhibited the phosphorylation of Thr308, Ser473, and ERK and promoted the phosphorylation of IKBα and NF‐κB in CACO‐2 cells. These findings collectively indicate that PC combined with RES might exert synergistic anticancer effects by regulating AKT, ERK, and NF‐κB signaling pathways. In the present study, the synergistic anticancer potential of procyanidins (PC) and resveratrol (RES) extracted from peanut skin and peanut buds, respectively, was investigated, when applied in combination. CACO‐2 and HCT‐8 cells as colorectal cancer models and HEPG‐2 and HUH‐7 cells as liver cancer models were used to observe the effects of PC and RES alone or in combination on the growth and proliferation of these four types of cancer cells. PC extraction from peanut skin was conducted according to the method of our laboratory. Peanut skin was ground, and oil was removed by petroleum ether, and then was dried. 70% ethanol was added, and peanut skin was extracted jointly by microwave and ultrasonic. The crude extract was purified by AB‐8 macroporous. Thereafter, the filtrate was filtered through a 0.22 μm filter membrane. The content of PC was 95% by high‐performance liquid chromatography (HPLC). At the same time, high‐performance liquid chromatography–tandem mass spectrometry (UPLC‐QTOF) analysis was carried out. The main components were Procyanidin A dimer, Procyanidin A trimer, Procyanidin A tetramer, Catechins, and Protocatechualdehyde, and a little of B‐type procyanidins. Then, experiments were conducted.RES extraction from peanut sprouts was conducted based on the method of Wang , when it reached 90%.Human colorectal cancer cell lines (CACO‐2 and HCT‐8) and liver cancer cell lines (HEPG‐2 and HUH‐7) were purchased from ATCC. The cells were cultured in Dulbecco Minimal Essential Medium [DMEM, Invitrogen Trading Co. Ltd.) containing 10% fetal bovine serum (Sijiqing) and 1% penicillin–streptomycin in a humidified incubator  with 5% CO2.4The effects of PC and RES on cancer cell proliferations were performed by CCK8 . Cancer cells in their logarithmic growth phase were seeded in a 96‐well plate at a density of 9000 cells/well for 24 h. Then, the cell medium with fresh medium containing PC and RES was replaced with different concentrations for 24 h. And then, cell proliferation activity was measured at different time periods according to the instructions of the CCK‐8 kit.2.5The CACO‐2 cells were seeded into 6‐well plates (500 cells/well) for 24 h and then replaced the supernatant with fresh medium containing different concentrations of PC and RES. After 24 h of incubation, the cells were treated with fresh medium for 14 days until the formation of visible colonies. And then, colonies were washed with PBS and fixed with 4% paraformaldehyde for 30 min, stained with crystal violet for 15 min. After staining, the cells were washed with distilled water twice and colony numbers were counted.2.65 cells/well) were placed in 6‐well plates and starved with serum‐free DMEM medium for 24 h before treating the cells with PC and RES. After 12 h of sample treatment, the cells were washed twice with PBS, centrifuged at 1500 rpm for 5 min, and collected at a concentration of 1 × 106/mL. After centrifugation of the prepared single cell suspension, the supernatant was removed and 500 μL of 70% precooled ethanol was added to the cells for overnight fixation. And then, 100 μL of RNase A solution was added to the cell precipitate and resuspended the cells in a water bath at 37°C for 30 min. Finally, 400 μL of PI staining solution was added and mixed well, incubating for 30 min at 4°C in the dark. Cell cycle distributions were detected by flow cytometer (Beckman Coulter), and the results were analyzed by FlowJo 10.8.1.The CACO‐2 cells (5 × 102.7Annexin V‐FITC Apoptosis Detection Kit (Beyotime Biotechnology) was used to detect cell apoptosis. Briefly, the CACO‐2 cells were seeded in a 6‐well plate for 24 h and then treated with PC and RES at different concentrations for another 24 h. 50,000–100,000 resuspended cells were taken and centrifuged at 1000 g for 5 min. The supernatant was discarded, and 195 μL Annexin V‐FITC conjugate was added to gently resuspend the cells. Thereafter, it was stained with 5 μL Annexin V‐FITC and 10 μL propidium iodide (PI) for 20 min at room temperature in the dark. The percentage of apoptotic cells was analyzed using a flow cytometer.2.8Total proteins from CACO‐2 cells were treated with PC and RES, and their combinations were extracted using RIPA lysis buffer containing a protease and phosphatase inhibitor cocktail. Protein concentrations were quantified by BCA Protein Assay Kit (solarbio) and separated by SDS‐PAGE (10%) for electrophoresis and then transferred to a polyvinylidene difluoride (PVDF) membrane. Next, membranes were saturated with 5% non‐fat milk for 2 h at room temperature and then incubated with primary antibodies overnight at 4°C. Goat anti‐mouse and anti‐rabbit IgG conjugated with horseradish peroxidase (HRP) were used as secondary antibodies to incubate with the membranes for 1 h at room temperature. Immunoreactive bands were visualized using Chemiluminescence kit and determined by an infrared imaging system . Quantitative analysis was conducted by Image J 1.53.2.9p < .05. Different letters represent statistically significant differences (p < .05).All data are given as mean results of at least three independent measurements and shown as means ± SD. The data were quantified for graphing using GraphPad Prism software v8.0.2. Where it was necessary to compare three groups of data, a one‐way ANOVA test was performed using IBM SPSS Statistics software v26. To determine significant differences between treatments, Duncan's test was employed with a significance level set at 33.1The CCK8 assay was used to verify whether PC and RES could synergistically inhibit the growth and proliferation of human colorectal and liver cancer cells. CACO‐2, HCT‐8, HEPG‐2, and HUH‐7 cells were treated with different concentrations of PC, RES, or their combinations for 24 h in this study. Four types of cell proliferations could be inhibited by PC or RES in a concentration‐dependent manner, and only the CACO‐2 colorectal cancer cell proliferation was synergistically inhibited by PC and RES Figure . The IC53.2Cell clone formation assay is the most effective way to evaluate cell proliferation ability, invasiveness, sensitivity to killing factors, and other items. Therefore, the cell clone formation assays were used to verify the combined effect of PC and RES on the late growth and proliferation capacity of CACO‐2 cells. As shown in Figure 3.3Cell cycle dysregulation is a fundamental marker of cancer spread. To detect the effect of the samples on the cell cycle, using a flow cytometry to examine the cell cycle distribution. As the Figure 3.4Flow cytometry is used to detect if the samples can induce apoptosis of the CACO‐2 cells. The results showed that the apoptosis rate of CACO‐2 cells was significantly higher after treatment with PC and RES alone or in combination, which suggested that apoptosis was the main pathway for sample‐mediated cell growth decline Figure . After t3.5Numerous studies had reported that aberrant activation of various signaling pathways could regulate the proliferation and survival of various cancer cells, such as AKT, ERK, and NF‐κB signaling pathways. To investigate the mechanism of synergistic anticancer effects of PC and RES, the changes of these three signaling pathways in CACO‐2 cells were examined after intervention of PC and RES by Western blot assay.3.5.1AKT signaling pathway is associated with proliferation, differentiation, and apoptosis in cancer cells. In human solid tumors and hematologic malignancies, this is usually accompanied by excessive activation of the AKT signaling pathway  is activated in a variety of human tumors and is a central link in the classical PI3K‐AKT–mTOR signaling pathway. AKT plays a critical importance in regulating tumor cell growth and proliferation, promoting cell invasion and metastasis, promoting neovascularization, and in the development of resistance to chemotherapy and radiation therapy in tumor cells. Therefore, inhibiting tumor cell proliferation by the suppression of Akt activity is an effective approach to alleviate or treat cancer. Study found that procyanidins from grape seeds inactivated the PI3‐kinase/PKB pathway, inducing apoptosis in colon cancer cell lines  and liver cancer cells  in a concentration‐dependent manner. PC and RES showed a positive synergistic trend in inhibiting the growth of CACO‐2 colorectal cancer cells, which was also further verified in our cell clone formation assay, cell cycle assay, and apoptosis assay. Additionally, Western blot results showed that PC combined with RES may exert its synergistic anticancer effects by inhibiting AKT and ERK signaling pathways and promoting NF‐κB signaling pathway. Among these four cells, PC and RES produced synergistic anticancer effects only on CACO‐2 colorectal cancer cells, which provide new ideas for precise preventive interventions of cancer and new insights for effective treatment of colorectal cancer.Na Wang: Conceptualization ; investigation ; methodology ; project administration ; resources ; supervision ; writing – original draft ; writing – review and editing . Enguang Gao: Conceptualization ; data curation ; methodology ; project administration ; software ; writing – original draft ; writing – review and editing . Chenxu Cui: Methodology . Fan Wang: Writing – review and editing . Hongtao Ren: Writing – review and editing . Chao Xu: Data curation . Cancan Ning: Writing – review and editing . Yuru Zheng: Data curation . Qingqing Liu: Formal analysis . Qiuying Yu: Formal analysis ; methodology ; writing – original draft ; writing – review and editing . Gaiping Zhang: Conceptualization ; investigation ; methodology ; resources .The research work was not funded by any project.The authors declare no conflicts of interest.All authors agreed for the publication of this manuscript.Appendix S1.Click here for additional data file.


Cervical adenocarcinomas (ADCs), including human papillomavirus (HPV)‐associated (HPVA) and non‐HPVA (NHPVA), though exhibiting a more malignant phenotype and poorer prognosis, are treated identically to squamous cell carcinoma (SCC). This clinical dilemma requires a deeper investigation into their differences. Herein a transcriptomic atlas of SCC, HPVA, and NHPVA‐ADC using single‐cell RNA (scRNA) and T‐cell receptor sequencing (TCR‐seq) is presented. Regarding structural cells, the malignancy origin of epithelial cells, angiogenic tip cells and two subtypes of fibroblasts is revealed. The promalignant properties of the structural cells using organoids are further confirmed. Regarding immune cells, myeloid cells with multiple functions other than antigen presentation and exhausted T lymphocytes contribute to immunosuppression. From the perspective of HPV infection, not only is HPV‐dependent and independent cervical cancer oncogenesis proposed but also three immune reaction patterns mediated by T cells  are identified. Strikingly, diagnostic biomarkers to distinguish ADC from SCC are discovered and prognostic biomarkers with marker genes for malignant epithelial cells, tip cells, and SPP1/C1QC macrophages are generated. Importantly, the efficacy of anti‐CD96 and anti‐TIGIT, not inferior to anti‐PD1, in animal experiments is confirmed and targeted therapies specifically for HPV‐positive SCC, HPVA and NHPVA‐ADC, providing essential clues for further clinical trials, are proposed. A comprehensive single‐cell atlas of cervical cancer (CC), including HPV‐positive squamous cell carcinoma (SCC), HPV associated (HPVA) and non‐HPV associated (NHPVA) adenocarcinoma, is depicted. Through verification from organoid culture, mice experiments and clinical samples, the complex CC microenvironment is unveiled, novel clinical biomarkers are generated, and promising precise treatment modalities are proposed, providing essential clues for further CC clinical trials. Compared with SCC, ADC manifests a worse prognosis owing to its lack of symptoms on clinical examination and greater propensity for metastasis, recurrence, and resistance to chemotherapy. However, patients suffering from ADC and SCC receive the same conventional treatment strategies. Therefore, it is of vital importance to elucidate the differences between SCC and ADC, especially the unique molecular mechanism of ADC considering its more malignant phenotype and poorer prognosis, which will hopefully provide new insights for precision treatment.Cervical cancer (CC) remains one of the leading causes of death among women worldwide.1 The  Since the adoption of this novel pathology scheme in the National Comprehensive Cancer Network guidelines in 2020, interest in ADC research has dramatically increased. In fact, high‐risk HPV gene integration into the host gene was found to be responsible for the oncogenesis of HPVA. However, the pathological mechanisms underlying NHPVA remain unknown. Although previous studies have illustrated gene mutations and pathway activation profiles among ADC cases, the differences between HPVA and NHPVA have not been well established. Additionally, studies applying genomic and transcriptome sequencing to ADC are limited to the analysis of bulk cells, reflecting the average profiles of tumor samples with low resolution. Therefore, detailed insight into the cellular composition, function, and microenvironment within the tumor is urgently needed to decipher the differences between HPVA and NHPVA ADCs.In 2018, ADC was categorized into human papillomavirus (HPV)‐associated (HPVA) and non‐HPV‐associated (NHPVA) based on both morphology and etiology using the International Endocervical Adenocarcinoma Criteria and Classification, which is more reproducible and clinically informative than the 2014 WHO classification based on only descriptive morphological features.7, 8 and has redefined our understanding of tumor oncogenesis, recurrence, metastasis and therapy resistance. In the field of CC, studies utilizing scRNA‐seq dealt with the heterogeneity of cervical cancer in different clinical stages with a small sample size, limiting the breadth and depth of the research. The complexity of the tumor microenvironment (TME), differences between different histologic subtypes and the association between HPV infection and tumorigenesis have not been systemically studied.Recently, single‐cell RNA sequencing (scRNA‐seq), emerging as a cutting‐edge technology, has yielded unprecedented insights into intra‐tumoral and inter‐tumoral heterogeneities,13 and Studies on the TIME of CC and clinical trials of immune checkpoint blockades (ICBs), including anti‐CTLA4 and anti‐PD1 agents, have been extensively conducted and have dramatically altered the landscape of cancer treatment. However, in CC, the response rate to immunotherapies is far from satisfactory. Therefore, it is paramount to unravel the mechanisms and pathways underlying this low efficacy and discover better strategies to direct the immune system to fight CC. Fortunately, T‐cell receptor sequencing (TCR‐seq) allows the recognition of viral antigens and tumor‐specific neoantigens and illustrates clonal expansion patterns as well as differentiation lineages of T cells. Hence, combining scRNA‐seq with TCR‐seq may help to uncover the unique TIME of CC.The tumor immune microenvironment (TIME) is not only a cause of tumors whose dysregulation facilitates immune escape and tumor initiation but also a unique consequence, displaying a complicated and immunosuppressive phenotype as the tumor advances.17 StuIn the current study, we performed scRNA‐seq and TCR‐seq with SCC, HPVA and NHPVA ADC samples, depicting a comprehensive single‐cell atlas of CC. Remarkably, by combining high‐throughput sequencing analyses with verification from 3D organoid culture, mouse experiments, clinical samples and The Cancer Genome Atlas (TCGA) public data, we not only unveil the complex TME of CC from the perspective of structural cells, immune cells and cellular crosstalk, highlight the mechanisms of HPV remodeling in the CC TME but also generate novel diagnostic and prognostic biomarkers for CC as well as promising precise treatment modalities specifically for HPV‐positive SCC, HPVA ADC and NHPVA ADC. Most importantly, we propose that anti‐CD96 and anti‐TIGIT would not be inferior to anti‐PD1, offering some basic evidence for further clinical investigation.22.1Figure , B cells , plasma cells (IGHG1 and TNFRSF17), myeloid cells including monocytes, macrophages and dendritic cells (CD14 and C1QA), neutrophils/mast cells (CSF3R/CPA3), fibroblasts (DCN and COL1A1), smooth muscle cells (TAGLN and ACTA2), endothelial cells (PECAM1 and VWF) and epithelial cells . The proportion of each cell type varied greatly in different cervical cancer tissues ; 2) gastrointestinal glandular epithelial cells ; 3) mucinous glandular epithelial cells ; 4) usual‐type glandular epithelial cells ; and 5) NMECs (SOX17). To elucidate the relationship between HPV infection and cell malignancy, we compared the distribution of HPV‐infected epithelial cells  to the gastric‐type. These findings suggested a non‐HPV‐related pathogenic mode of CC malignancy. Together, these results demonstrated that the pathogenic mechanisms of HPV‐ and non‐HPV‐related CC remain fundamentally different.To clarify the origin of epithelial cells, we categorized the aforementioned 16 subclusters into the following five types based on gene expression24 . Among MGECs, subclusters 15 and 8 expressed high levels of stem cell markers, such as ALDH3A1 and ALDH1A1, highlighting their developmental diversity. Moreover, MGECs and GGECs were inferred to be the predecessors of UGECs; the malignancy of mucinous‐ and gastric‐type ADCs may be due to their high developmental diversity and poor differentiation compared with usual‐type ADCs. In summary, we discovered that cells with stemness and poor differentiation might be the origin of CC malignancy.Quantitative set analysis of gene expression (QuSAGE) was conducted to determine the biological functions of the aforementioned five types of epithelial cells Figure . Among SS100A8 and KRT14 could serve as markers of SCC, while LGALS4 and CLDN3 were specifically expressed in ADC , EGR4 (related to mitogenesis and differentiation), and PPARG , respectively. Additionally, SOX17 (a marker for reserve cells) was found to regulate NMECs. Collectively, the DEGs and TFs of SCC and ADC hold promising potential to serve as diagnostic biomarkers.To further explore the molecular heterogeneity of SCC and ADC, we analyzed the DEGs and transcription factors (TFs) of the two histologic subtypes. Genes, such as C Figure . In addiC Figure . Notably2.2.2 ; 2) tip cells (CXCR4); 3) artery cells (FBLN5); and 4) lymphatic cells (PDPN). Remarkably, tip cells, a group of specialized endothelial cells with protruding filopodia, which guide vessel‐sprouting, attracted our attention. High expression of genes such as ESM1, COL4A1, ADAMTSL2, ANGPTL2, and APLN was observed in tip cells  veins Figure . Additios Figure . Thus, t2.2.3 we reclustered the CAFs and further categorized them into two main types  and myoblastic CAFs . QuSAGE functional analysis . Moreover, expression of these genes was more remarkable in 3D invasive CC organoids than in immortalized tumor cell lines cultured in traditional 2D conditions without endothelial cells or CAFs , which serve as bridges between the innate and adaptive immune system, were subsequently investigated. ; 2) C1QC+ macrophages ; and 3) SPP1+ macrophages (SPP1 and MARCO). Although macrophages have traditionally been classified as proinflammatory (M1‐like) or anti‐inflammatory (M2‐like) macrophages, this classic dichotomy was not applicable in our study, failing to discriminate the cells into distinct states ; 2) cDCs (CD1C); 3) LAMP3+ DCs (LAMP3); 4) CLEC9A+ DCs (CLEC9A); and 5) pDCs (LILRA4). Functional analysis indicated that CLEC9A+ DCs and cDCs participated in antigen processing and presentation, whereas LAMP3+ DCs were associated with apoptosis  CD14s Figure . Next, ws Figure : CD14+CD2.3.2 ; 2) type I helper‐like T cells (Th1‐like cells) ; and 3) regulatory T cells (Tregs) . Notably, high expression of the chemokine receptor CCR8, which mediates recruitment, along with a regulatory marker (IL2RA) and co‐stimulatory molecules  were observed in CD4+ Tregs ; 2) central memory T (Tcm) cells ; 3) exhausted T (Tex) cells ; 4) intraepithelial lymphocytes (IELs) ; and 5) recently activated effector memory or effector T (Temra/eff) cells . Next, we evaluated the functional state of CD8+ T cells , which exhibited a positive functional state. Additionally, the proportion of cells with activation checkpoint expression was generally low, whereas inhibition checkpoint expression was clearly observed in CD8+ Tex cells , which lay at the end of the differentiation trajectory of CD8+ T cells according to pseudo‐time analysis  mucos Figure . NKG7 ans Figure . Moreoves Figure . Based o2.3.3FigureUsing scRNA‐seq and TCR‐seq, we comprehensively illustrated the clonal expansion of T cells Figure6A. We fWe also investigated clonotype expansion in various CD8+ T cells and CD4+ T cell subclusters Figure . NotablySubsequently, we identified three immune reaction patterns on the basis of TCR clonotypes overlapping within CD8+ and CD4+ T cell clusters in CC samples with different HPV infection statuses, namely coordinated immune reaction (CIR), inactive immune reaction (IIR) and imbalanced immune reaction (IBIR) between CD4+ and CD8+ T cells Figure . Specifi2.4FigureHaving dissected the diverse cell components in the TME of CC, we further investigated the cell–cell interaction network and illustrated the CC TME using schematic diagrams  dataset  staining in SCC and ADC tissues. As expected, KRT14 had a wider expression in SCCs while CLDN3 had a stronger expression in ADCs , superior to the M1/M2 signature which was unhelpful for evaluating prognosis (p = 0.17).Next, since unique cell clusters such as malignant epithelial cells and tip cells play crucial roles in CC TME, we wondered whether the marker gene expression of these cell clusters could offer prognostic value. Therefore, we established novel gene signatures using the TCGA CESC dataset. Notably, the malignant epithelial gene signature Figure  embodyinMYC, DDIT3 and ERG4 in regulating SSECs, UGECs and GGECs, respectively; 2) regarding tip cell‐targeted therapies, we proposed blocking VEGFC, ANGPT2, APLN and NOTCH4, which are commonly expressed in tip cells in CC. Of note, MMP2 inhibitors should be further investigated in NHPVA ADC, since MMP2 was found to be highly expressed by tip cells in such aggressive histologic subtypes; 3) regarding fibroblast‐targeted therapies, we proposed using anti‐PDGF in both SCC and ADC, and anti‐HGF specifically in NHPVA ADC; 4) regarding CD4+ Treg‐targeted treatment, we proposed anti‐CCR8/CD177 treatment to inhibit the infiltration of CD4+ Tregs, given that CCR8 and CD177 mediate the migration and activation of CD4+ Tregs. Moreover, we proposed TNFRSF4 (a co‐stimulatory molecule) stimulation in both HPV‐positive SCC and NHPVA ADC because TNFRSF4 stimulation may inhibit the suppressive effect of CD4+ Tregs; and 5) regarding CD8+ T cell‐targeted therapies, we assumed that PD1/PDL1 agents should be more actively used in ADC. Besides, targeting CD96 for SCC and TIGIT for ADC should also be further investigated, since we have preliminarily proved that these two immune checkpoints were differentially expressed in different histologic subtypes of CC  rega4+ Tregs;35 and3FigureIn the field of CC, previous studies mainly focused on the heterogeneity of cervical cancer in different clinical stages, with limited sample size. Here, for the first time, we generated a single‐cell transcriptional atlas of eight CC samples with different histologic subtypes and HPV infection statuses. Through the combination of scRNA‐seq and 3D organoid culture, we demonstrated the significance of structural cells in the CC TME, among which epithelial cells mediated oncogenesis, tip cells dominated sprouting angiogenesis and CAFs assisted tumor progression. From the perspective of HPV infection, we proposed innovative mechanisms of HPV remodeling CC TME. Notably, we identified three immune reaction patterns in CC patients with different HPV infection statuses by integrating scRNA‐seq and TCR‐seq. Based on bioinformatics analysis, we established novel diagnostic and prognostic biomarkers for CC by verifying the TCGA CESC dataset and clinical samples from our center. Lastly, we pioneer by proposing a precise treatment landscape uniquely for HPV‐positive SCC, HPVA ADC and NHPVA ADC, which will be of tremendous clinical value Figure9.SOX17 expression were shared, which was similar to the finding of a previous research on tumor heterogeneity. Our findings also highlight the importance of a deeper investigation of NMECs in suppressing CC oncogenesis possibly mediated by SOX17 through the WNT/β‐catenin pathway, which might be valuable for treatment development; 2) since angiogenesis is one of the hallmarks of cancer, we studied the lymphovascular system in CC and discovered tip cells. Remarkably, a high abundance of tip cells indicated shorter survival, in accordance with previous research on lung tumor tip cells. More significantly, this previous study showed that tip cells were the most sensitive to anti‐VEGF blockade among the various endothelial cell clusters. Therefore, we suggest that tip cells could serve not only as prognostic biomarkers but also as promising anti‐angiogenic targets for CC. In addition to anti‐VEGF treatment, other anti‐angiogenic targets should also be studied, such as anti‐ANGPT, anti‐APLN, andti‐NOTCH4 and anti‐MMP2; and 3) inspired by the research of Chen et al. indicating the crucial function of CAFs, we identified two types of CAFs: iCAFs mediating inflammation‐to‐cancer transformation through the interaction of cytokines and chemokines, and myCAFs engaged in ECM remodeling. These two types of CAFs produce different patterns of growth factors to stimulate tumor proliferation. Moreover, myCAFs in subcluster 9 were involved in antigen processing and presentation pathways, which is partly consistent with the findings of Elyada et al.’s study, which first reported antigen‐presenting CAFs. These findings indicate that CAFs play multiple roles in TME and may serve as therapeutic targets to slow CC progression. Collectively, we not only illustrated the critical roles of structural cells in the CC TME with differences between SCC and ADC but also identified key genes and pathways in structural cells, generating novel prognostic biomarkers reflecting cancer hallmarks and promising therapeutic targets for CC.Epithelial cells, endothelial cells and fibroblasts are essential structural components of the TME that serve as barriers, form the lining of vessels and make up the stroma. In this study, we thoroughly investigated these three cell types: 1) in terms of epithelial cells, we illustrated the potential malignant origin of epithelial cells featuring stemness and poor differentiation, as well as the potential mechanism of HPVA and NHPVA CC oncogenesis. Notably, we found that malignant epithelial cells were derived from different individuals, whereas non‐malignant epithelial cells featuring ogeneity.36 OurConsidering the necessity of structural cells in the TME and that 3D organoids can exhibit more heterogeneity than traditional cell lines, we generated a CC organoid consisting of tumor epithelial cells, endothelial cells and fibroblasts, and preliminarily verified that such an organoid could manifest the key molecular and functional properties of structural cells discovered from scRNA‐seq. Therefore, we propose a novel research model that integrates high‐throughput sequencing with organoid culture technology to build more biomimetic in vitro models and validate the in‐silico analysis results. In terms of DCs, we identified that CLEC9A+ DCs exerted positive antigen presentation, while LAMP3C+ DCs were related to apoptosis, which implied a mature and exhaustion‐like functional state concordant with previous researches. Notably, we discovered CD8+ T ex cells expressing immune checkpoints, including not only PDCD1 but also TIGIT and CD96, which have already aroused great interests as novel immune checkpoint targets. Furthermore, we conducted TCR‐seq, which has been widely used in various immune‐related diseases, including cancer, but is seldom used for CC. For the first time, we found that HPV16 stimulated much more clonal expansion than other high‐risk HPV subtypes, such as HPV18 and HPV45, while the neoantigens in NHPVA ADC might also contribute to T cell proliferation and interaction; yet, there was an imbalanced immune response between CD8+ and CD4+ T cells. These findings provide new insights into the development of CC immunotherapy, including anti‐tumor vaccines and chimeric antigen receptor (CAR) T cell therapy.In addition to structural cells, immune cells were also a focus of our research. Regarding macrophages, we discovered that the SPP1/C1QC dichotomy of macrophages was more reasonable than the classic M1/M2 classification in CC, further supporting previous studies.41 In KRT14 and CLDN3 could assist in the accurate identification of SCC and ADC. Regarding prognostic biomarkers, we generated several gene signatures that embodied cancer hallmarks in various dimensions, including the malignant epithelial cell gene signature, tip cell gene signature, combined gene signature and SPP1/C1QC macrophage gene signature. To achieve precise treatment for CC, we suggest a unique therapy for HPV‐positive SCC, HPVA ADC and NHPVA ADC, targeting both structural cells and immune cells. For instance, considering the aggressiveness of NHPVA ADC, EGR4, MMP2, and HGF inhibitors should be further investigated. More importantly, regarding immune checkpoints, novel ICB treatments, such as anti‐CD96 and anti‐TIGIT, hold great potential for clinical use, which was proven not to be inferior to anti‐PD1 by in vivo experiments, thus confirming our hypothesis. Additionally, we assumed that anti‐CD96 could be considered in SCC treatment, whereas anti‐TIGIT and anti‐PD1 could be applied in ADC treatment according to the heterogenous expression of the aforementioned immune checkpoints in SCC and ADC tissues.Regarding diagnostic biomarkers, we identified DEGs between SCC and ADC through scRNA‐seq and preliminarily validated that Although the sample size of our current study was insufficient and further validation of the novel biomarkers and histologic‐specific therapeutic targets in larger sample sizes is required, our findings will facilitate more accurate diagnosis and precise treatment for CC patients with different histologic subtypes and HPV infection statuses.CLDN3 for ADC and KRT14 for SCC) with validation of clinical tissues and innovatively established prognostic biomarkers based on several unique cell clusters  embodying cancer hallmarks using TCGA data. Lastly, we not only pioneer by proposing a precise treatment landscape uniquely for HPV‐positive SCC, HPVA ADC and NHPVA ADC but also demonstrated the efficacy and potential of anti‐CD96 and anti‐TIGIT as novel immunotherapy strategies for CC, providing a reliable basis for preclinical studies and hopefully paving the way for the precise treatment of CC.In conclusion, we depicted, for the first time, a comprehensive CC single‐cell transcriptomic atlas, covering structural cells, immune cells and cellular interactions within both SCC and ADC. Notably, we investigated both HPV‐dependent CC carcinogenesis (attributing to dysregulated cell cycle) and HPV‐independent CC tumorigenesis . Additionally, based on different HPV infection statuses and overlapping TCR clonotype expansion, we proposed three immune reaction patterns, namely, coordinated immune reaction (CIR), inactive immune reaction (IIR) and imbalanced immune reaction (IBIR), consistent with the clinical phenotype of different CC histologic subtypes. More significantly, we discovered novel diagnostics biomarkers , and informed consent was obtained from all participants for sample collection. Exclusion criteria were as follows: 1) patients with serious concomitant systemic disorders; 2) history of chemotherapy or radiotherapy before; 3) any history of malignant tumors other than cervical cancer; and 4) patients with evidence of distant metastasis. Three HPV‐positive SCC, three HPVA ADC and two NHPVA ADC samples for scRNA‐seq and TCR‐seq were included, and the HPV infection status of the samples was confirmed by clinical HPV testing and HPV sequence blasting analysis. All eight patients were primarily treated at FIGO stage I‐II preoperatively. Detailed clinical characteristics of the eight CC samples are shown in Table −1 of penicillin, 100 mg mL−1 of streptomycin, and 250 ng of amphotericin B (NCM Biotech). HUVECs were cultured in an endothelial cell growth medium  supplemented with 5% FBS (Gibco). Primary CAFs were isolated as described previously and cultured in the Dulbecco Modified Eagle Medium/F12 (Gibco) supplemented with 10% FBS. All cells were cultured at 37 °C in an atmosphere of 5% carbon dioxide. CS Well 600 chambers , which contained 261 wells per chamber, were used to fabricate the 3D organoids. A total of 1000 SiHa/HeLa cells, HUVECs and CAFs per well were mixed in a proportion of approximately 9:3:1, seeded into the chambers and cultured for 48 h to generate organoids.SiHa, HeLa, and HUVEC cell lines were obtained from American Type Culture Collection. SiHa and HeLa cells were grown in minimum essential medium and RPMI‐1640 supplemented with 10% fetal bovine serum (FBS) , 100 U mLreviously46 and3 pieces and enzymatically digested using a tumor dissociation kit (Miltenyi Biotec) for 30 min at 37 °C with agitation. After digestion, the dissociated cells were passed through a 70‐µm cell strainer and centrifuged at 300 × g for 5 min. After supernatant removal, the pelleted cells were suspended in red blood cell lysis buffer (Miltenyi Biotec) to lyse the red blood cells. The cell pellets were subsequently washed, resuspended in PBS containing 0.04% BSA, and re‐filtered through a 35‐µm cell strainer. Dissociated single cells were stained with AM/PI for viability assessment using a Countstar Fluorescence Cell Analyzer.After surgical removal, fresh CC tissue samples were stored in MACS Tissue Storage Solution (Miltenyi Biotec) and subsequently processed according to the manufacturer's instructions in the laboratory of NovelBio Co., Ltd. The samples were first washed with phosphate‐buffered saline (PBS), cut into ≈1 mmThe scRNA‐seq libraries and V(D)J libraries were obtained using the 10× Genomics Chromium Controller Instrument and Chromium Single Cell 5′ Library & Gel Bead kit, together with the V(D)J Enrichment Kit (10× Genomics). After concentration to 1000 cells/µL, the cells were loaded into each channel to generate single‐cell gel bead‐in‐emulsions (GEMs), which resulted in the expected mRNA barcoding of 5000 single cells for each sample. The GEMs were broken and barcoded complementary (c)‐DNA was purified and amplified after the reverse transcription step. The amplified barcoded cDNA was fragmented, A‐tailed, ligated with adaptors, and amplified by polymerase chain reaction (PCR), which ultimately generated 5′ gene expression libraries. For the V(D)J library, human T cell V(D)J sequences were enriched from the amplified cDNA, followed by fragmentation, A‐tailing, adaptor ligation and index PCR amplification. The final libraries were quantified using the Qubit High Sensitivity DNA assay (Thermo Fisher Scientific), and the size distribution of the libraries was determined using a High Sensitivity DNA chip on a Bioanalyzer 2200 (Agilent). All libraries were analyzed using an Illumina sequencer (Illumina) with 150‐bp paired‐end reads.www.novelbrain.com). Fastp with default parameter filtering of the adaptor sequence was applied, and the low‐quality reads were removed. Then, feature‐barcode matrices were obtained by aligning reads to the human genome (GRCh38 Ensemble: version 100) using CellRanger v5.0.1. Next, downsampling analysis among samples sequenced according to the mapped barcoded reads per cell of each sample was applied and finally an aggregated matrix was achieved. Low‐quality cells were discarded if the number of expressed genes was <200 or if the mitochondrial UMI rate was >20%. The mitochondrial genes were removed from the expression table. Further analysis was conducted using the Seurat package . Based on the expression table according to the UMI counts of each sample and percent of mitochondria rate, raw feature counts were log‐normalized, scaled and subjected to principal component analysis (PCA), which was performed based on the scaled data of the top 2000 highly variable genes. The top 10 PCs were used for tSNE and UMAP construction, and unsupervised cell cluster results based on the top 10 PCs were obtained using a graph‐based cluster method. The marker genes using the FindAllMarkers function with the Wilcoxon rank sum test algorithm were calculated with the following criteria: 1) lnFC > 0.25; 2) p‐value <0.05; and 3) min.pct > 0.1. To identify specific cell type, clusters of the same cell type were selected for re‐tSNE/re‐UMAP analysis, graph‐based clustering, and marker analysis.Sequencing data analysis was performed by NovelBio Co., Ltd. using the NovelBrain Cloud Analysis Platform (m). Fastp47 witCells defined as endothelial cells, fibroblasts and smooth muscle cells were used as references to identify somatic CNVs with the R package inferCNV (version 0.8.2). Each cell was scored according to the extent of the CNV signal, defined as the mean of the squares of the CNV values across the genome. Putative malignant cells were defined as those with a CNV signal >0.05 and CNV correlation >0.5.Common high‐risk HPV gene sequences, including HPV 16, 18, 58, 52, 31 and 45 are shown in Table p‐value < 0.05, and 3) min.pct > 0.1.To identify DEGs among groups , the function FindMarkers with Wilcoxon rank sum test algorithm was used. Significantly DEGs were selected as those meeting the following criteria: (1) lnFC > 0.25, 2)  analysis was performed to elucidate the biological implications of DEGs and marker genes. GO annotations were downloaded from the NCBI (http://www.ncbi.nlm.nih.gov/), GO (http://www.geneontology.org/), and UniProt (http://www.UniProt.org/) databases. The Fisher exact test was used to identify the significant GO categories, and a false discovery rate (FDR) was used to correct the p‐values.GO48 anap‐value and false discovery rate (FDR).Pathway analysis was used to explore the significant pathways of the DEGs and marker genes based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The Fisher exact test was used to identify significant pathways, and the threshold of significance was defined using the https://www.gsea‐msigdb.org/gsea/msigdb/index.jsp), and Reactome (https://reactome.org) gene sets QuSAGE (2.16.1) analysis was performed.To characterize the relative activation of a given gene set, such as the KEGG, Hallmark  workflow was used,50 whi (http://cole‐trapnell‐lab.github.io/monocle‐release) with DDR‐Tree and default parameters to determine the dramatic translational relationships among cell types and clusters. Before performing Monocle analysis, marker genes of the Seurat clustering result and raw expression counts of the cells that passed filtering were selected.Single‐cell trajectory analysis was performed using Monocle251 .Cellular interaction analysis was conducted with CellPhoneDB (version 1.1.0),53 a p to obtain the gene enrichment score of each cell, including gene sets from the KEGG pathway database (https://www.kegg.jp/) and MSigDB (http://www.gsea‐msigdb.org/gsea/msigdb/index.jsp).Single‐cell gene set enrichment analysis (ssGSEA) was performed based on the domestic gene set and normalized gene expression matrix by single‐cell gene set enrichment analysis (ssGSEA) function in the gene set variation analysis (GSVA) package54 to https://xena.ucsc.edu/). Survival analysis was conducted using the powerful online tool GEPIA (http://gepia.cancer‐pku.cn/) or R software  with packages including survival and rms. The RNA‐seq data included 257 SCC and 32 ADC samples, with the expression of each gene using the log2(TPM + 1) scale.RNA‐seq data, clinicopathological information, and survival data of the CESC dataset from the TCGA database were downloaded from Xena (‐pku.cn/)55 or To determine the proportion of epithelial cells, endothelial cells and fibroblasts in the CC TME, one SCC sample and one ADC sample were fixed in 4% paraformaldehyde (Servicebio) for paraffin embedding. Thereafter, 4‐µm‐thick sections were prepared. The following antibodies and dilutions were used to detect proteins: anti‐pan‐keratin , anti‐vimentin , and anti‐CD31 . According to the percentage of positive cells, the IHC staining area score was divided as follows: 0 point (<5%), 1 point (5–25%), 2 points (26–50%), 3 points (51–75%) %), and 4 points (76–100%), whereas the IHC staining intensity was divided as follows: 0 point (negative); 1 point (weak positive); 2 points (positive); 3 points (strong positive). Immunoreactive scores were calculated by multiplying the area and intensity scores.−1, Thermo Fisher Scientific), SiHa/HeLa cells stably expressing GFP and HUVECs stably expressing mCherry were used for further organoid generation and screening. To test invasive ability, the organoids with SiHa‐GFP/HeLa‐GFP, HUVEC‐mCherry and DAPI‐stained CAFs were generated and observed under a fluorescence microscope to record their morphologic shapes.To distinguish and track the different cell types, tumor epithelial cells (SiHa and HeLa) and HUVECs were transfected with lentiviral vectors carrying a cytomegalovirus promoter driving the expression of green  and red  fluorescent proteins. After puromycin selection  are listed in Table GAPDH expression. Fold changes in the expression of each gene were calculated using the comparative Ct method with the 2−ΔΔCt relative quantification method.The primer sequences used for amplification of −1 of streptomycin sulfate, and 10% of FBS (Thermo Fisher Scientific). To establish ectopic tumors, 1 × 106 TC‐1 cells (in 200 µl of PBS) were injected subcutaneously into the right shoulders of C57BL/6 mice. When the tumor size reached ≈50 mm3, the tumor‐bearing mice were randomly divided into four groups (n = 6 for each group): the control, anti‐PD1, anti‐TIGIT, and anti‐CD96 groups (denoted as Day 0). For PD‐1, CD96 and TIGIT blockades, transplant recipients received 200 µg of anti‐PD‐1 , 200 µg of anti‐CD96  and 200 µg of anti‐TIGIT (BE0274) twice a week × 4 doses  through intraperitoneal injection. For the control group, the mice received an immunoglobulin G control (BioLegend) at a dose of 200 µg/mouse on a similar schedule. For immune response analysis, the tumors were excised on day 12. Tumor tissues were digested in the RPMI 1640 medium supplemented with 0.2% collagenase type IV, 0.01% hyaluronidase, and 0.002% DNase I for 3 h at 37 °C in 5% carbon dioxide. The TILs were separated using Ficoll (Dakewe Biotech Company), followed by flow cytometry analysis. The tumor size was monitored every 3 days and measured at the time of sacrifice.All experiments were performed under the approval of Fudan University Animal Care and Use Committee. Female C57BL/6 mice (5–7 weeks old) were purchased from the Laboratory Animal Center of the Shanghai Institutes for Biological Sciences and were housed in a pathogen‐free environment. The TC‐1 cell line was purchased from the Chinese Academy of Sciences Shanghai Cellular Library. The cells were maintained in the RPMI 1640 medium (Thermo Fisher Scientific) supplemented with 100 IU/mL penicillin G, 100 mg mL−1 brefeldin A and 2 µm of ionomycin (Absin) before staining. Then, the cells were fixed and permeabilized using a fixation/permeabilization wash buffer (BioLegend) and stained with antibodies for 30 min in the dark. For Ki67 staining, cells were incubated in 70% ethanol for 1 h at −20 °C and then stained with an anti‐Ki67 antibody . All the antibodies used in this study are listed in Table To detect intracellular proteins, cells were stimulated for 6 h with 10 µg mLt‐test, Mann–Whitney test or Kruskal–Wallis test; one‐way analysis of variance with the Tukey post hoc test was used to compare data between multiple groups. Kaplan–Meier curves between the two groups were analyzed using the log‐rank test. All statistical analyses were performed with GraphPad Prism  or R (version 3.6.2). Statistical significance was set at p < 0.05.Specific statistical tests and metrics  used for comparisons along with sample sizes were described in the results and figure legends sections. Comparisons of continuous data of different data types between two groups were performed using the unpaired two‐tailed Student The authors declare no conflict of interest.J.Q., X.Q., and Y.W. contributed equally to this work. J.Q., X.Q., and L.W. developed the concept and discussed experiments. X.Q. and Y.W. collected patient samples and data. X.Q., C.G., and J.Q. contributed to bioinformatic data analysis and interpretation. X.Q., Y.W., L.W., and W.S. designed, performed and analyzed experiments. J.Q., X.Q., L.W., and K.H. wrote and edited the manuscript. B.L. and X.Q. contributed to graph drawing. J.Q. and K.H. supervised the progress of the study.Supporting InformationClick here for additional data file.


Early childhood exposure to heavy metals like arsenic (As), cadmium (Cd), and lead (Pb) through baby foods unfolds many concerns about their toxic effects on growth and health. In this study, occurrence and dietary intake of As, Cd, and Pb in stage 1 infant formula (0–6 months), stage 2 infant formula (7–12 months), cereal‐based meals, and biscuits were estimated. First, the levels of As, Cd, and Pb were determined with ICP‐MS, followed by the calculation of estimated daily intake (EDI), target hazard quotient (THQ), and hazard index (HI) for As and Cd, and margin of exposure (MoE) for Pb. Mean levels of As, Cd, and Pb were the highest in cereal‐based meals and biscuits as 15.5–11.1, 5.18–8.76, and 35.2–53.8 μg/kg, respectively. Newborns to 6 months old infants were estimated to be the highest exposed population to Cd and Pb (0.08 and 0.36 μg/kg bw/day), while infants aged 7–12 months old were exposed the highest to As. Based on the THQ, HI, and MoE findings, the current exposure levels from the selected baby foods to As, Cd, and Pb pose low potential chronic risks to both infant age groups. This research provides a roadmap for future investigations in chemical contaminants often detected in baby foods consumed regularly by Saudi infants. Mean levels of As, Cd, and Pb were the highest in cereal‐based meals and biscuits. The current exposure levels from the selected baby foods to As, Cd, and Pb pose low potential chronic risks to both infants age groups. It is especially essential in the cases of infants who depend majorly on formulas, do not get enough supplementary nutrients in their diet, or suffer of certain types of allergies  increased incidence of lung cancer was in the same range as the PTWI of 3 μg/kg bw/day  for As for being no longer health protective, as the benchmark dose for a 0.5%  executed by the Saudi Food and Drug Authority (SFDA). Based on such data, detailed health assessment was performed to evaluate noncarcinogenic risks. In this monitoring program, infant formula and cereal‐based baby foods contaminated with As, Cd, and Pb have been selected for the assessment of dietary exposure and chronic noncarcinogenic risks associated with consumption for infants in the age group of 0–12 months old. Due to the inadequacy of thorough risk assessment reports of heavy metals in baby foods in Saudi Arabia, as well as the lack of maximum limits for certain types of heavy metals in baby foods, such as cereal meals and biscuits, this study was performed. Additionally, this study aims to assess the applicability of suggested maximum limits for heavy metals on such food items in Saudi Arabia. To the best of our knowledge, this is the first study that estimates the full risk assessment of Saudi infants to heavy metals in baby foods. Such results will provide basic data for food scientists and regulators in Saudi Arabia and in Middle East, as well as other importing countries of baby foods.22.1Data of 111 samples of commercially available food products for infants (newborns to 12 months) were collected and analyzed for heavy metals contamination during the 2020 NFMP executed by the SFDA. Toxic elements targeted in this study for baby food products are As, Cd, and Pb. Samples were collected throughout the year 2020 from pharmacies and main markets of three regions , Kingdom of Saudi Arabia. The baby food products under analysis were 39 samples of stage 1 infant formula (0–6 months), 22 samples of stage 2 (7–12 months), 33 sample of processed cereal‐based meals, and 17 samples of biscuits. Since most baby foods retailed in Saudi Arabia are imported, samples selected in this study consisted of 21 and 90 samples of domestic and imported products, respectively. These products were selected based on market share data of baby food brands commonly retailed in Saudi Arabia throughout the year 2020. The collected samples were preserved at room temperature and sent immediately to corresponding SFDA laboratories in the three regions selected.2.22.2.1All reagents used were of ultra pure grade. High purity nitric acid (65.0%) was provided by Romil Pure Chemistry. The multielement standard solution  and the internal standard solution (100 mg/mL) were obtained from Agilent.2.2.2For the preparation of a test sample, 200 g should be available from the laboratory sample. The sample was homogenized by a blender until each baby food product became a fine powder using a Titanium homogenizer . All materials used were sufficiently clean to avoid any contamination. A homogenized sample (0.50 g) was weighed into quarts vessels with 5 mL ultrapure nitric acid (65.0% v/v) and the reaction was accelerated in a pressure‐ and heat‐controlled Ultraclave microwave digestion system (Milestone ultraCLAVE) programmed to ramp temperature from 60.0 to 200°C over a 30‐min cycle, and at 120 bar pressure. Samples were then diluted in centrifuge tubes to 50 mL  with deionized water . An aliquot of 6 mL of the sample was pipetted into disposable ICP‐MS vials without filtration  is used for metal analysis. the radio frequency power for the ICP was set at 1500 W, and the plasma gas flow was 15.0 L/min, while the auxiliary (makeup) gas flow was 1.00 L/min. Helium gas was used in the collision cell with a flow rate 4.50 mL/min. Before injecting the samples, sensitivity, oxide ratio, and doubly charged ratio specifications were checked to minimize interferences. There are two choices of mode to determine metals analysis (no gas/helium) mode, all results were approved in helium mode. Recommended atomic masses for the elements under study are As 75, Cd 111, and Pb 208 amu. ICP‐MS method was applied and validated for the analysis of food including baby food samples according to the European standards   was confirmed to be >1.00. Additional quality control techniques used were solvent blank, fresh calibration standards, and spiked samples. Certified reference material was prepared and analyzed with each analysis batch to ensure the reliability and accuracy of the results.The validation was performed according to the internal validation procedure developed according to Eurachem guidelines  of baby food was calculated based on scoops and portion sizes recommended daily on the packaging as shown in Equation  next:. For infants aged 7–12 months, the recommended number of feeds per day ranged between 3 and 5 times/day. The estimated average daily consumption was calculated as recommended on the label of stage 2 infant formula was 129 g/day (seven scopes each one of 4.60 g/ formula bottle). For the other cereal‐based food products, the recommended daily number of cereal‐based meals was one meal per day and was calculated as recommended on the label , while the recommended daily number of biscuits were one stick per day (the stick weigh 8 g as described on the packaging).2.3.2EDI values were estimated using mean daily consumption (CONS) in g/day, body weight (BW) of Saudi infants in kg  represents the probable chronic risk from exposure to toxic elements present in food  describes an assessment of oral daily intake that is likely to be without deleterious effects during a lifetime for the general population  refers to the cumulative noncarcinogenic risks caused by multiple toxic elements that share combined adverse effects through food ingestion US EPA, . HI was 01) and the human EDI to evaluate the cancer risk upon chronic exposure, as illustrated in Equation (Margin of exposure (MOE) approach was adopted to evaluate the risks related to human health associated with Pb in baby foods. This was carried out due to lack of appropriate RfD and HBGVs for Pb MOE2.4p < 0.05) between various types of baby foods were present, nonparametric Kruskal–Wallis and the Mann–Whitney tests were applied. The tests were select in view of the fact that the current data did not follow a normal distribution. All tests were performed using IBM SPSS statistics for windows Version 21 (IBM Corp.). The p ≤ .05 was considered statistically significant.Statistical analysis was performed using Microsoft Excel 2016. Mean and standard deviation values for heavy metals content in the selected samples were determined. Statistical treatment of left‐censored data was based on the standard approach by Codex Committee on Contaminants in Foods CL 2021/78‐CF) . No significant differences were identified among As and Pb levels present in all baby food groups . However, the Kruskal–Wallis test showed significant differences in Cd levels between all baby food groups (Cd p [MB] .000 < .05). Mann–Whitney test was used to validate the significant differences in Cd contents between all baby food types, and it showed significant differences between infant formula and cereal‐based meals (p [MB] .000 < .05), infant formula and biscuits (p [MB] .000 < .05), cereal‐based meals and biscuits (p [MB] .007 < .05).In this study, As, Cd, and Pb were determined in all selected samples Figure . For staThe lowest mean concentrations of the contaminants were determined mainly in stage 1 infant formula, while the highest mean concentrations were observed in biscuits. The highest mean content of As was observed in cereal‐based meals and biscuits . The highest mean content of Cd was observed in cereal‐based biscuits (8.76 ± 5.56 μg Cd/kg) followed by cereal‐based meals (5.18 ± 6.16 μg Cd/kg) and stage 2 infant formula (1.47 ± 1.63 μg Cd/kg). Pb mean contents were found the highest in biscuits (53.8 ± 68.6 μg Pb/kg), followed by cereal‐based meals (35.2 ± 55.7 μg Pb/kg).3.201 of neurodevelopmental toxicity were estimated as shown in Table Dietary exposure to toxic elements investigated in this study was estimated for two age groups: 0–6 and 7–12 months. For the first group (0–6 months), exposure assessment was based on an infant diet consisted of commercial formula only. For the second group (7–12 months), the assessment was based on a diet of commercial infant formula stage 2 and cereal‐based meals and biscuits only (for 7–12 months). Total intakes of heavy metals for both age groups based on LB, MB, and UB approaches were presented in Figure Intake estimation of As for 0–6 months was below the daily RfD set by USEPA for As, which was 0.30 μg/kg bw/day based on cardiovascular effects, and As contribution to the total intake was 19.5%. The intake of commercial infant formula (0–6 months) ranged from 0.05 to 0.07 μg/kg bw/day (LB‐UB), as shown in Figure Intake estimation of Cd for 0–6 months was below the daily PTWI set by EFSA for Cd as 2.50 μg/kg bw/week (corresponding to PTDI of 0.36 μg/kg bw/day) based on cardiovascular effects, and the mean contribution of infant formula was 21.4%  of Pb was determined as 0.50 μg/kg bw/day based on the observed neurodevelopmental toxicity and decreased IQ  was below the BMDL and ranged from 0.10 to 0.21 μg/kg bw/day (LB‐UB). Figure Intake estimation of Pb by 0–6 months old infants based on infant formula only was 0.21–0.50 μg/kg bw/day (LB‐UB). For embryos, infants, and children ≤7 years, a benchmark dose  is crucial to determine the health risks of lead on embryos, infants, and children , undiluted infant formula and cereal‐based meals, without considering the use of water in the process. Other sources of exposure to heavy metals might contribute to increased exposure, for example, drinking water, ready‐to‐eat purees in jars, gruels, baby juices, artificial toys, feeding bottles, pacifiers, and teethers  by the consumption of multiple food items, among which stage 1 infant formula accounted for the highest contribution of 20.0%, followed by 9.20% and 1.60% of cereal‐based food and stage 2 formula, respectively , rice starch could be used in formula attributing to its protein abundancy, hypoallergenic and antireflux potential compared to the standard or soy‐based formula  opinion which concluded that the consumption of foods by infants resulted in an exposure of Pb below the BMDLThis study is the first to conduct risk assessment on baby foods consumed by infants under 12 months in Saudi Arabia. Moreover, it can provide a research roadmap for future studies for further investigation. However, there is a lack of specific consumption data for Saudi infants and young children consuming the infant formula of both stages and cereal‐based meals and biscuits. Furthermore, the included baby food samples in the NFMP were restricted to undiluted infant foods  and ready to eat baby biscuits available at Saudi markets, while composite foods prepared at home for infants aged 7–12 months were not considered in this assessment. Therefore, the actual exposure to heavy metals might be at even lower levels than the results obtained in this study. Additionally, including other baby products that could affect the total intake such as homemade foods, ready‐to‐eat purees, gruels, baby juices, and teething crackers is highly encouraged for future research conducted through NFMP or other research groups and programs. Assessing the accurate dietary intakes of toxic elements by infants is challenging, as there are many factors that could affect their daily intake. At the first year of life, infants commonly have feeding problems. For instance, they usually have impaired gastrointestinal function or allergy reactions to certain foods. The effects that these factors may have on the total dietary intake of infants were not considered in this study. Essentially, the consumption data used to assess the dietary exposure does not essentially reflect the actual intake of infants.5Based on the occurrence data obtained from the NFMP of 2020, the mean concentrations of As and Cd in stage 1 and stage 2 infant formula, cereal‐based foods, and biscuits samples were in accordance with the maximum limits set by SFDA. When assessing the dietary intake of arsenic and cadmium in infants, the exposure levels were below the corresponding RfD values, as well as the levels of THQ and HI (<1.00). This indicates that the occurrence of As and Cd in the examined food groups pose low chronic health risks on infants in Saudi Arabia. Additionally, MOE for Pb revealed that the related health risks are likely to be low with prolonged exposure in infants, however, not in levels that can be neglected. In light of such evidence, the exposure of As, Cd, and Pb in the investigated baby food samples is likely to pose low chronic health concerns. Many limitations were observed including the infant formula and cereal meals samples used in this assessment were tested in dry form, as well as the lack of consumption data for Saudi children under the age of 1 year old consuming infant formula of both stages, cereal‐based meals and biscuits, and finally, the absence of toxic elements exposure evaluation in light of feeding on other homemade baby food and consuming breast milk alongside the tested baby foods in this study. Further research studies are a necessity to examine heavy metals in more diverse food items for infants and young children , evaluate other toxic elements or contaminants , and include other age groups . Finally, this study might assist in revising the current maximum limits of Pb, among many other heavy metals, in baby foods. This step could tremendously help in reducing the associated probable risks of heavy metals on infants.Najla S. Alharbi: Data curation (lead); formal analysis (lead); methodology (lead); project administration ; visualization (lead); writing – original draft (lead); writing – review and editing . Rawdah M. Akamsiei: Data curation (supporting); formal analysis (supporting); investigation (supporting); methodology ; writing – original draft ; writing – review and editing . Lama A. Almaiman: Data curation (supporting); writing – review and editing (supporting). Mostafa A. AL‐Samti: Formal analysis ; methodology ; resources (lead); validation (lead); writing – original draft (supporting). Hamad S. Al‐Mutairi: Formal analysis ; methodology ; resources (lead); validation (lead); writing – original draft (supporting). Bandar S. Al‐owais: Data curation (supporting). Majid M. AlKhalaf: Writing – review and editing . Mohammed A. Bineid: Conceptualization (lead); methodology ; project administration (lead); writing – review and editing (lead).The authors thank the Saudi Food and Drug Authority (SFDA) for financial support.The authors of this study claim no conflicts of interest.Ethical approval is not applicable to this study.The views expressed in this article are those of the authors and do not necessarily reflect those of the Saudi Food and Drug Authority (SFDA) or its stakeholders. Guaranteeing the accuracy and the validity of the data is a sole responsibility of the research team. The authors are not aware of any affiliation, membership, funding, or financial holding that might be perceived as affecting the objectivity of this study.


To explore the mediating role of self‐esteem in social anxiety and QoL during the COVID‐19 local epidemic in China.A cross‐sectional survey.This study conveniently selected 344 hypertensive patients from a Grade‐A tertiary hospital in Wuhu, Anhui Province, as the study population. Participants completed the demographic questionnaire, the social phobia inventory, the self‐esteem scale and the 36‐item short‐form health survey.β = 0.011, p > 0.05). Social anxiety showed an indirect effect on QoL . Self‐esteem fully mediated the association between social anxiety and QoL in hypertensive patients.Social anxiety, self‐esteem and QoL were significantly correlated with each other. Social anxiety showed no direct effect on QoL (Hypertensive patients in this study were participants during the data collection process. Nursing staff from the cardiology department at the hospital of a hospital in Wuhu City, Anhui Province assisted in the recruitment phase of the data collection process. The target number of participants was determined based on the criterion proposed by Kendall in 1975  according to 2018 Chinese guidelines for the prevention and treatment of HTN, HTN was defined as systolic blood pressure (SBP) ≥ 140 mm Hg and/or diastolic blood pressure (DBP) ≥ 90 mm Hg, and/or medical history of HTN and taking anti‐hypertensive medication. (2) hypertensive patients with age ≥ 45 years old, (3) essential HTN, (4) conscious and able to communicate normally with investigators. Exclusion criteria: (1) secondary HTN, (2) dementia or inability to answer the questions.3.2.3It was designed by the researcher based on previous similar studies. The questionnaire included age, gender, educational level, residence, cigarette smoking, alcohol use experience and physical exercise habit.p < 0.01).The SPIN .This scale .The SF‐36  was conducted by AMOS 21.0 to test the mediation role of self‐esteem between social anxiety and QoL. A sample of 5000 is set in Bootstrap for mediation analysis. The confidence level of Bias‐corrected confidence intervals is set to 95%.3.4All participants were voluntary and anonymous in this study. They gave written informed consent in accordance with the Declaration of Helsinki, and their personal information was maintained confidentially. This study was conducted as a field survey communication and allowed participants to ask any questions about the study, fully respecting their decisions. The Medical Ethics Committee of a medical college in Anhui Province啊 a hospital in Wuhu City, Anhui Province (2021‐LS‐03) approved this study.44.1As shown in Figure 4.2r = −0.305, p < 0.01) and QoL . Meanwhile, self‐esteem was positively associated with QoL .Table 4.3p < 0.001), gender (p < 0.05), exercise (p < 0.001) and self‐esteem (SES) (p < 0.001) were found to have significant associations, with an explanatory power for hypertensive patients' QoL was, thus, 28.20% (p < 0.001).Stepwise multiple regression analysis was performed to identify factors affecting the QoL of patients with HTN Table . In Mode4.4χ2/df = 2.257, p < 0.001, RMSEA = 0.062) was good. Figure β = 0.011, p > 0.05). Social anxiety could only affect QoL through self‐esteem . In sum, social anxiety and self‐esteem explained 35.00% of the variance in QoL.Table 5Recent research showed that social anxiety might play an essential role in the development of QoL , which is consistent with previous findings , which was in line with the study of Ran et al. , which was consistent with the findings of Liang et al. . Social anxiety (SPIN) did not affect QoL . Furthermore, SEM suggested that self‐esteem was an essential mediation variable that affected the QoL of hypertensive patients. Social anxiety did not directly affect QoL and could only predict QoL when self‐esteem played a role. It certainly helps us better understand how these factors affect the QoL of hypertensive patients and provides a reference for improving the QoL.This study investigated the factors associated with QoL in hypertensive patients during the COVID‐19 local epidemic in China. It explored mediating roles by integrating the concepts of social anxiety, self‐esteem and QoL into a single model. The results showed that self‐esteem (SES) was a positive predictor of QoL (R2 = 0.111) was more important than social anxiety (Adjusted R2 = 0.024) in predicting QoL in hypertensive patients. Among the factors affecting QoL in hypertensive patients, self‐esteem accounted for 39.40% (0.111/0.282). This finding gives a new perspective on promoting QoL in hypertensive patients. Since age and gender are non‐modifiable factors affecting QoL, further research should focus on increasing self‐esteem, decreasing social anxiety and increasing physical activity to improve the QoL of patients with HTN.Analysis of the factors that affect hypertensive patients' QoL showed that the regression model including age, gender, physical activity and self‐esteem was statistically significant and accounted for 28.20% of QoL. In particular, patients with lower age, regular exercise and higher self‐esteem were associated with higher SF‐36 scores. The regression model can be inferred that hypertensive patients with these characteristics indicate better QoL and are less affected by external factors such as disease and COVID‐19. Young people made full use of online social networks, were socially active, their parents and spouses were alive, their careers and families were prosperous and they received more social support . Finally, most patients are hospitalized due to changes in their condition. There will be changes in psychological status. The above factors might influence the interaction among this study's mediating models of social anxiety, self‐esteem and QoL. Therefore, in the context of an important public health event (COVID‐19), the results of this study are hardly unaffected by these factors. The mechanisms involved need to be explored in‐depth in later studies.6Hypertensive patients need to take medication for life which gradually deteriorates their physical and mental condition compared to healthy people. Their management of chronic disease is a long‐term process. Interventions should be targeted and implemented to improve their self‐esteem. Set up support groups for patients to communicate, encourage and support each other. To utilize social support, patients should contact family and friends more frequently, who give positive comments to the patient when appropriate. Self‐esteem will then slowly increase. Organize educational programs to teach the patient's family how to assess the patient's physical and mental condition. Hospitals and nursing professionals should inform patients of the correct information and protective measures about COVID‐19 to reduce their psychological burden of going out. Patients should be encouraged to participate in family affairs to the best of their ability and reflect on their self‐worth. In addition, patients need to emphasize physical activity. They are available for group activities (wearing masks) such as tai chi, dancing and jogging. Through these measures, patients learn how to improve their self‐esteem, exercise and increase opportunities for social interaction. All of these measures improve patients' self‐esteem and gradually improve their QoL.7First, this cross‐sectional study cannot determine the cause‐and‐effect relationship between the variables. Second, our analysis was from only one hospital, and the sample size was small and limited to the southern region of one province. Finally, the COVID‐19 situation in other cities might differ from Wuhu, China. Thus, the results of this study need to be further tested using additional regional samples, especially those regions with varying degrees of COVID‐19 severity.8In summary, social anxiety, self‐esteem and QoL were significantly correlated. Within the context of the COVID‐19 local epidemic in China, self‐esteem fully mediated the association between social anxiety and QoL. And QoL in hypertensive patients would be further improved by increasing self‐esteem and reducing social anxiety levels.Yuanzhen Li: conceptualization and supervision. Mengzhao Gao, Yidan Lu, Min Shi, Haoran Zhou, Helian Zhou and Xia Kuang: investigation. Mengzhao Gao, Yidan Lu and Lin Zhang: data curation and software. Writing – original draft: Mengzhao Gao, Yidan Lu, Min Shi, Haoran Zhou, Helian Zhou, Xia Kuang and Yuanzhen Li. Writing – review and editing: Mengzhao Gao, Yidan Lu and Yuanzhen Li. All authors gave the final approval of the manuscript.The authors have no competing interests to declare.The authors have checked to make sure that our submission conforms as applicable to the Journal's statistical guidelines described here. Dr. Lin Zhang among the team was responsible for the verification of statistical aspects of this study.