Carboxyl/cholinesterases (CCEs) represent a family of enzymes distributed in many organisms, including insects. Despite their relatively simple catalyzed hydrolysis reaction, CCEs facilitate insects’ adaptation to chemical signals and stressors from the environment through various trajectories, including developing pesticide resistance, facilitating the adaptation of insects to their host plants, and manipulating insect behaviors. The CCE-mediated mechanisms of pesticide resistance to organophosphate, carbamate, or pyrethroid pesticides comprise enhanced metabolism, the sequestration of pesticides to prevent them from reaching their target sites, or conformational changes in target sites to prevent pesticides from binding. In addition, CCEs aid in the adaptation to chemical signals through the olfactory system by degrading insect semiochemicals. In this review, we summarize the current knowledge of the classification, structures, and functions of insect CCEs, which will help the development of more sustainable pest control strategies in the future.Insects have evolved several intricate defense mechanisms to adapt to their chemical environment. Due to their versatile capabilities in hydrolytic biotransformation, insect carboxyl/cholinesterases (CCEs) play vital roles in the development of pesticide resistance, facilitating the adaptation of insects to their host plants, and manipulating insect behaviors through the olfaction system. CCEs confer insecticide resistance through the mechanisms of qualitative or quantitative changes of CCE-mediated enhanced metabolism or target-site insensitivity, and may contribute to the host plant adaptation. CCEs represent the first odorant-degrading enzymes (ODEs) discovered to degrade insect pheromones and plant odors and remain the most promising ODE candidates. Here, we summarize insect CCE classification, currently characterized insect CCE protein structure characteristics, and the dynamic roles of insect CCEs in chemical adaptation. Pesticides are the mainstay for the control of numerous pest populations in agricultural and urban ecosystems. However, the extensive application of pesticides accelerates the accumulation of resistance-associated genes in survivors and causes the development of pesticide resistance. Commonly, multiple mechanisms are involved in pesticide resistance, including changes in cuticle thickness and composition, behavioral avoidance, target site insensitivity, sequestration, and metabolic detoxification . With thInsects have evolved sophisticated mechanisms to adapt to various xenobiotics, such as plant allelochemicals, insect odors and pheromones, pesticides, and industrial pollutants ,2,3. MetCCEs constitute a multigene family of α/β-hydrolase fold enzymes that are distributed in numerous organisms including insects, mammals, plants, and microorganisms ,11,12,13Insect CCEs are divided into 14 clades in three classes based on their physiological and biochemical functions: dietary/xenobiotic detoxification (clades A–C), pheromone/hormone processing (clades D–H), and neuro/developmental functions (clades I–N)  9,19,20,2019,20.Myzus persicae confer organophosphate (OP) resistance to the resistant populations .,20.19,20ulations ,27. Apoleromones ,29,30. Seromones . One honeromones . JHEs  and five noncatalytic clades (Clades I-N) ,40. Asidnogaster ,42,48,49D. melanogaster with an aspartate in place of the glutamate , D. melanogaster (DmAChE:PDB 1QO9), Homo sapiens (hAChE:PDB 4EY4), Mus musculus (mAChE PDB:2HA2) and Torpedo californica (TcAChE PDB:2WG2) revealed α-carbon backbone root-mean-square-deviation (RMSD) ranges from 1.1 Å to 1.9 Å . A. A55]. Ato 1.9 Å . A key sto 1.9 Å . In the s 17.9 Å ,56. Muchte Trp83 B 52]. M. MAnophecumented A [56]. SLucilia cuprina α-esterase LcαE7 caused gaining of function hydrolysis activity toward the OP chlorfenvinphos, and in turn resulted in lost activity towards model substrates α-naphthyl acetate, p-nitrophenyl acetate, and methylthiobutyrate , DmEST6 (PDB:5THM), and MsJHE (PDB:2FJ0) revealed that the hormone/pheromone processing class exhibits the typical esterase structural elements (CqESTβ2 (PDB:5W1U), DmEST6 (PDB:5THM) and MsJHE (PDB:2FJ0) are superposed with DmAChE PDB:1QON, the largest α-carbon RMSDs are observed in N and C terminal regions, along with the subdomain 1 and subdomain 2 regions adjacent to the catalytic triad that play a dominant role in defining the size and shape of the binding pocket and thus the substrate specificity of CCEs [AgAChE was found to have a binding pocket volume of 1736.2 Å, MsJHE pocket volume was 1308.0 Å, LcαE7 pocket volume was 2725.5 Å, DmEST6 pocket volume of 935.0 Å, and CqESTβ2 pocket volume was 4735.5 Å [AgAChE and acetylcholine, MsJHE and juvenile hormone, LcαE7 and fatty acid methyl esters, DmEST6 and short to medium chain length food odorant esters, and CqESTβ2 with mono or diacylglycerols of medium chain length [CqESTβ2, it was found to play an insecticide resistance role in Culex quinquefasciatus through a sequestration mechanism, exhibiting a high affinity for OPs but also a low turnover number [The hormone/pheromone processing class contains the insect β-esterase and juvenile hormone esterase ,53,73. Celements E and 1F. of CCEs ,52,53,73The function and specificity of individual CCEs can be defined by their substrate binding affinity and the rate at which they are able to catalyze the hydrolysis of different esters. One of the most substrate specific is the JHE in insects . AlthougDeciphering the biochemical functions of insect CCEs and their substrate spectrums is vital for understanding the mechanisms by which insects adapt to their chemical environment. Gains in our understanding of such mechanisms will the facilitate management of pesticide resistance in insect pests and aid in protecting pollinators and other beneficial insects from xenobiotics. The current section will synopsize mechanisms of xenobiotic adaptation associated with insect CCEs that are involved in metabolizing pesticides, plant allelochemicals, and degrading volatile semiochemicals.cis- or trans-), or gene amplification. The enhanced CCE expression (quantitative changes) can also be combined with mutations in the coding sequence  that cause boosted metabolism of insecticides or increased binding affinity, resulting in higher levels of insecticide resistance [Ace genes, which encode insect AChEs. Multiple mutations often lead to greater insensitivity and resistance than one mutation [Insect CCE, cytochrome P450 and GST enzyme families are commonly implicated in the development of insecticide resistance . Most ensistance . Insensimutation ,87. The Myzus persicae) variants showed cross resistance to OPs, carbamates and pyrethroids by upregulating the expression of β-esterases E4 or FE4 (different variants boost copies of E4 or FE4). Resistant variants had as many as 80 more copies of the gene at the genomic DNA level than the susceptible variant, with no change in the enzyme coding sequence [M. persicae had for both insecticide classes. Interestingly, E4 was also able to hydrolyze the trans-isomers of the pyrethroid insecticide permethrin, but not cis-isomers, and at a significantly lower rate than OPs and carbamates [M. persicae populations through both E4 enhanced expression and target-site insensitivity (a mutation in the AChE enzyme or the voltage gated sodium channels) [Nl-EST1 gene in the brown planthopper, Nilaparvata lugens, which shares 38% sequence identity (higher than other insect CCEs) with M. persicae E4. Similar to E4, the Nl-EST1 gene expression was enhanced through gene amplification in OP and carbamate resistant variants, while it showed no change in the enzyme coding sequence between the resistant and susceptible variants. The gene copies from amplification were less than that of E4 and correspond with the relative resistance level [Peach-potato aphid  ,91. A sice level ,93.L. cuprina, one mutation (Gly to Asp) within the active site of an α-esterase LcαE7 (E3) was detected to enhance the hydrolysis of diethyl OPs and subsequently reduce the CCE natural substrate hydrolysis activity [Musca domestica [L. cuprina, with a change from Trp to Leu close to the catalytic triad. This mutation allows for greater malathion hydrolysis in malathion resistant L. cuprina populations than susceptible ones, which show malathion hydrolysis but at very low levels. When the mutant E3 was phosphorylated by the oxidized malathion, malaoxon, it could reactivate and continue hydrolyzing malathion and malaoxon, whereas susceptible variant malathion hydrolyzing CCEs were irreversibly inactivated by phosphorylation [In a diazinon OP resistant variant of the sheep blowfly activity . The samomestica ,94. An arylation .Bradysia odoriphaga, which in its larval form is a crop pest of the Chinese chive, has been known to develop insecticide resistance in field populations. The CCE BoαE1 of the dietary/detoxification class, isolated from a laboratory colony, was transcriptionally upregulated after malathion exposure and showed hydrolysis capability for malathion. The induced upregulation of BoαE1 was suggested to contribute to the resistance found in field populations [C. quinquefasciatus, a CCE Cqestβ2 was upregulated and played a major role in OP resistance through acting as a “sponge” to intercept insecticides before reaching their target. The stopped-flow kinetic analysis showed that a covalent intermediate formed after the rapid OP binding of Cqestβ2 with high affinity, resulting in irreversible and prolonged enzyme inhibition [Culex tarsalis generated a new malathion carboxylesterase (MCE I) that could hydrolyze malathion 18 times faster than MCE II. MCE II was in both the susceptible and resistant strains and neither MCE I nor MCE II showed higher expression, indicating that the insecticide resistance was due to qualitative changes but not quantitative changes [M. domestica through the constitutive and permethrin induced upregulation of multiple α-esterases, β-esterases, and integument esterases [Cimex hemipterus through OP and carbamate induced upregulation [SY-VP was functionally characterized in two genetically independent spirodiclofen resistant Tetranychus urticae strains. Studies suggested that the sequestration of spirodiclofen by overexpressed CCE04SY-VP was likely to contribute to the spirodiclofen resistance in these two strains [Plutella xylostella strains [PxαE14, may play roles in resistance to multiple OP and pyrethroid insecticides in P. xylostella [The fungus gnat ulations . In C. qhibition . By cont changes . CCE medsterases ,98, and gulation , with a gulation . Recentl strains . More re strains . Furtherlostella .D. melanogaster [Aphis gossypii [Leptinotarsa decemlineata [M. domestica [T. urticae [ace-2 gene that had independently occurred four times in different mosquito species: C. pipiens, A. albimanus, and A. gambiae [AChE insensitivity has developed in many insect species, including nogaster , Aphis ggossypii , Leptinomlineata , M. domeomestica , and T.  urticae . Certain urticae . One of  gambiae .CCEs combined with GSTs and Cytochrome P450s are three major superfamilies of detoxification genes involved in plant allelochemical detoxification which facilitate insect host plant adaptation ,111,112.Papilio rutulus larvae were tolerant of diets containing phenolic glycosides tremulacin and salicortin, both of which were found in one of their host plants, quaking aspen [Lymantria dispar), feeding on a diet with added phenolic glycosides (2 or 4% wet weight) induced both CCE and GST activity in the midgut of the larvae, indicating CCEs facilitating the host plant adaptation of Spongy moth [Sitobion avenae, CCE activity was induced by indole alkaloid gramine. The increase in CCE activity was positively correlated with the concentration of gramine, suggesting that CCE may contribute to the host allelochemical adaptation [The western tiger swallowtail ng aspen . When feng aspen . In anotngy moth ,117. In aptation .Helicoverpa assulta, and tobacco cutworm, Spodoptera litura, differences in diets and plant allelochemicals affected both the expression of detoxifying enzymes and susceptibility to insecticides [S. litura showed a 4.9-fold increase in CCE activity and a 2.4-fold increase in GST activity in addition to a greater resistance to Phoxim, Methomyl, Chlorfenapyr, Fenvalerate, and Emamectin benzoate when fed with tobacco plants compared with sweet potato plants [T. urticae, an increase in OP dimethoate pesticide mortality was detected after feeding for seven days on cucumber versus lima bean plants. This enhanced mortality was correlated with a 60% decrease in overall esterase activity, indicating the potential roles of CCE-mediated detoxication in host plant adaptation and insecticide resistance [In tobacco budworm, cticides ,120,121.sistance .A. polyphemus, in 1981 [A. polyphemus sexes [Olfaction plays vital roles in insect communication. One of the major olfactory organs in insects is the antenna, which detects volatile semiochemicals  from the external environment for host plant location, mating, and avoiding predators ,122. Aft in 1981 . In addi in 1981 , and theus sexes . HoweverSpodoptera littoralis (Egyptian cotton leafworm), SICXE10 and SICXE7, two CCEs predominantly expressed in olfactory sensilla, both hydrolyze the female-produced sex pheromones (Z9E11-14:Ac and Z9E12-14:Ac) and the green leaf volatile ((Z)-3-hexenyl acetate) in vitro. SICXE10 and SICXE7 were shown to be expressed in pheromone-sensitive sensilla and short sensilla, and both esterases hydrolyzed Z9E11-14:Ac, Z9E12-14:Ac, and (Z)-3-hexenyl acetate, suggesting that these two CCEs play a role in degrading pheromone and plant odorants [S. exigua (Beet armyworm) for SexiCXE11 and SexiCXE14, both showing hydrolysis activity towards host plant volatiles and pheromone esters [CCEs represent the first ODEs discovered to degrade insect pheromones and plant odors and remain the most promising ODE candidates. CCEs that are involved in odorant degrading mainly belong to β-esterases, integument esterases, or mitochondrial and cytosolic CCEs. In odorants ,128. Sime esters ,129.D. melanogaster with high expression in the third antennal segment. Est-6 could hydrolyze multiple volatile food esters but had little to no hydrolysis activity towards the pheromone cis-vaccenyl acetate [P. japonica, one ODE PjapPDE was found to specifically metabolize sex pheromone, R-japonilure. It was also able to hydrolyze the enantiomer (S-japonilure), but at a much slower rate. This enantiomer S-japonilure happened to be an important sex pheromone of a competitive species and a behavioral antagonist for the Japanese beetle [PxylCCE16a and PxylCCE16c, were identified and characterized in diamondback moth P. xylostella [Grapholita molesta, two β-esterases (GmolCXE14 and GmolCXE21) able to hydrolyze ripe fruit esters were discovered and functionally characterized. In addition, another β-esterase and one integument esterase, GmolCXE1 and GmolCXE5, were found to be able to hydrolyze the sex pheromone (Z/E)-8-dodecenyl acetate [Two ODEs, Est-6 and JHE-dup, were uncovered in  acetate ,130. How acetate ,131. In e beetle . Recentllostella . Both CC acetate . CCEs constitute a multigene family of enzymes that catalyze the hydrolysis of structurally diverse xenobiotics, including pesticides, insect and plant odors, insect pheromones and hormones, as well as environmental pollutants. Studies have suggested that insect CCEs play multiple vital roles in facilitating the adaptation of insects to their chemical environment. Understanding the mechanisms of insect CCE-mediated chemical adaptation will help in advancing the management of insect pests. With recent advances in genome sequencing, X-ray crystallography and functional genomics, our knowledge of insect CCE classification, protein structure characteristics and the dynamic roles of insect CCEs in chemical adaptation will improve by leaps and bounds.


The C1q/TNF-related protein (CTRP) family affects inflammation regulation, energy metabolism, and insulin signaling. However, their role in acute coronary syndrome (ACS) development is unclear. In this cross-sectional study, we aimed to investigate the association between CTRP family and ACS.We enrolled 289 consecutive inpatients with suspected ACS. Serum CTRP family, tumor necrosis factor-α (TNF-α), and adiponectin (ADP) levels were assessed using enzyme-linked immunosorbent assay (ELISA). Multivariate logistic regression and subgroup analyses were used to assess risk factors for ACS. Spearman's tests were used to analyze correlations between CTRP family and continuous variables.p < 0.05). After adjusting for confounding factors, CTRP family were independently associated with ACS (p < 0.05). The association between serum CTRP family levels and ACS was stable in various subgroups according to sex, age, diabetes mellitus, and dyslipidemia status (p for interaction > 0.05). Increasing tertiles of serum CTRP1 levels, significantly increased ACS risks, which decreased gradually with increasing CTRP2, CTRP12, and CTRP13 tertiles (p for trend < 0.05). Additionally, serum CTRP1, CTRP2, CTRP13, and CTRP15 levels were weakly correlated with the severity of coronary artery stenosis.Serum CTRP family levels differed significantly between ACS and Control groups (CTRP1 and CTRP5 were identified as independent ACS risk factors, whereas CTRP2, CTRP3, CTRP9, CTRP12, CTRP13, and CTRP15 were independent protective factors for ACS. CTRP family, especially CTRP1 and CTRP3 could be novel potential clinical biomarkers of ACS. As a chytokines .C1q/TNF-related protein (CTRP) are a family of adiponectin (ADP) paralogs, including CTRP1 to CTRP15, which show the highest expression in adipose tissue around the heart . Howevern = 210) and Control (n = 79) groups according to the diagnostic criteria of ACS  ≥ 100 mg/dL or previously diagnosed type 2 DM (T2DM) , history of smoking, dyslipidemia, hypertension, diabetes mellitus (DM), metabolic syndrome (MetS), history of stroke, and family history of CAD, were collected by our research team. Blood pressure at admission were recorded by one single measuring before the CAG. Routine blood test results, and serum biochemistry results, such as total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), albumin (ALB), creatinine (Cr), homocysteine (Hcy) were also recorded. MetS was defined as the presence of three or more of the following criteria: BMI > 30 kg/mM (T2DM) .The angiogram results were independently evaluated and explained by two experienced interventional cardiologists who were blinded to this study. The severity of coronary artery stenosis was quantitated using the international Gensini score and a guide for score calculation .Blood samples of all patients were drawn from the radial artery before the CAG was performed. The blood samples were centrifuged at 3,000 g for 10 min, and then stored at −80 °C. The serum levels of CTRP1 (MM-2097H1), CTRP2 (MM-60202H1), CTRP3 (MM-1628H1), CTRP5 (MM-610054H1), CTRP9 (MM-2503H1), CTRP12 (MM-50530H1), CTRP13 (MM-60207H1), CTRP15 (MM-60212H1), TNF-α (MM-0122H1), and ADP (MM-1535H1) were measured using an enzyme-linked immunosorbent assay (ELISA) kit .via PASS 15 software . The Kolmogorov-Smirnov test was used to analyze the continuous variables to verify the normal distribution of variables, and the results were reported as medians and interquartile ranges for skewed distributions. The Mann-Whitney U test was used to compare the relationship of all continuous variables between the ACS and Control groups. Categorical variables were expressed as frequency and percentages, and compared using the chi-squared test.Statistics analyses were performed using SPSS 26.0  and GraphPad Prism 8.0 . The sample size was calculated p < 0.1.Receiver-operating characteristic (ROC) curves were used to calculate the cutoff value of adipocytokines for diagnosing ACS. Youden's index (sensitivity + specificity – 1) was calculated, the maximum value which corresponds to the optimal cutoff value of CTRP family members. Multivariable logistic regression models were constructed to evaluate the association between CTRP family and ACS in the general population and different subgroups. The odds ratio (OR) was determined based on 1 standard deviation (1-SD) increases of serum CTRP family levels. In addition, for subgroup analysis, the baseline variables were selected and entered into the multivariable logistic regression models at p for trend, which was calculated using CTRP family tertiles as a continuous variable. Spearman's tests were used to analyze the correlations between CTRP family and continuous variables. A Kruskal-Wallis H test was used to test for differences between the quartiles groups of the Gensini score. The missing data were handled automatically by the statistic software. Two-tailed p < 0.05 were considered significant.To examine the independent performance of CTRP family for assessing the occurrence of ACS, participants were divided into three groups  according to the tertiles of serum CTRP family levels. We examined the linear trend using p < 0.05).A total of 289 patients were included in this study and their baseline clinical characteristics are presented in p < 0.05). In addition, the angiographic findings showed that coronary stenosis with three-vessels involvement (39.1%) was the most common.Patients with ACS showed significant differences in white blood cell (WBC) count, platelet count, TG, HDL-C, ALB, Cr, serum uric acid, Hcy, creatine kinase (CK), CK-MB, left atrial (LA) diameter, left ventricular end-systolic diameter (LVESD), and left ventricular ejection fraction (LVEF) .p < 0.001; The AUC of CTRP2, CTRP3, CTRP9, CTRP12, CTRP13, CTRP15, and ADP were 0.810 , 0.800 , 0.706 , 0.740 , 0.794 , 0.748 , and 0.704 . Furthermore, the predictive power for ACS improved after data for CTRP2, CTRP3, CTRP9, CTRP12, CTRP13, and CTRP15 were combined , 0.668 , 0.350 , 1.858 , 0.661 , 0.653 , 0.716 , and 0.696 . CTRP1, as a pro-inflammatory adipokines, had a greater causative effect on ACS. Whereas, CTRP3 has a greater protective effect on ACS than other anti-inflammatory adipokines.p < 0.001 vs. 0.400 , p < 0.05, respectively; p for interaction < 0.05]. Moreover, the CTRP5 expression level was an independent risk factor for ACS in patients without, however, not in those with DM . The specific subgroup analysis results are illustrated in Subsequently, the independent association between CTRP family and ACS was also assessed in various subgroups according to sex , age (≥ 65 or <65 years), and DM and dyslipidemia status (with or without). The CTRP3 expression level exhibited a higher protective role against ACS in male than in female patients . In contrast, the risk of ACS showed a downward trend with increasing CTRP2, CTRP12, and CTRP13 expression levels (p for trend < 0.05). However, the protective role of CTRP3 and CTRP15 expression for ACS exhibited a U-shaped trend with increasing levels (p for trend < 0.05).p < 0.05).The results illustrated in n = 81, Gensini score ≤ 26.5), Q2 , Q3 , and Q4 . The expression level of CTRP1 was increased across the quartiles of the Gensini score. In addition, CTRP2, CTRP3, CTRP12, CTRP13, and CTRP15 levels showed a decreasing trend across the quartiles of Gensini score  CTRP family members, as pro-inflammatory or anti-inflammatory adipokines, were independent predictors for ACS; (2) the association between CTRP family and ACS was not significantly affected by sex, age, DM, and dyslipidemia status; (3) increasing levels of CTRP1, CTRP2, CTRP12, and CTRP13 showed a linear trend with the risk of ACS; and (4) the serum levels of CTRP1, CTRP2, CTRP13, and CTRP15 showed a weak correlation with the severity of coronary artery stenosis. To the best of our knowledge, compared with previous studies, this is the first study to elucidate and compare the diagnostic performance and causative risk of eight CTRP family members for ACS in the general population and different subgroups.As the leading cause of ACS, atherosclerosis is a complex process, and its development involves lipids, immune cells, vascular smooth muscle cells (VSMCs), and various adipokines . PreviouA previous study showed that serum CTRP1 levels were higher in patients with CAD than in those without CAD, which can promote the expression of adhesion molecules and synthesis of inflammatory cytokines through activating the p38 mitogen-activated protein kinase (MAPK)/nuclear factor (NF)-kB pathway . Our stup for interaction > 0.05). However, Ilbeigi et al. -LDL-induced inflammation and endothelial dysfunction by activating the phosphoinositide 3-kinase (PI3K)/Akt/endothelial nitric oxide synthase (eNOS) pathway -6 and TNF-α secretions from macrophages  is mainly expressed in the muscle tissues. In our study, we found CTRP15 to be an independent protective factor for ACS, which is consistent with the findings of Zhang et al. . Mechanirophages . Furtherclerosis . Shokoohclerosis  reportedp for interaction > 0.05). However, the protective effect of CTRP3 appeared stronger in male patients than in female patients. Wagner et al.  awarded to LS and the Department of Education Graduate Innovation Funding of Hebei Province (Grant no. CXZZSS2022139) awarded to YL.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.


Enhancer RNA (eRNA) has potential utility in the diagnosis, prognosis and treatment of cancer, but the role of eRNAs in NSCLC metastasis is not clear; (2) Methods: Differentially expressed transcription factors (DETFs), enhancer RNAs (DEEs), and target genes (DETGs) between primary NSCLC and metastatic NSCLC were identified. Prognostic DEEs (PDEEs) were screened by Cox regression analyses and a predicting model for metastatic NSCLC was constructed. We identified DEE interactions with DETFs, DETGs, reverse phase protein arrays (RPPA) protein chips, immunocytes, and pathways to construct a regulation network using Pearson correlation. Finally, the mechanisms and clinical significance were explained using multi-dimensional validation unambiguously; (3) Results: A total of 255 DEEs were identified, and 24 PDEEs were selected into the multivariate Cox regression model (AUC = 0.699). Additionally, the NSCLC metastasis-specific regulation network was constructed, and six key PDEEs were defined ; (4) Conclusions: This study focused on the exploration of the prognostic value of eRNAs in the metastasis of NSCLC. Finally, six eRNAs were identified as potential markers for the prediction of metastasis of NSCLC. Lung cancer is the malignant tumor with the highest incidence and mortality rates in recent years. Approximately 1.6 million people die per annum as a result and lung cancer death rates worldwide are estimated to be higher by the World Health Organization (WHO) . Non-smaEnhancers are short genomic regions ,8 that cHowever, the eRNA regulation mechanisms underlying NSCLC metastasis have not been elucidated, and eRNA targeted anti-cancer agents that can improve the prognosis of NSCLC patients are still insufficient. Here, differentially expressed transcription factors (DETFs), eRNAs (DEEs), and target genes (DETGs) between primary and metastatic NSCLC patients were determined. Prognostic DEEs (PDEEs) were further identified using univariate and multivariate Cox regression analyses, based on which a metastatic NSCLC-specific prognosis prediction model was constructed. Significant immune cells and immune-related pathways were identified using cell type identification by estimating the relative subsets of RNA transcripts (CIBERSORT)  and singThe mechanism by which eRNAs promote the transcription of target genes was shown in In addition, we can see from the heatmap plot A and volp < 0.05)  C and int < 0.05) D.For the evaluation of the independent prognostic value of RS, the formula mentioned in the methods was applied to calculate the RS for each NSCLC patient, and the distribution of NSCLC patients with low and high RS was shown by the risk scatter plot A and risp < 0.001) (p < 0.001) (p < 0.001) models  C. Furthe< 0.001) F and mul) models G, which ) models A–D.p < 0.05.In all samples, the composition of 22 immune cells was estimated by the CIBERSORT algorithm E, MacropThe expression levels of 30 DETFs were illustrated in a heatmap plot A and volp < 0.05, pairwise interactions between 24 immune-related gene sets and 13 PDEEs with a |correlation coefficient| > 0.20 and p < 0.05, and pairwise interactions between 34 hallmark pathways and 16 PDEEs with a |correlation coefficient| > 0.25 and p < 0.05.Finally, several elements aforementioned were considered to have significant correlations which were extracted for the construction of a regulation network. Specifically, this regulation network consisted of pairwise interactions between 24 DETFs and 15 PDEEs with a |correlation coefficient| > 0.20 and p-value < 0.05.The heatmap plot A and volp-value < 0.05.Besides, 38 RPPA protein chips and 11 PDEEs were identified to have significant correlations with the criterion of a |correlation coefficient| > 0.25 and p < 0.001), EXT1 , PAI1 , EGFR , HALLMARK_P53_PATHWAY , and Mast cell resting  in NSCLC metastasis. CASTOR2 mainly positively regulated H2AFX , SLC23A2 , TFRC , HALLMARK_G2M_CHECKPOINT  and NK cell resting  in NSCLC metastasis. CYP4B1 mainly positively regulated FOS , GTF2IRD2B , NAPSINA , HALLMARK_FATTY_ACID_METABOLISM , Mast cell activated  and Type_II_IFN_Reponse  in NSCLC metastasis. GTF2H2C mainly positively regulated NAIP , TTF1 , HALLMARK_PROTEIN_SECRETION , Macrophage M2  and iDCs  in NSCLC metastasis. PSMF1 mainly positively regulated TP63 , EXT1 , TFRC , HALLMARK_MYC_TARGETS_V1  and Mast cell resting  in NSCLC metastasis. TNS4 mainly positively regulated TP63 , EXT1 , CD49B , HALLMARK_P53_PATHWAY  and Mast cell resting  in NSCLC metastasis.Twenty-eight DEGs were eventually integrated to construct a regulatory network, and the expression levels of them between primary NSCLC samples and metastatic NSCLC samples were shown in the heatmap plot A. Specifp value < 0.001) may be the best small-molecule bioactive inhibitor that may inhibit NSCLC metastasis by suppressing the expression of key DEGs in this study.Because eRNA-related transcriptional regulatory changes were implicated in the pathological processes of NSCLC, and traditional long-term treatment with drugs may result in refractoriness and unsatisfactory outcomes, it is urgent to find potential inhibitors which target NSCLC-related PDEEs, DETGs, and DETFs. Therefore, the small-molecule bioactive inhibitors for DEGs within the regulatory network in this study were identified based on the CMap database. The heatmap plot D showed https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE153935, accessed on 8 June 2022) for validation of the subcellular locations of six key PDEEs. Fifteen cell clusters and seven cell types  were identified by t-distributed Stochastic Neighbor Embedding (t-SNE) analysis (Data of the single-cell RNA sequencing (scRNA-seq) from 24 NSCLC samples (GSE153935) were obtained from the GEO database  A, three p < 0.05).Multiple databases were utilized for reducing the bias caused by different platforms and to improve the reliability of our results. The key role of PDEEs including ANXA8L1, CASTOR2, CYP4B1, GTF2H2C, PSMF1, and TNS4 in pathogenesis and metastasis of NSCLC were validated based on GEPIA , UCSC xehttps://hanlab.uth.edu/eRic/, accessed on 6 August 2021) [p < 0.05). The clinical characteristics, drug responses, and target genes of six key PDEEs were validated by the eRic database (st 2021) . The resThe accessibility in chromatin of key PDEEs was validated by the data from ATAC-seq downloaded from the TCGA database and the results in LUAD and LUSC were shown in The Chip-seq validation was performed by the Cistrome database to determine whether PDEEs are bound to DETFs binding sites, PDEEs and DETFs  with the highest correlation were selected for further analysis. Finally, six key PDEEs were determined to be bound to the DETFs binding sites .NSCLC is the main type of lung cancer , whose mIn our study, 24 PDEEs were identified and based on them, the risk score was defined by the multivariate Cox regression model (AUC = 0.699). Furthermore, the risk score was identified as the significant predictive factor for the prognosis of NSCLC patients by Univariate and multivariate Cox regression analysis adjusted by age, gender and stage. In addition, for the exploration of potential mechanisms of NSCLC metastasis, correlation analysis was utilized to construct the NSCLC metastasis-specific regulation network, including key PDEEs, DETGs, DETFs, RPPA protein chips, hallmark signaling pathways, immune-related gene sets, and immune cells. Finally, six key PDEEs including ANXA8L1, CASTOR2, CYP4B1, GTF2H2C, PSMF1 and TNS4 were identified.ANXA8L1 (annexin A8 like 1) encoded one of the top 10 antigens identified by the majority of serological tests for pemphigus vulgaris patients . High exCASTOR2 (cytosolic arginine sensor for mechanistic target of rapamycin complex 1 (mTORC1) subunit 2) is a subtype of CASTOR, a kind of protein readily detectable in vertebrates . CASTOR2As a superfamily of enzymes related to phase I drug metabolism, Cytochrome P450 (CYP) was involved in multiple biological processes, such as maintaining calcium homeostasis, fatty acid metabolism, and steroid and cholesterol biosynthesis . CYP4B1 GTF2H2C (GTF2H2 family member C) was a subtype of general transcription factor IIH subunit 2-like, related to transcription by RNA polymerase II and DNA nucleotide excision repair . The expPSMF1 (proteasome inhibitor subunit 1), the proteasome inhibitor PI31 subunit, was able to bind to the outer rings of the 20S proteasome directly or compete for 20S binding with the activating particles to inhibit the proteasome activities ,40,41. HTNS4 (Tensin 4) participated in the cell movement, which was induced by MET and was related to the GPCR signaling pathway. High expression of TNS4 was reported to be associated with poor prognosis in gastric cancer and esophageal squamous cell carcinoma patients ,44. FurtSix key PDEEs mainly positively regulated TP63, H2AFX, FOS (TFs), EXT1, SLC23A2, GTF2IRD2B, NAIP (DETGs), PAI1, TFRC, NAPSINA, TTF1, CD49B (RPPA protein chips), P53 pathway, G2M checkpoint, fatty acid metabolism, protein secretion, MYC targets V1 , Mast cell resting, NK cell resting, Mast cell activated, Macrophage M2 (immune cell), Type_II_IFN_Reponse, and iDCs (immune-related gene sets).TP63 (tumor protein p63) was associated with the proliferation, migration, colony formation, and invasion of certain squamous cell carcinomas (SCCs) ,47. The EXT1 (exostosin glycosyltransferase 1) methylation can regulate gene expression and activate the WNT pathway, which affected the proliferation and migration of NSCLC and predicted a poor prognosis . SLC23A2PAI1 (phosphoribosylanthranilate isomerase 1) promoted glycolytic metabolism , and theThe transcription factor p53 participated in the mechanism of the cell cycle and was reported as an important tumor suppressor . The G2/Irinotecan is a topoisomerase inhibitor that causes cytotoxic protein-linked DNA breaks , which iFive key eRNAs  were found to be expressed in Alveolar, B, NK/T, Endothelial, Epithelial, Fibroblast and Myeloid cells in single cell sequencing analysis and the analysis of the scRNA-seq transcriptomes. The tumor-microenvironment was detected to play an important role in each step of NSCLC metastasis, and the synergistic mechanisms of tumor cells and the microenvironment may provide biomarkers or potential therapeutic targets for cancers , which sThe ATAC-seq validation showed that six key PDEEs were chromatin accessible in the NSCLC metastasis state, which indicated that these eRNAs were related to NSCLC metastasis. In addition, through Chip-seq validation analysis, these key PDEEs were found to bind to the binding sites of enhancers and DETFs in this study. Therefore, our study verified that these key eRNAs regulated target genes and mediated NSCLC metastasis by recruiting TFs, which may be implicated in the metastasis of NSCLC.This is the first study that explored the roles that eRNAs played in NSCLC metastasis, however, several limitations still remained in our study. Firstly, since the samples were all from America, selection bias was inevitable, and the applicability of the prediction model to other countries was uncertain. Secondly, our results were completely based on public databases and have not been verified by our own population studies and experiments. Therefore, multidimensional validation was performed to validate our hypothesis based on various online databases in the multi-omics dimension. Moreover, further cell and animal experimental validation and clinical trials were warranted in future studies, demonstrating correlations between identified key PDEEs and other multi-omics biomarkers, and validating the clinical relevance of our key findings regarding novel eRNA regulatory mechanisms from multiple dimensions.https://tcga-data.nci.nih.gov, accessed on 3 August 2020) [https://met500.path.med.umich.edu/, accessed on 4 August 2020) [http://cistrome.org, accessed on 14 August 2020) [https://www.immport.org/, accessed on 19 February 2020). Fifty cancer-related hallmark pathways and 29 immune-related pathways were obtained from the Molecular Signatures Database   [RNA-seq data from 1011 primary NSCLC, clinical information from 829 primary NSCLC, and 258 protein chips were obtained from The Cancer Genome Atlas (TCGA) database (st 2020) . RNA-seqst 2020) . Immune er 2020) .https://hanlab.uth.edu/eRic/, accessed on 6 June 2020) [The list of eRNAs, which was normalized and annotated by Ensemble ID and the corresponding target gene list was available in the eRic (enhancer RNA in cancers) database (ne 2020)  in additne 2020) . In addine 2020) .p-value < 0.05 and |log2 Fold Change (FC)| > 1.0; DEEs, DETGs, and DETFs were all identified. To determine the most enriched pathways, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Oncology (GO) enrichment analysis was applied [Differentially expressed genes (DEGs) between primary NSCLC and metastatic NSCLC were identified by the limma and edgeR algorithm ,88 with  applied .Prognostic DEEs (PDEEs) were screened by univariate Cox regression analysis from identified DEEs. Furthermore, the Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to determine the independent variables with great significance, reducing the over-fitting phenomenon . AfterwaIn this formula, “n” represented the number of PDEEs in the multivariate model; “β” represented the coefficient of corresponding PDEEs. Then, the median of the RS was used to divide all the NSCLC samples into two groups, including the high-risk group and the low-risk group. Additionally, the survival conditions of the high-risk group and the low-risk group were described by Kaplan–Meier survival analysis. The independent prognostic value of RS was evaluated by univariate and multivariate Cox regression analyses, and the multivariate Cox regression model was adjusted by clinical variables, such as age, gender, and stage.The composition of 22 types of immune cells in all samples was evaluated by the CIBERSORT algorithm. To determine the correlations between PDEE signature and immune cell infiltration in NSCLC tissues, PDEE expression matrix data were uploaded to CIBERSORT . Infiltrp-value < 0.05 was considered statistically significant.Additionally, a single-sample gene set enrichment analysis (ssGSEA) was carried out to quantify 29 immune-related gene sets in all samples . FurtherGene Set Variation Analysis (GSVA) was utilized to explore potential downstream hallmark pathways of PDEEs. Absolute quantification of 50 hallmark pathways was calculated to determine the differentially expressed hallmark pathways between primary NSCLC samples and metastatic NSCLC samples using the GSVA package .p value < 0.05 for PDEEs and DETGs; |correlation coefficient| > 0.20 and p value < 0.05 for PDEEs and DETFs; |correlation coefficient| > 0.25 and p value < 0.05 for PDEEs and RPPA protein chips; |correlation coefficient| > 0.25 and p value < 0.05 for PDEEs and hallmark pathways; |correlation coefficient| > 0.20 and p value < 0.05 for PDEEs and immune-related gene sets, and |correlation coefficient| > 0.15 and p value < 0.05 for PDEEs and immune cells.Key PDEEs along with DETGs, DETFs, RPPA proteins chips, 50 hallmark pathways, 29 immune gene sets, 22 immune cells, were integrated into Pearson correlation analysis to construct a NSCLC metastasis-specific eRNA regulation network with the criterion of |correlation coefficient| > 0.20 and https://portals.broadinstitute.org/cmap/, accessed on 25 September 2020) [http://clue.io/, accessed on 25 September 2020) including human cell lines’ transcriptional responses to perturbagens, structural formulas, and protein targets. Therefore, based on MoA, inhibitors that may target NSCLC metastasis-related DEGs in this study were all identified.Furthermore, the connectivity map (Cmap) database was utilized to identify bioactive small molecule inhibitors with the potential as target drugs for DEGs of the NSCLC metastasis-specific regulation network (er 2020) ,92. DEGser 2020) . Informahttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE153935, accessed on 8 June 2022) were applied to analyze the cellular localization of key PDEEs. The analysis of the integrated data was carried out by the R toolkit Seurat [https://github.com/garthtarr/edgebundleR, accessed on 8 June 2022).scRNA-seq data from 24 NSCLC samples obtained from the GEO database  [http://cistrome.org/, accessed on 30 August 2021) [For balancing false positive results and reducing information bias, multi-database external verification was performed based on various online databases including Gene Expression Profiling Interactive Analysis (GEPIA) , UCSC xest 2021) . The assst 2021) . Chromatst 2021) ,116,117.p-value < 0.05 was considered statistically significant, and the utilization of the Pearson/Spearman correlation coefficient depended on the results of the normality tests. The machine diagram was drawn on the biorender website .All statistical analyses were performed by R version 3.6.1  . A two-sided This study verified that eRNAs played significant roles in NSCLC metastasis. Moreover, six key PDEEs  were identified as potential markers to predict the prognosis of NSCLC and provide references for the treatment of metastatic NSCLC, which occupied the central position in the NSCLC metastasis-specific regulation network.


Staphylococcus aureus (MRSA), a leading cause of chronic infections, forms prolific biofilms which afford an escape route from antibiotic treatment and host immunity. However, MRSA clones are genetically diverse, and mechanisms underlying biofilm formation remain under-studied. Such studies form the basis for developing targeted therapeutics. Here, we studied the temporal changes in the biofilm transcriptome of three pandemic MRSA clones: USA300, HEMRSA-15, and ST239.Methicillin-resistant in silico co-expression network construction and compared between clones. To understand the influence of SCCmec and ACME on biofilm formation, isogenic mutants containing deletions of the entire elements or of single genes therein were constructed in USA300.Biofilm formation was assessed using a static model with one representative strain per clone. Total RNA was extracted from biofilm and planktonic cultures after 24, 48, and 72 h of growth, followed by rRNA depletion and sequencing . Differentially expressed gene (DEG) analysis between phenotypes and among early (24 h), intermediate (48 h), and late (72 h) stages of biofilms was performed together with n = 11) was remarkably lower than that in USA300/EMRSA-15 biofilms . The clfA gene, which encodes clumping factor A, was the single common DEG identified across the three clones in 24-h biofilm culture (2.2- to 2.66-fold). In intermediate (48 h) and late (72 h) stages of biofilms, decreased expression of central metabolic and fermentative pathways , indicating a shift to anaerobic conditions, was already evident in USA300 and HEMRSA-15 in 48-h biofilm cultures; ST239 showed a similar profile at 72 h. Last, SCCmec+ACME deletion and opp3D disruption negatively affected USA300 biofilm formation.Genes involved in primarily core genome-encoded KEGG pathways (transporters and others) were upregulated in 24-h biofilm culture compared to 24-h planktonic culture. However, the number of affected pathways in the ST239 24 h biofilm (clfA gene was the only significantly upregulated gene across all three strains in 24-h biofilm cultures and exemplifies an important target to disrupt early biofilms. Furthermore, our data indicate a critical role for arginine catabolism pathways in early biofilm formation.Our data show striking differences in gene expression during biofilm formation by three of the most important pandemic MRSA clones, USA300, HEMRSA-15, and ST239. The  Staphylococcus aureus, a leading cause of nosocomial and community-acquired infections  is a multidrug resistant variant of S. aureus  element via horizontal gene transfer , belonging to pandemic clonal lineages USA300, HEMRSA-15, and the Hungarian/Brazilian ST239 clone, respectively, were utilized in this study . These smec type IV, previously identified as a prolific biofilm former; (ii) HEMRSA-15  belonging to ST22, harboring SCCmec type IV; and (iii) ST239-16  belonging to ST239, harboring SCCmec type III. These strains have been shown previously by us to have strikingly different biofilm phenotypes in terms of biomass or cell-to-matrix ratios  UAS391  belonging to ST8, harboring SCCx ratios .in silico co-expression networks were constructed to observe inter- and intrastrain patterns.First, we studied growth rates of each strain to understand if these differences might be due to differences in replication rates. Next, biofilm formation was assessed in a static biofilm model with a high-nutrient environment, followed by quantification after 24 h. Subsequently, these strains were allowed to form static biofilms for 24, 48, and 72 h with planktonic cultures generated in parallel. Samples for total RNA extraction were obtained at the given time points for both phenotypes and used for transcriptomic analyses. Genes found to be differentially expressed (at least 2-fold) between (i) the same strain’s biofilm and planktonic phenotypes at the same time point, or (ii) the same strain’s consecutive biofilm time points were studied further using metabolic pathway analysis. In parallel, mec influences MRSA biofilm formation  for distribution by BEI Resources (Nebraska Transposon Mutant Library (NTML),1All strains were grown either on 5% horse blood Columbia agar plates  or in brain heart infusion  broth supplemented with 0.1% D(+)-glucose monohydrate  as previously described . Bursa a®, Greiner Bio-One, Kremsmünster, Austria) in triplicate to measure biofilm biomass using the crystal violet assay as described previously . A single colony was utilized to start an overnight broth and agar subculture from the same plate. The overnight broth culture was utilized to start and collect, in the exponential growth phase, 0.05 McF of each strain that was inoculated in a 136-mm polystyrene Petri dish , and grown under static conditions for 24, 48, or 72 h before rinsing  and scraping off adherent bacteria. Simultaneously, one colony from the blood agar subcultures was re-suspended in medium and incubated for 24, 48, or 72 h with shaking  for each strain. The growth medium was refreshed every 24 h by spinning down the culture, removing the supernatant, and adding the fresh medium. After incubation, bacteria were collected by centrifugation at 4,600 × g for 20 min. Subsequently, approximately 0.200 g of both biofilm and planktonic bacterial suspensions were placed in RNAprotect™ Bacteria Reagent  and incubated for 5 min at room temperature to stabilize the RNA. Bacteria were then harvested by centrifugation at 5,000 × g for 10 min at 4°C. Then two biological repeats were analyzed for each strain, phenotype, and time point.®, Biotechnologies, Madison, WI, United States) according to the manufacturer’s protocol with modifications. Briefly, cells were mechanically disrupted using Lysing Matrix B 0.1 mm silica sphere bead beating  before extraction. Each RNA sample was suspended in 30 μL of RNA storage solution, and the quality was determined using an Agilent 2100 Bioanalyzer . Ribosomal RNA (rRNA) was depleted using a Ribo depletion kit , followed by preparation of a stranded TruSeq library and 1 × 75 bp sequencing using the NextSeq 500 system, v2 .Total RNA was purified from harvested bacteria using the Masterpure™ Complete DNA and RNA Purification kit  and then used for alignment by BBMap (v.38.47) against their reference genomes with default parameters to obtain transcripts per million (TPM) values  , and Fas) values . For UAS) values , and ann) values . DEGs   were used to study temporal changes in gene (co-)expression by generating a network based on Spearman correlations with (v0.9.2) , and the(v0.9.2) . In tota(v0.9.2) .mec or single elements were cured by electroporation  of pSR2, a thermosensitive plasmid containing the ccrAB2 gene complex and a tetracycline resistance marker, isolated from Escherichia coli SF8300 , using the method described by S. aureus cloning host RN4220 to adapt the plasmid DNA to S. aureus modifications. RN4220 and UAS391 transformants were grown for three consecutive days at 30°C in BHI broth supplemented with tetracycline. Single colonies were picked and grown for 24 h in the BHI broth at 42°C to promote loss of pSR2. The deletion of ACME, SCCmec, or ACME and SCCmec was confirmed by PCR using X1-X5, X2-X3, and X1-X3 PCR primers  ≥ 2 were extracted, and their p-values were adjusted by using the false discovery rate (FDR) approach (padj). Finally, genes with padj < 0.05 and FC ≥ 2 in each pairwise comparison were considered as DEGs. One-way ANOVA was conducted in R (v3.6) to evaluate the differences in biomass and growth rates at the three time points between the three tested strains. Transcriptomic profiles across both phenotypes and time points were compared using the permutational multivariate analysis of variance (PERMANOVA) using the Adonis function with 999 permutations in the vegan package3 in R.For RNASeq data, DESeq2 (v.1.14.1) was used to determine significantly differently expressed genes (DEGs) in each pairwise comparison based on TPM values of every gene . Specifip ≤ 0.03) than UAS391  and ST239-16  showed a lower growth rate than UAS391  and ST239-16 (OD492 = 1.366) showed higher biomass after 24 h of growth  identified between the biofilm and planktonic phenotypes for UAS391, HEMRSA-15, and ST239-16, respectively  did not show significant differences between the biofilm and planktonic phenotypes in any of the three strains  was remarkably lower than that in UAS391 (n = 27) and HEMRSA-15 (n = 58). Interestingly, the majority of the affected KEGG pathways were not linked to the accessory genome, indicating that expression of core genome pathways differed substantially in 24-h biofilms between the studied strains (All identified DEGs between the biofilm and planktonic phenotypes after 24 h of growth were mapped against the KEGG database with 56 (46.67%), 107 (44.96%), and 13 (30.23%) DEGs assigned KO terms for UAS391, HEMRSA-15, and ST239-16, respectively . When coectively . UAS391  strains .Staphylococcus aureus infection” pathway, the only DEG that was commonly upregulated in all three strains in 24-h biofilms was clfA , staphylokinase (sak), and a complement inhibitor SCIN family protein (scn) were downregulated in 24-h biofilms of UAS391  and HEMRSA-15  compared to planktonic bacteria (lysM) and gamma hemolysin subunit B (hlgB) showed downregulation in HEMRSA-15 , but virulence factors chs, sak, and hlgB were absent in the ST239-16 genome.Of note, in the “bacteria . AdditioarcA, arcB, arcC2_1, arcD, and arcR) was upregulated at least 2- and 3-fold, respectively, compared to the planktonic phenotype  in UAS391 and HEMRSA-15 24-h biofilms. In line with these results, transposon inactivation of arcA in USA300-JE2 proved to have a significant impact on produced biofilm, compared to the wild type  cycles on USA300 biofilm formation , argininhenotype . Among tic cells .argBCJ) coupled with the urea cycle  (ureABCDEFG), which catabolizes urea formed by arginase (rocF) in the urea cycle. In UAS391 24-h biofilms, all urease-related genes showed FCs ranging from 1.99 to 2.37 , did not show significant differential expression in 24-h biofilms of the three strains (p > 0.05).Arginine can be synthesized in the arginine biosynthesis pathway  . ExpressH, rocF) . On the  to 2.37 . Another+-specific transport system (kdpABC and kdpDE) also contributes to pH homeostasis through the exchange of K+ for H+ , and in HEMRSA-15, both kdpABC and kdpDE were upregulated . By contrast, in the ST239-16 24 h biofilm, this was not observed , 563 (HEMRSA-15), and 422 (ST239-16)] and 72-h biofilm to 48-h biofilm [3 (UAS391), 198 (HEMRSA-15), and 406 (ST239-16), However, as with expression patterns in 24- and 48-h biofilms compared to the planktonic phenotype, ST239-16 showed striking differences during biofilm maturation compared to the other strains . In the The ADI pathway, showing significant upregulation in 24-h biofilms of UAS391, ST239-16, and HEMRSA-15 compared to planktonic cells, did not show differential expression in biofilms of 48 h and 72 h for both strains . HoweverpyrP) in 48-h UAS391 biofilms, compared to the 24-h time point, with FC ranging from −1.71 to −3.61  and pyrF and pyrR  from the expected profiles , as compared to the wild type . However, lower expression of mecA and mecR1 components was observed in later biofilm time points (48 and 72 h) compared to 24 h in UAS391 and HEMRSA-15. The deletion of SCCmec, as with ACME, resulted in a small biomass decrease of 18% .Arginine catabolic mobile element is closely associated with methicillin resistance encoding SCCUnderstanding the ability of pandemic MRSA clones causing human infections to form biofilms and the mechanisms underlying biofilm formation is the first step toward a possible solution for biofilm-related infections. However, our temporal biofilm transcriptome analysis of three major MRSA clones causing infections, USA300, HEMRSA-15, and ST239-16, showed remarkable divergence in gene expression patterns and in metabolic pathways involved in biofilm formation. Although this study only tested a single strain per clonal type, the fact that temporal changes in gene expression during biofilm formation were primarily observed in core genome-encoded pathways supports our hypothesis that these data could be broadly representative of the three major clones studied here.Staphylococcus aureus infection,” “Transporters,” “Transcription,” and “Peptidases.” However, the only gene that was commonly upregulated across all three biofilms was clfA, which encodes clumping factor A (ClfA), a cell wall-anchored protein and a virulence factor that promotes bacterial adhesion to the blood plasma protein fibrinogen even under high shear stress and facilitates colonization by S. aureus of blood protein-coated biomaterials  biofilms primarily showed pathways encoded in the core genome to be affected in all three clones, and these included KEGG pathways “aterials . Interes. aureus . Prior s results . For insmaterial . ClfA isS. aureus infections, the expression of related virulence factors was compared. Staphylokinase has been shown to disrupt biofilm formation in both S. aureus and polymicrobial biofilms , which catabolizes urea produced in the urea cycle to ammonia and CO2. As observed in the pyrimidine biosynthesis pathway, distinct differential expression was observed in UAS391, but not in HEMRSA-15 and ST239-16. UAS391 showed high upregulation of the urease genes in 24-h biofilms, countered by a subsequent downregulation in biofilms, indicating its importance in early biofilm formation of this clone. Bacterial ureases have been described as a potential key factor in the persistence of certain pathogens, with the release of ammonia being the major cause of host tissue damage , apparently contributing to pH homeostasis by cation transport (kdpABC) 24-h biofilms. Similar to the urease pathway, this early trend was not observed in biofilms of both strains, while ST239-16 only showed upregulation of kdpC in 48-h biofilms. The differences in gene expression between biofilm and planktonic cells are determined by distinct phenotypes but also might be partly attributed to differences in micro-environments  between biofilm and planktonic cell cultures.Our data also showed upregulation of the high-affinity Kransport . This opmec mobile elements led to a significant decrease in biomass formation compared to deletion mutants of single elements, suggesting a link between the two elements which are harbored as a composite element in the USA300 clone. mec from clinical MRSA isolates but observed heterogeneity among isolates with different genetic backgrounds . Expression data of the SCCmec genes did not show significant differences between biofilm and planktonic phenotypes. Single transposon interruptions of ACME-related genes showed no influence of the ADI genes. However, this could be explained by the fact that this is an additional copy of the ADI genes in strains belonging to USA300, and loss could be compensated by the putative ADI -cluster. On the other hand, disruption of opp3D, an ABC transporter ATP-binding protein harbored on the ACME, had a significant negative impact on biofilm formation by USA300-JE2.Last, we showed that a deletion of both ACME and SCCclfA gene was the only significantly upregulated gene across all three strains in 24-h biofilms and exemplifies an important target to disrupt early biofilms. The fact that neutralizing human monoclonal antibodies against ClfA were found to inhibit biofilm formation in vitro and the hematogenous implant-related infection in vivo  than UAS391 (5.12%) or HEMRSA-15 (7.75%), the majority of affected pathways in early, 24-h biofilms involved the core genome. The  in vivo  strengthhttps://www.ncbi.nlm.nih.gov/, PRJNA769537.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: HD, J-PH, HG, and SM-K: conceptualization. JV, QL, BX, and SDB: investigation. JV and QL: visualization. JV, QL, SDB, and SM-K: writing—original draft preparation. JV, QL, BX, MB, HD, J-PH, SK-S, HG, and SM-K: writing—review and editing. All authors read, gave input, and approved the final manuscript.


P < 0.05). Protein–protein interaction analysis revealed that COL4A2, COL1A2, COL4A1, COL5A2, COL12A1, ELN, ALB, and MMP10 might be key candidate genes for follicular development in chicken. The current study identified dominant genes and pathways contributing to our understanding of chicken follicular development. During follicular development, a series of key events such as follicular recruitment and selection are crucially governed by strict complex regulation. However, its molecular mechanisms remain obscure. To identify the dominant genes controlling chicken follicular development, the small white follicle (SWF), the small yellow follicle (SYF), and the large yellow follicle (LYF) in different laying stages  were collected for RNA sequencing and bioinformatics analysis. There were 1866, 1211, and 1515 differentially expressed genes (DEGs) between SWF and SYF in W22, W31, and W51, respectively. 4021, 2295, and 2902 DEGs were respectively identified between SYF and LYF in W22, W31, and W51. 5618, 4016, and 4809 DEGs were respectively identified between SWF and LYF in W22, W31, and W51. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that extracellular matrix, extracellular region, extracellular region part, ECM-receptor interaction, collagen extracellular matrix, and collagen trimer were significantly enriched (The online version contains supplementary material available at 10.1186/s12864-022-08728-w. In poultry breeds, high-efficiency follicular development means huge economic output for the egg industry. Follicles at different stages exist in the ovary of sexually mature hens, and a hierarchical system is formed in the ovary according to different functions and sizes: Pre-hierarchical follicle and hierarchical follicle  . Once ovOver the past decade, RNA sequencing (RNA-seq) has become an indispensable tool for transcriptome analysis. RNA-seq is often used for analyzing differential expression genes . RNA-seqThe Nandan-Yao domestic chicken is a native breed in China. It has the characteristics of coarse food resistance, strong foraging ability, delicate and delicious meat, but its performance in egg production is low. In this study, RNA-seq and bioinformatics analysis were performed to identify the differentially expressed genes and pathways between different follicles in different laying stages to reveal the molecular mechanisms of follicular development.All experimental and sample collection procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of the College of Animal Science and Technology of Guangxi University , with approval number GXU2018-058.Gallus gallus) purchased from Guangxi Guigang Gangfeng Agriculture and Husbandry Co. Ltd, laying continuously 3 eggs, were used in this study. Hens in early laying , peak laying , and late laying  were selected for ovarian follicle sampling (n = 4). Follicles within the ovary were classified as small white follicles , large white follicles , small yellow follicles , and large yellow follicles  [Nandan-Yao domestic hens (ctively) , 20. SWFThe total RNA was extracted from SWF, SYF, and LYF using TRIzol reagent  according to the manufacturer's instructions. RNA integrity was monitored on 1% agarose gels. RNA concentration was checked using the UV–Vis Spectrophotometer Q5000 .The cDNA libraries were constructed and sequenced following the manufacturer’s standard procedures on an Illumina HiSeq 2500  in Novogene Bioinformatics Technology Co., Ltd., Beijing, China. Raw reads of FASTQ format were processed with trim galore . To obtahttp://ftp.ensembl.org/pub/release-102/gtf/gallus_gallus/, http://ftp.ensembl.org/pub/release-102/fasta/gallus_gallus/dna/). The clean reads were mapped to the chicken reference genome using Hisat2v2.1.0 [P-value < 0.05 and |foldchange|> 2 were used as the criteria of significance. GO term and KEGG pathway analyses of coding genes were performed by the R package clusterProfiler 3.14.3 [P-adjust < 0.05 were considered to be significantly enriched. The STRING  database was used to explore the interaction between DEGs. A confidence score > 0.9 was defined as valid.Reference genome and gene model annotation files were downloaded from the genome website . Primer sequences of target and reference genes were shown in Supplemental table As shown in Supplementary table 2, 18,911,563 to 34,680,085 clean reads per sample were obtained after quality control. The average GC content of all samples was 52.54%. The average mapped rate was 92.38% comparing clean reads with the reference genome. For all samples, at least 96.75% of the reads were equal to or exceeded Q20.At W22, 1866, 4021, and 5618 DEGs were respectively identified between SWF and SYF, SYF and LYF, SWF and LYF  related to follicular development for QRT-PCR analysis , COL1A2 , COL4A1 , COL5A2 , COL12A1  are five kinds of collagen. Type IV collagen is the main component of the basement membrane and constitutes its skeleton. It not only maintains the integrity of the basement membrane but also plays a key role in its formation. In normal conditions, the basement membrane is stable, dense, and continuous and can prevent macromolecules and cells from passing through [ELN gene encodes elastin. Fibrillin microfibrils are widely distributed components of extracellular matrices that function in the formation of elastin, serve structural roles and provide substrates for cell adhesion [ALB may be a requirement for the control of follicle growth, which is attributable to albumin binding to specific cell-membrane components followed by the intracellular uptake of Alb-bound substances [MMP10  gene belongs to the matrix metallopeptidase family. A growing body of evidence suggests that MMPs play a relevant role in the ECM remodeling of ovarian tissues [Protein network interaction analyses of DEGs in W51 identified several genes associated with follicle development including  through . The ELNadhesion . Albuminbstances . The MMP tissues –53. Many tissues . It indiCOL4A2, COL1A2, COL4A1, COL5A2, COL12A1, ELN, FBN2, ALB, MMP10) may regulate egg production by taking part in the extracellular matrix, extracellular region, extracellular region part, extracellular space, ECM-receptor interaction, collagen containing extracellular matrix and collagen trimer. The study constructed the transcriptional profiles of chicken growing follicles in different laying stages laying a foundation for further research on follicular development.The current study identified a series of key genes and signaling pathways associated with chicken follicular development by RNA-seq and bioinformatics analysis. These key genes (Additional file 1:Supplementary Table 1. qPCR Primer sequences.Additional file 2:Supplementary Table 2. Summary of sequencing reads mapping to the reference genome and quality parameters.Additional file 3:Supplementary Table 3. Information of all enriched GO Terms based on DEGs of W51 intersection. Supplementary Table 4. Information of all enriched KEGG pathway based on DEGs in W51 intersection. Supplementary Table 5. Information of all enriched GO Terms based on DEGs in SWF intersection. Supplementary Table 6. Information of all enriched KEGG pathway based on DEGs in SWF intersection


PER) and cryptochrome (CRY) families play critical roles in circadian rhythms. The imbalance of circadian factors may lead to the occurrence of cancer. Expressions of PER and CRY family members decrease in various cancers. Nevertheless, expression levels, genetic variations, and molecular mechanisms of PER and CRY family members in lung adenocarcinoma (LUAD) and their correlations with prognoses and immune infiltration in LUAD patients are still unclear. In this study, to identify their biological functions in LUAD development, comprehensive high-throughput techniques were applied to analyze the relationships of expressions of PER and CRY family members with genetic variations, molecular mechanisms, and immune infiltration. The present results showed that transcription levels of PER1 and CRY2 in LUAD were significantly downregulated. High expression levels of PER2, PER3, CRY1, and CRY2 indicated longer overall survival. Some cancer signaling pathways were related to PER and CRY family members, such as cell-cycle, histidine metabolism, and progesterone-mediated oocyte maturation pathways. Expressions of PER and CRY family members significantly affected the infiltration of different immune cells. In conclusion, our findings may help better understand the molecular basis of LUAD, and provide new perspectives of PER and CRY family members as novel biomarkers for LUAD.The period ( PER and CRY [According to a report provided by cancer statistics 2022, the most commonly diagnosed cancer is lung cancer, which is also the leading cause of cancer deaths in the world . Lung ca and CRY , which a and CRY . Afterwa and CRY –19. The PER1, PER2, and PER3 are obviously methylated in breast cancer patients [PER expression in colorectal cancer cells is also significantly lower than that of normal colorectal mucosal cells [PER and CRY are important negative regulators of circadian rhythms . Studiespatients . PER expal cells . Recent al cells . CLOCK/Bal cells . In addial cells . DespitePER and CRY family members and related molecules in tumorigenesis are still unclear. Multiple microarray and sequencing technologies have enhanced the ability of robust computational algorithms to rapidly analyze biomedical data [PER and CRY in lung cancer development. In this study, different bioinformatics databases were incorporated to analyze various PER and CRY family members in LUAD to understand expressions of these factors, molecular functions such as proliferation or tumorigenesis, and their impacts on OS, genetic changes, immune infiltration, and immune checkpoints in LUAD, which would help identify whether PER and CRY are suitable biomarkers for precision treatment and detection of LUAD.Few previous studies reported the roles of PER and CRY in lung cancer. In particular, interactions and pathways between all cal data –34. ExamPER and CRY family members, BMAL1 (ARNTL), and CLOCK, with differential expression levels between different types of cancers and normal tissues , CRY family (CRY1 and CRY2), BMAL1, and CLOCK in different types of cancer , -1.717- (p = 1.38E-7), -2.125- (p = 8.29E-19), and -2.148-fold changes (p = 1.77E-8), respectively. In the Su Lung dataset [PER1, PER2, CRY1, and CRY2 were -1.861- (p = 6.23E-7), -1.803- (p = 1.29E-7), -2.058- (p = 1.39E-6), and -3.450-fold lower (p = 2.47E-5) in LUAD than in normal samples. In the Hou Lung [PER3, CRY1, and CRY2 were significantly lower than in normal tissues in LUAD patients with respective fold changes of -2.024 (p = 1.05E-9), -1.702 (p = 5.10E-13), and -1.836 (p = 9.60E-13). In GEPIA2, PER1 and CRY2 were also found to have higher expressions in normal lung tissues than in LUAD tissues , followed by CRY2 (7%), and mutation rates were lowest in PER1 and PER2 (6%) . Others showed coexpression and mutual exclusion without statistical significance (p>0.05) possibly due to insufficient sample sizes  of LUAD patients had genetic changes in  factors . TCGA daER2 (6%) . In addile sizes .CLOCK and ARNTL  are positive mediators of circadian rhythms and mediate CRY and PER transcription [PER1, PER2, PER3, CRY1, and CRY2, and analyze the functions that may be related to networks composed of PER and CRY family members. They were all surrounded by 20 nodes, representing genes that may have physical interactions, coexpressions, predictions, co-localizations, pathways, gene interactions, and shared protein domains with PER and CRY family members , CSNK1E (casein kinase 1 epsilon), CSNK1D (casein kinase 1 delta), TIMELESS (timeless circadian regulator), ARNTL, and CLOCK. In the CRY family network , PER2, TIMELESS, PER3, ARNTL, CLOCK, and PPP5C .From previous studies, we know that the functions of these genes are often related to regulating circadian rhythms. For instance, cription . GeneMAN members . In the  network , the mos network , the mosPER1-, PER2-, PER3-, CRY1-, and CRY2-related genes  diseases of ed genes . The fires  T cells. PER3 expression was positively related to the infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells (DCs). CRY1 expression was positively related to the infiltration of macrophages and neutrophils. CRY2 expression was positively related to the infiltration of B cells, CD4+ T cells, macrophages, and DCs. These results demonstrated that the circadian-related factors of the PER and CRY families were related to immune infiltration in LUAD patients.In the current research, some relationships between the immune system and circadian rhythms were discovered. For example, it is believed that ammasome . It is,  in LUAD . The anaPER1 affected the infiltration level of B cells, CD4+ T cells, macrophage, neutrophil, and DCs; CNAs of PER2 affected the infiltration levels of B cells, CD4+ T cells, macrophage, and neutrophils; CNAs of PER3 affected the infiltration levels of B cells, CD8+ T cells, CD4+ T cells, and macrophages; CNAs of CRY1 affected the infiltration levels of B cells, CD4+ T cells, and macrophages; and CNAs of CRY2 affected the infiltration levels of B cells and CD4+ T cells. These results demonstrated that genetic alterations of the PER and CRY families in LUAD led to changes in immune infiltration levels.In addition, somatic copy number alterations (CNAs) of circadian factors were significantly associated with infiltration levels . Among tPER1 was correlated with CD274  and PDCD1LG2  , CTLA4 , PDCD1 , and PDCD1LG2   , CTLA4 , and PDCD1  , CTLA4 , PDCD1 , and PDCD1LG2   ; the exp.83e-16) ; the exp 0.0203) ; the exp0.00164) ; and the.22e-07) . Finally.22e-07) . Express.22e-07) . However.22e-07) .The physiological behavior of animals often exhibits periodic changes to adapt to repeated environmental changes. The most typical one is the sleep-wake cycle, and others include neurological, metabolic, endocrine, cardiovascular, and immune functions . There aPER and CRY family members were found to be basic factors in modulating circadian rhythms [ARNTL, CLOCK, RORA, RORB, CRY1, CRY2, and PER3 genes were associated with a higher risk of lung cancer [PER and CRY families have the same effect on lung cancer development [Previous studies showed that an imbalance of circadian rhythm-related factors may lead to the occurrence of cancer. At present, circadian rhythm-related factors have been discovered, and the basic operation mode of circadian rhythms has been established.  rhythms , and theg cancer . Moreoveelopment .PER and CRY transcription levels, genetic variations, molecular functions, diseases, and their relationships with prognoses and immune infiltration in LUAD patients. We conducted an Oncomine database analysis and found that compared to normal tissues, expressions of circadian rhythm-related factors of PER1, PER2, PER3, CRY1, and CRY2 in LUAD tissues were relatively low. Consistently, expressions of PER1 and CRY2 were found in a GEPIA2 analysis to be lower than in normal tissues. Moreover, the KM analysis showed that high expression levels of PER2, PER3, CRY1, and CRY2 in LUAD patients were related to a better OS.Thus, this is the first study to use bioinformatics to analyze and discuss different PER and CRY family members were found to have higher mutation rates (29.42%) in LUAD patients. Mutually exclusive coexpressions were found between differentially expressed PER and CRY family members (mainly coexpression), which meant that LUAD might be induced by the co-inhibition of these gene family members. Furthermore, a molecular mechanism pathway analysis demonstrated that the functions of PER- and CRY-related genes were mainly involved protein-binding, cell-cycle, histidine-metabolism, and progesterone-mediated oocyte-maturation pathways. In particular, progesterone-mediated oocyte-maturation pathways and the cell cycle were previously demonstrated to be correlated with LUAD [FAM83A in LUAD [PER and CRY family members in LUAD have the potential to become crucial genes for targeted therapy.In addition, ith LUAD . Histidi in LUAD . TherefoPER and CRY family members and the functions associated with those genes. We found that there were many signaling pathways involved in immune evasion, cancer migration and proliferation, and other functions. Among them, PER1 was related to the Immune response_IL-6 signaling pathway via JAK/STAT. Interleukin (IL)-6 can be found in all human inflammatory diseases and cancers due to its dysregulation and overexpression [PER2 included a G-protein signaling RhoA regulatory pathway, cell adhesion tight junctions, and development positive regulation of WNT/Beta-catenin signaling in the cytoplasm, which are all related to the function of cell migration [PER2 may allow cancer cells to easily migrate and spread. Signaling pathways associated with PER3 were more related to gastrin, including the development of gastrin in cell growth and proliferation, and immune response of gastrin in the inflammatory response. Gastrin is currently considered to be related to cancer development, proliferation, and anti-apoptosis in addition to digestion-related functions [CRY1-related pathway was the WNT signaling pathway. The canonical WNT signaling pathway is associated with cell migration, and the WNT5A non-canonical signaling pathway was also found to be associated with a variety of human cancers [CRY2 was mainly related to the cytoskeletal remodeling regulation signaling pathway. The abnormality of cytoskeletal remodeling was related to the invasion and metastasis of cancer cells in previous research [PER and CRY family members and provide insights into why dysregulation of circadian rhythms may contribute to cancer development.We also analyzed MetaCore and found genes that are coexpressed with pression . It is apression . The topigration –56. Therunctions . The CRY cancers . Finallyresearch . These fCRY1 and BMAL1 core-clock genes were correlated with clinical parameters in epithelial ovarian cancer [PER and CRY family members and related circadian clock genes interfere with melatonin secretion and circadian rhythms, which have effects on pathogeneses of malignancy.Previous studies demonstrated that deregulation of circadian clock genes was indicated in the development of cancers. Melatonin can resynchronize rhythmic patterns of gene expressions, correcting defects in various circadian rhythm oncogenes. Melatonin also inhibits myeloperoxidase catalytic activity , which in cancer , 64. Meln cancer , and hasn cancer . In addin cancer . In the n cancer . These fPER and CRY family members. In LUAD, expressions of PER1 and PER2 were positively correlated with the immune infiltration of cluster of differentiation 4-positive (CD4+) T and natural killer (NK) cells. CRY1 expression was positively correlated with the infiltration of NK cells, macrophages, and neutrophils, and CRY2 was correlated with the infiltration of NK cells, B cells, CD4+ T cells, macrophages, and DCs. PER3 was connected to the immune infiltration of NK cells, B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and DCs. Additionally, it was found that circadian rhythm factors were negatively correlated with the expressions of immune checkpoint-related genes. Further analysis of TISMO found that circadian rhythm factors had no significant relationships with immune checkpoint blockades, but were more related to stimulation of cytokines. These results suggest that circadian rhythm factors may contribute to increases in levels of partial immune infiltration by downregulating expressions of immune checkpoint genes [Circadian rhythms can directly interact with components of the immune system, thereby affecting aspects of the immune system such as inflammation. Recent studies also indicated that phagocytosis, migration of inflammatory or infected tissues, cytolytic activity, and proliferative responses to antigens are closely related to circadian rhythms . Furthernt genes –73.PER1 and CRY2 were correlated with the expression of CD4+ T cells, and the expression of PD-1 exhibited a robust circadian rhythm in normal lung tissues [Our results correspond to other research in immunotherapy . PER1 an tissues , which sPER1 and CRY2 are significantly downregulated in LUAD. Except for PER1, high expressions of PER2, PER3, CRY1, and CRY2 lead to better OS in LUAD patients. In the functional analysis of these circadian coexpressed genes, many factors related to cancer development were also found. In addition, PER1, PER2, PER3, CRY1, and CRY2 were related to six different immune cells to varying degrees, which may be related to the downregulation of different immune checkpoints. Given the above results, these circadian rhythm factors may be involved in tumor immunity of LUAD. PER and CRY family members could be novel and promising prognostic biomarkers of LUAD.Taken together, the results indicated that PER and CRY family members and their influences. The present study may help us better understand the molecular functions of circadian rhythm-related factors in LUAD and may provide possible molecular targets for LUAD in chemotherapy and immunotherapy.In summary, we used several high-throughput bioinformatics databases to analyze and investigate gene expressions of http://ualcan.path.uab.edu/) is an online tool for analyzing cancer OMICS data and was used to analyze the relationship between gene expressions and various cancers. Its functions include (1) analysis of the relative expressions of genes in tumor and normal samples, (2) analysis of the effects of gene expressions on survival rates of different cancer patients, and (3) analysis of high and low expressions of genes in different cancer samples. These helped us understand expression levels of circadian factors in different cancer types [UALCAN (er types .https://genemania.org/), an online server for prediction, is used to prioritize genes and predict gene function biological networks [PER and CRY families and other genes, and to establish a network.GeneMANIA . This platform was established with multiple human cancer cell lines (n = 1457) and plenty of unique datasets  [Cancer cell lines are the most relevant approach in cancer biology research to verify targets and determine drug efficacies  . Of inte136,488) –82.http://gepia2.cancer-pku.cn/#index), an upgraded version of GEPIA, is a web-based data platform that can be used to compare tumor tissues and normal tissues, and provides 60,498 genes and 198,619 isoforms for querying. Like the older version of GEPIA, functions include differential expression analyses, spectrogram drawing, correlation analyses, patient survival analyses, similar gene detections, and dimensionality reduction analyses. In addition, some of the original older functions have been upgraded, and there are also new functions such as survival maps, isoform use profiling, uploaded expression data comparisons, and cancer-subtype classifiers [GEPIA2 , an online database with gene expression and clinical data, can be used to analyze relationships between gene expressions and cancer survival rates. Types of cancer that can be analyzed include lung cancer [PER1, PER2, PER3, CRY1, and CRY2 genes in lung cancer patients and analyzed the OS of lung cancer patients under expressions of related genes, as well as the number of patients, median values of messenger (m)RNA expressions, 95% confidence intervals (CIs), hazard ratios (HRs), p values, and other related information.The KM plotter  to analyze expression levels, coexpressions, and network analyses of circadian rhythm-related genes of PER and CRY family members [TCGA is an open database with genome sequencing and related pathological data of more than 30 human tumors . We sele members –94.https://string-db.org/) is a biological database and web resource for searching and predicting protein-protein interactions (PPIs) [PER and CRY families, and establish a relationship network.STRING (s (PPIs) . In thishttps://david.ncifcrf.gov/) is a database that aims to provide functional explanations for a large number of genes from genome research. DAVID has four analytical modules, namely Annotation Tool, GoCharts, KeggCharts, and DomainCharts [PER1, PER2, PER3, CRY1, and CRY2 gene lists after cross-comparisons of different databases to evaluate how PER1, PER2, PER3, CRY1, and CRY2 affect molecular functions and may be related to various diseases.DAVID  were established to study interactions between malignant cells and host immune systems. It can be used to understand relationships between genes and tumor-infiltrating immune cells and evaluate their clinical impacts [PER and CRY family gene expressions on tumor-infiltrating immune cells.TIMER (cistrome.shinyapps.io/timer) and its upgraded version TIMER2.0 ( impacts –101. Thihttp://cis.hku.hk/TISIDB/) is an integrated repository portal for tumor-immune system interactions, which integrates multiple heterogeneous data types including the PubMed database, genomics, transcriptomics, and clinical data of 30 cancer types from TCGA, high-throughput screening data, exome and RNA sequencing datasets of patients, and other public databases including UniProt, GO, DrugBank, etc. It can be used to analyze correlations between immune checkpoint factors and circadian factors [TISIDB ( factors .http://tismo.cistrome.org/) is a database for hosting and analyzing an extensive collection of syngeneic mouse model data. The entire repository contains raw sequencing data from 1518 mouse samples, including 68 cell lines and 19 cancer types, which can be used to analyze relationships between cancers receiving different treatments  and gene expressions of circadian factors [TISMO . For the survival analysis, a KM plotter was applied, with all default settings, and recurrence-free survival (RFS) was preferred, with the auto-best cutoff values and J best probe set. All possible cutoff values between the lower and upper quartiles were determined, and the best presenting threshold was subsequently used as the cutoff. A log-rank p value of <0.05 was considered statistically significant [We utilized TCGA Pan-Cancer Atlas, a dataset from cBioPortal, to obtain patient data and query the effects of the expressions of different nificant , 109.https://cbioportal.org; The Human Protein Atlas: https://www.proteinatlas.org; Kaplan-Meier plot database https://kmplot.com; MetaCore analysis https://portal.genego.com. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.CBioPortal: Supplementary FiguresSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary Table 6Supplementary Table 7