TIMELESS as a Prognostic Biomarker and Therapeutic Target in Gastric Cancer

TIMELESS as a Prognostic Biomarker and Therapeutic Target in Gastric Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article TIMELESS as a Prognostic Biomarker and Therapeutic Target in Gastric Cancer Xiangrong Meng, Qi sun, Zhongshuang Liu, Shenqi Cao, Chunyang Xu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5397080/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Gastric cancer, a prevalent malignancy, exhibits intricate etiological and pathological characteristics. Recent insights into the dysregulation of clock genes offer novel avenues for diagnosis, treatment, and prognosis in patients with gastric cancer. Methods: This study leveraged machine learning, Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, survival prognosis analysis, drug sensitivity analysis, and in vitro experiments to elucidate the role of core clock genes in gastric cancer. Results: By integrating TCGA, GEO datasets, and NCBI database, we identified 29 differentially expressed clock genes. Utilization of four machine learning algorithms revealed TIMELESS and BHLHE41 as critical genes, with TIMELESS (AUC, 0.802) showing enhanced diagnostic potential for GC. High levels of TIMELESS expression in gastric cancer were associated with poor tumor prognosis and immune cell infiltration. We identified a targeted interaction between TIMELESS and the pyroptosis-related molecule CASP8, suggesting their collaborative involvement in gastric cancer pathogenesis. Moreover, Bortezomib was found to be a potential targeted therapy for TIMELESS in gastric cancer. Conclusion: TIMELESS emerges as a significant biomarker and therapeutic target in gastric cancer, with considerable implications for patient prognosis and treatment. TIMELESS gastric cancer prognosis tumor-infiltrating pyroptosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Gastric cancer (GC) is the fifth most commonly diagnosed cancer worldwide, with over one million new cases reported annually, and it is the third leading cause of cancer-related mortality [ 1 , 2 ] . Surgical resection is the optimal treatment for early-stage GC, offering the best chance for a favorable outcome. For patients with inoperable tumors or those presenting with advanced metastases, chemotherapy assumes a pivotal role in management [ 3 ] . The early stages of GC are often asymptomatic, leading to the majority of patients being diagnosed at an advanced stage with a concomitant poor prognosis [ 2 ] . The efficacy of current targeted therapies is suboptimal, attributable to the heterogeneity in clinical and biobehavioral factors, as well as the emergence of multi-drug resistance (MDR) in gastric cancer cells [ 4 ] . The molecular mechanisms underlying tumorigenesis and disease progression in GC remain poorly characterized, which significantly hampers the development of effective treatment strategies. A deeper understanding of the genomic underpinnings of tumor invasion, metastasis, and prognosis is crucial. Such insights may yield highly sensitive therapeutic approaches and facilitate the identification of novel prognostic biomarkers and therapeutic targets, potentially transforming the landscape of GC treatment. Circadian rhythms exert a pervasive influence on the behavior and physiology of eukaryotic cells, including the regulation of cellular processes that are integral to maintaining homeostasis. Oncogenes have been shown to modulate the expression of circadian rhythm genes, effectively disrupting the circadian cycle and predisposing cells to neoplastic transformation [ 5 , 6 ] . The interplay between cancer biology and cellular circadian rhythms is pivotal for elucidating the pathogenic mechanisms underlying cancer and for advancing therapeutic strategies. A less-recognized circadian regulator, TIMELESS, serves as a critical component of the cell cycle checkpoint system [ 7 ] . These genes regulate the auto-regulatory feedback loops that govern the core mammalian circadian rhythm, as well as exerting control over the negative limb of the circadian cycle [ 8 ] . Intriguingly, research into the expression patterns of circadian rhythm genes in cancer has revealed that TIMELESS are frequently overexpressed in a variety of malignancies, including breast [ 9 , 10 ] , colorectal [ 11 ] , lung [ 12 ] , and cervical cancers [ 13 ] . The overexpression of clock genes in cancer and their role in high-fidelity and rapid DNA synthesis suggest that these genes may contribute to the dysregulated proliferation characteristic of cancer cells. In this study, we integrated STAD datasets from GEO and TCGA and clock genes from GeneCards and NCBI to identify TIMELESS as a potential diagnostic biomarker in STAD using four machine learning algorithms. We conducted multidimensional analyses to evaluate TIMELESS genomic alterations, protein-interaction networks, and their implications for prognosis and tumor immunity. Single-gene enrichment analysis revealed a link between TIMELESS and pyroptosis, leading to the identification of key pyroptosis genes and potential binding sites with TIMELESS via molecular docking. Additionally, we identified miRNAs and lncRNAs associated with TIMELESS and performed drug sensitivity analysis (Fig. 1 ). Our results suggest the potential for TIMELESS to serve as a novel therapeutic and diagnostic target in gastric cancer. Materials and methods Collection and pretreatment of Gastric Cancer Dataset High-throughput gene expression datasets for stomach adenocarcinoma (STAD) patients and corresponding controls were obtained from The Cancer Genome Atlas database (TCGA, https://portal.gdc.cancer.gov/ ) and the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/gds ). The pre-processing steps for these three datasets are detailed as follows: the TCGA dataset underwent log2 transformation, data from all three datasets were merged based on shared genes, batch effect correction was applied, and the data were ultimately normalized, resulting in a consolidated dataset that includes 698 samples and 14,969 genes. Clock genes acquisition Clock genes were identified through searches utilizing the keyword "clock gene" in the GeneCards database ( https://www.genecards.org/ ) and the National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/ ) repository. The GeneCards screening was conducted based on correlation scores exceeding the average threshold and the criterion of protein-coding potential, yielding a total of 1840 genes. The NCBI screening yielded 270 genes. A comprehensive list of all searched clock genes is provided in Supplementary Table 1. Identification of the differentially expressed genes in STAD To identify differentially expressed genes in stomach adenocarcinoma (STAD), we employed the limma R software package (version 4.3.1) to analyze and compare the gene expression profiles between STAD tissues and their normal counterparts. Its selection criteria are P 0.5. The visualization of the differential expression analysis was conducted using the ‘gplots’ and ‘ggplot2’ packages in R, generating heatmaps and volcano plots. Ultimately, a Venn diagram analysis was applied to identify the differentially expressed clock genes among the significant findings. Function enrichment analysis Enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) terms was conducted using the "clusterProfiler" R package. This analysis encompassed Gene Ontology biological functions, including biological processes (BP), molecular functions (MF), and cellular components (CC). P -values were adjusted using the Benjamini-Hochberg method to control for multiple testing, and a threshold of P < 0.05 was applied to determine statistical significance. Based on these criteria, a core gene was selected for further Gene Set Enrichment Analysis (GSEA). GSEA was employed to explore the functional enrichments associated with the hub gene, utilizing the "clusterProfiler" package in R. Machine learning-identified characteristic Clock genes. We employed four distinct machine learning algorithms-LASSO, Random Forest (RF), XGBoost, and Neural Network (NNET) models-to identify discriminative clock genes in STAD patients. These models were implemented using the “glmnet”, “randomForest”, “xgboost”, and “neuralnet” R packages, respectively. In the LASSO model, the coefficients of the top 10 significant variables were determined based on the optimal penalty parameter λ, selected through tenfold cross-validation. The RF algorithm, utilizing 500 trees per data point, was employed to identify the top 10 variables by importance. The NNET model, a nonlinear approach, establishes gene importance rankings through multiple hidden layers and activation functions. Both XGBoost and NNET models were configured to ascertain the significance of gene sequences. The combined dataset was randomly partitioned into a training set (70%) and a validation set (30%). The outcomes from the four machine learning algorithms were intersected to identify the definitive set of discriminative clock genes. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), calculated with the “pROC” R package. Additionally, boxplots illustrating the differential expression of the intersecting genes across the combined dataset were generated using the “ggplot2” package, with statistical significance determined by the Wilcoxon test ( P < 0.05). TIMER Analysis TIMER ( https://cistrome.shinyapps.io/timer/ ) is a comprehensive and publicly accessible platform designed for systematic analysis of targeted gene expression across various tumor types [ 14 ] . We leveraged TIMER to explore the correlation between TIMELESS gene expression and STAD. Immune infiltration level analysis of hub genes The CIBERSORT algorithm was employed to investigate the cellular heterogeneity of target genes within the gastric cancer immune microenvironment [ 15 ] . TCGA-STAD samples were stratified into high and low-expression groups based on the median expression levels of the hub genes. Utilizing the "CIBERSORT" package, we quantified the proportion of 22 different types of tumor-infiltrating immune cells in both high and low-expression groups. The correlations between the hub genes and immune-infiltrating cells were evaluated using Spearman's correlation coefficients, which were computed with the R package "ggpubr". Finally, boxplots and forest plots were generated using the "ggpubr" and "vioplot" packages, respectively. P < 0.05 was considered statistically significant. Survival Analysis The Kaplan-Meier database ( https://kmplot.com/analysis/ ) is used to estimate the survival probabilities of study subjects over time, following specific treatment adjustments [ 16 ] . Accordingly, K-M survival analysis was conducted to investigate the correlation between the expression levels of the hub gene and the survival duration of STAD patients. This analysis involved the calculation of log-rank P-values and hazard ratios. P < 0.05 was considered statistically significant. Docking analysis of core pyroptosis-associated molecules with TIMELESS. A set of 52 pyroptosis-associated genes was curated based on prior literature (Supplementary Table 2). We conducted a correlation analysis between TIMELESS (TIM) and the intersecting genes derived from both pyroptosis and gastric cancer differential gene sets, utilizing R and applying Spearman's coefficient to determine the strength of these correlations. The Protein Data Bank (PDB) sequences for TIMELESS and its associated genes were retrieved from the PDB website ( https://www.rcsb.org/ ), and potential docking data were obtained from the ZDOCK website ( https://zdock.wenglab.org/ ). Visualization of these data was performed using PyMOL. Corresponding microRNA and long non-coding RNA (lncRNA) targeting mRNA were identified from the TargetScan database ( https://www.targetscan.org/vert_80/ ) and the ENCORI database ( https://rnasysu.com/encori/ ). Prediction of potential therapeutic drugs Expression data were sourced from the GDSC2 database, and 189 drug response profiles were retrieved from the Cancer Drug Sensitivity Genome (GDSC) database ( https://www.cancerrxgene.org/ ). The TCGA samples were stratified into high and low-expression groups based on the median expression levels of the hub genes. The susceptibility of the hub genes to drugs in the high- and low-expression groups was predicted using the “calcPhenotype” function within the “oncoPredict” R package. Statistical differences in drug response between the two groups were assessed using the Wilcoxon test, with a significance threshold set at P < 0.05. Finally, data visualization was carried out using the “ggplot2” R package. Immunohistochemical validation in the THPA database The Tissue Human Protein Atlas (THPA), a publicly accessible resource funded by Sweden, comprises over five million immunohistochemically stained tissue and cell distribution data points for 26,000 human proteins. THPA facilitates the examination of both normal and STAD tissues through antibody proteomics and is frequently utilized for the validation of hub gene expression. We employed this pathology tool to assess the expression levels of TIMELESS in normal gastric mucosal tissues and STAD tissues as recorded in the THPA database. Cell culture The human gastric cancer cell line AGS was cultured in RPMI 1640 medium (Gibco, USA) supplemented with 10% fetal bovine serum (FBS), 100 µg/mL streptomycin, and 100 U/mL penicillin. The cells were incubated at 37°C in a humidified chamber with an atmosphere containing 5% CO 2 . Western blot AGS cells were lysed using RIPA lysis buffer, and western blot analysis was conducted following established protocols. Briefly, 30 µg of protein from each sample was loaded and subjected to separation on a 10% SDS-polyacrylamide gel electrophoresis (SDS-PAGE) gel, followed by transfer to a polyvinylidene fluoride (PVDF) membrane. After blocking to prevent non-specific binding, the membranes were incubated with primary antibodies against TIMELESS (Proteintech) and GAPDH, along with a horseradish peroxidase (HRP)-conjugated goat anti-rabbit IgG secondary antibody. Protein bands were detected and visualized using the ChemiDoc XRS gel documentation and analysis system with a chemiluminescence kit (Beyotime, Shanghai, China). Statistical analysis Data analysis and visualization were performed using R software version 4.3.1. The comparison of gene expression levels between the two groups was conducted using the unpaired t -test as implemented in GraphPad Prism version 9 (GraphPad Software, California, USA). Results are presented as means ± standard deviations. Unless otherwise indicated, a P < 0.05 was considered to indicate statistical significance. Results Identification and functional Enrichment of differentially expressed Clock genes in GC. We integrated expression profiles from the TCGA, GSE27342, and GSE63089 datasets and performed batch effect removal, and standardization to compile a dataset consisting of 161 normal tissue samples and 537 stomach adenocarcinoma (STAD) tissue samples. By Limma program analysis, 2287 differentially expressed genes (DEGs) were identified ( P 0.5), of which 1694 were up-regulated and 593 were down-regulated (Fig. 2 A and B). Subsequently, we obtained 2110 clock genes from GeneCards and NCBI databases and identified 29 DEGs clock genes through VENN analysis (Fig. 2 C). GO analysis shows that, the functions of differentially expressed clock genes in STAD are mainly concentrated in the regulation of inflammatory response, cellular response to UV-A, angiogenesis, oxidative stress, interleukin-1 signaling, NNS dependent protein nuclear input, Wnt signaling bodies, NADPH oxidase complexes, NF-kappaB binding, RAGE receptor binding, and toll-like receptor Body combination and other aspects (Fig. 2 D-F). In addition, KEGG analysis showed that Th17 cell differentiation, leukocyte transendothelial migration, reactive oxygen chemical carcinogenesis, IL-17 signaling pathway, and HIF-1 signaling pathway were closely related to these clock regulators (Fig. 2 G). The heat map further details the relationship between enrichment function and the inclusion of each clock gene (Fig. 2 H). Differential gene enrichment analysis suggested that clock genes might be involved in gastric cancer progression through regulation of validation response, oxidative stress and signal transduction. Machine learning-identified Key Clock Genes. LASSO, RF, XGBoost, and NNET techniques were employed to identify pivotal clock genes within the gastric cancer context. In the LASSO regression analysis, the optimal λ value of 0.072 facilitated the selection of the top 10 variables (KAT2B, BHLHE41, MAOA, PER1, RB1, TIMELESS, MMP9, AHR, GHRL, HIF1A) based on the ranking of their coefficient magnitudes (Fig. 3 A). The RF algorithm, integrated with feature importance assessment, delineated the relationship between the error rate, the number of classification trees, and the 29 genes, culminating in the identification of the top five influential genes (Fig. 3 B). Both XGBoost and NNET algorithms independently ranked the top 10 variables according to their significance (Fig. 3 C-D). The performance of each model was rigorously evaluated using ROC curves for both training and validation datasets. Through Venn diagram analysis, we identified two key diagnostic clock genes, TIMELESS and BHLHE41, in STAD (Fig. 3 E). The ROC curves for TIMELESS (AUC, 0.802) and BHLHE41 (AUC, 0.662) suggested that TIMELESS is a potential valuable diagnostic biomarker in STAD (Fig. 3 F-G). Expression level assessment and GSEA of TIMELESS in GC. Analysis with the TIMER database revealed a markedly elevated TIM mRNA expression in GC tissues relative to normal controls (Fig. 4 A). Examination of the RNA-seq dataset from TCGA patients showed increased TIM mRNA expression across all STAD tissues (Fig. 4 B-C). Western blot analysis confirmed these findings, demonstrating a pronounced elevation in TIM expression in gastric cancer cells compared to normal gastric mucosa cells ( P < 0.0001) (Fig. 4 D). Immunohistochemical staining of tissue microarrays from THPA further corroborated this, with higher TIM expression levels in gastric cancer tissues compared to normal gastric mucosal tissues (Fig. 4 E). Through the use of cBioPortal, we identified significant copy number variations within the TIM genes of interest. Figure 4 F details a total of 206 mutations within the sample, spanning from 0 to 1100 alteration sites, comprising 167 missense mutations and 23 truncation mutations. The mutational burden of TIM in gastric cancer, ranking fourth, was primarily characterized by amplifications and truncal mutations (Fig. 4 F-G). GSEA was performed to elucidate TIM-related functional roles and signaling pathways (Fig. 4 H-I). GO analysis revealed that TIM is predominantly linked to DNA repair signaling pathways and cellular activities, including p53 mediator signal transduction, receptor regulatory activity, nucleocytoplasmic transport activity, and mRNA cleavage. KEGG enrichment analysis highlighted significant associations between TIM and pathways implicated in DNA damage repair, NOD-like receptor signaling, p53 signaling, nucleoplasmic transport, mRNA surveillance, and drug metabolism via cytochrome P450 pathways. TIMLESS modulates immune cell infiltration and GC Prognosis To further investigate the correlation between TIM expression and patient prognosis from published gastric cancer microarrays, we employed the Kaplan–Meier plotter, which disclosed that TIM overexpression was significantly associated with diminished overall survival (OS) (HR 1.68, 95% CI 1.41–1.99, P = 2.7 e − 09 ) and post-progression survival (PPS) (HR 1.8, 95% CI 1.43–2.28, P = 5.7 e − 07 ) in gastric cancer patients (Fig. 5 A-C). We further appraised the impact of TIM expression on the survival of gastric cancer patients under various confounding factors. The findings indicated that elevated TIM expression was associated with poorer OS irrespective of gender or differentiation levels of gastric cancer patients (Fig. 5 D-H). TIM overexpression curtailed OS in patients who underwent surgery alone or 5-FU adjuvant therapy at both 5 and 10 years (Fig. 5 I-J). Given the intimate link between tumorigenesis, invasion, and the dynamics of the immune microenvironment, we scrutinized the immunomodulatory role of TIM in gastric cancer. We stratified 412 gastric cancer tissue samples from the TCGA into high and low TIM expression cohorts based on the median expression level. The TIM high-expression group exhibited a significantly elevated proportion of CD4 memory activated T cells, follicular helper T cells, resting NK cells, M0 macrophages, and M1 macrophages compared to the low-expression group (Fig. 5 K). Conversely, the proportion of memory B cells, regulatory Tregs, CD4 memory resting T cells, resting mast cells, and monocytes was markedly reduced in the TIM high-expression group. Spearman's rank correlation analysis revealed that TIM correlated positively with follicular helper T cells, M1 macrophages, M0 macrophages, CD4 memory activated T cells, activated mast cells, and resting NK cells, and negatively with regulatory Tregs, resting mast cells, activated NK cells, monocytes, CD4 memory resting T cells, and memory B cells (Fig. 5 L). Association of TIMELESS with pyroptosis in GC Given the significant association of TIM with molecular pathways related to inflammation and DNA damage repair, and considering pyroptosis as a cell death mechanism intricately linked with these processes, we identified 52 common pyroptosis factors from the existing literature to explore the potential molecular interplay between TIM and pyroptosis molecules. Our analysis involved the intersection of 2287 differentially expressed GC genes with pyroptosis genes, yielding 7 overlapping genes (CASP5, CASP8, IL1A, IL18, GZMB, PLCG1, BAX) (Fig. 6 A). Employing Spearman's correlation analysis within the gastric cancer combined dataset, we found significant positive correlations between TIM and three of these pyroptosis genes (PLCG1, CASP8, BAX) (Fig. 6 B-D), while no significant correlations were observed for the others. Subsequent molecular docking predictions using ZDOCK, visualized with PyMOL, indicated potential interaction sites between TIM and CASP8 (Fig. 6 E-G). To further elucidate the nuanced regulation of the TIM-CASP8 interaction, we identified microRNAs and long non-coding RNAs associated with TIM and CASP8 from TargetScan and ENCORI databases (Fig. 6 H). This comprehensive approach provides insights into the intricate regulatory network involving TIM and pyroptosis in gastric cancer. Drug sensitivity analysis of TIMELESS To elucidate the influence of TIM gene expression on drug treatment sensitivity in STAD, we conducted further drug sensitivity predictions, which are graphically represented as boxplots. The analysis revealed significant disparities in drug responsiveness between patients with high TIM expression and those with low TIM expression. Patients in the high TIM expression group demonstrated enhanced sensitivity to Bortezomib, Vinorelbine, Rapamycin, Dinaciclib, Daporinad, Eg5_9814, Sepantronium, Docetaxel, Vinblastine, MG-132, and Paclitaxel, whereas patients with low TIM expression exhibited greater sensitivity to AZD8055 (Fig. 7 ). Notably, among the drugs exhibiting potent efficacy in the TIM high expression group, Bortezomib stood out with the most pronounced effect on gastric cancer treatment (IC50 < 0.01). This finding implies that Bortezomib, potentially targeting TIM, could serve as a more effective guide for prognostic stratification among gastric cancer patients. Discussion The circadian rhythm, a fundamental biological clock, exerts a pivotal influence on the periodic fluctuations of various biological processes and behaviors [ 17 , 18 ] . Perturbations within the circadian regulatory framework have been identified as potential contributors to the etiology of a spectrum of cancers, notably breast, endometrial, lung, glioma, and colorectal malignancies. These perturbations modulate key oncogenic properties, including angiogenesis, apoptosis, and cellular proliferation [ 19 ] . Nonetheless, the definitive function of the circadian clock in the prognostic assessment and therapeutic intervention of stomach adenocarcinoma (STAD) remains a necessitating further exploration to unravel its intricate mechanisms in this context. The analysis of GC datasets within TCGA, GSE27342 and GSE63089 databases, employing four machine learning (ML) algorithms, identified TIMELESS and BHLHE41 as signature genes. Our study confirmed that TIMELESS demonstrates enhanced diagnostic accuracy in GC, with this gene being markedly overexpressed in GC tissues and cell lines at both the transcriptional and translational levels. Extant literature indicates that TIMELESS expression is elevated in gliomas, with notably higher expression in high-grade gliomas compared to low-grade tumors [ 20 ] . Yang et al. [ 21 ] have proposed the circadian rhythm gene TIMELESS as a candidate for comprehensive assessment and a prognostic biomarker across various cancers. The aberrant expression of TIMELESS is significantly correlated with advanced tumor stages, unfavorable prognosis, and a spectrum of immune cell infiltration within neoplasms. Collectively, these findings, alongside our results, imply that TIMELESS expression is not confined to specific tissues, suggesting a widespread influence in oncogenesis. A recently study [ 22 ] documented that elevated TIMELESS expression in tissues characterized by active proliferation, suggesting that the dysregulation of TIMELESS may be a pivotal factor in the progression and spread of GC. TIMELESS is a regulator of the circadian rhythm and serves as a crucial connecting molecule that "directly couples" the circadian rhythm with the cell cycle [ 7 ] . It directly or indirectly modulates the activity of self-regulatory components central to mammalian circadian rhythms, including the CLOCK, Per, and Cry proteins, which are associated with the S-phase replication checkpoint proteins Claspin and Tipin [ 23 , 24 ] . In the context of GC, we observed that TIMELESS carries amplification and motility mutations that are likely to disrupt the biological clock and cell cycle checkpoints, resulting in the loss of circadian patterns. Furthermore, single-gene Gene Set Enrichment Analysis (GSEA) results indicated that TIMELESS was predominantly enriched in DNA damage repair signaling pathways and intracellular transcriptional activity. A characteristic of GC is a high degree of genomic instability, which is linked to the dysregulation of DNA damage repair pathways [ 25 ] . The efficiency of DNA damage induction and repair has been demonstrated to be influenced by the diurnal cycle [ 26 ] . Defects in DNA replication can lead to mutations or replication blockages that result in chromosome breakage, rearrangement, or incorrect segregation, which are chromosomal aberrations that can precipitate cancer and a spectrum of other diseases [ 27 , 28 ] . The Kaplan-Meier analysis revealed that elevated TIMELESS expression is significantly correlated with an adverse prognosis in GC, impacting both overall survival and post-progression survival. This association holds true regardless of sex or the degree of tumor differentiation. Notably, even patients who have undergone surgery or 5-FU treatment and exhibit TIMELESS overexpression have a poorer prognosis at the 5- and 10-year follow-ups. The intracellular circadian clock was observed to directly regulate apoptosis [ 29 ] , suggesting that TIMELESS suppression could enhance the cytotoxicity of chemotherapeutic drugs, particularly those that target DNA response pathways within cancer cells [ 30 ] . In vitro studies have shown that the suppression of TIMELESS in doxorubicin-treated HCT116 colon cancer cells correlates with a decrease in G2/M cell cycle arrest and heightened sensitivity to doxorubicin-induced cytotoxic effects [ 31 ] . Therefore, the modulation of TIMELESS protein levels may impede aberrant cell growth. These findings collectively suggest that TIMELESS may be implicated in the modulation of critical oncogenic pathways. The composition of immune cells within the tumor microenvironment (TME) significantly influences the prognosis of cancer patients [ 32 – 34 ] . Tumors are categorized into three fundamental immunophenotypes based on the spatial distribution of cytotoxic immune cells within the TME: immunoinflammatory, immune exclusion, and immune desert phenotypes [ 35 ] . Immunoinflammatory tumors, colloquially referred to as "hot tumors," are distinguished by T lymphocytes infiltration and elevated tumor mutational burden (TMB). In contrast, immunorepulsive and immune desert tumors are classified as "cold tumors" [ 36 ] . In immunologically rejected tumors, T lymphocytes, particularly CD8 + T cells, fail to infiltrate effectively. These "cold tumors" are further characterized by a low mutational load, diminished major histocompatibility complex (MHC) class I expression, and reduced PD-L1 expression. Additionally, immunosuppressive cell populations, such as T-regulatory cells (Tregs) and myeloid-derived suppressor cells (MDSCs), are prevalent in cold tumors. In our study, the TME exhibited heightened infiltration of CD4 memory-activating T cells and follicular helper T cells, alongside a reduction in Treg cell infiltration. This profile aligns more closely with the characteristics of "hot tumors," that is, immunoinflammatory tumors, suggesting that GC may exhibit greater sensitivity to immune checkpoint inhibitors. Based on the characteristic pro-inflammatory tendencies of immunoinflammatory tumors in GC and the regulatory role of clock genes in inflammatory responses, as identified in the enrichment analysis of differentially expressed GC genes, we hypothesize the involvement of the pyroptosis pathway in GC development. Pyroptosis is the predominant mode of cell death in inflammatory contexts. We identified seven genes through the intersection of gastric cancer differential genes and pyroptosis-associated genes. It was confirmed that CASP8, BAX, and PLCG1 were significantly positively correlated with TIMELESS. However, only CASP8 is predicted to have potential docking sites with TIMELESS. CASP8, a member of the cysteine protease family, initiates death receptor-mediated apoptosis [ 37 ] . Variations in the casp8 gene are associated with reduced susceptibility to various cancers, including stomach, lung, esophageal, colorectal, cervical, and breast, acting in an allelic dose-dependent manner [ 38 ] . Hypermethylation of the casp8 gene promoter in cancer tissue and blood samples is significantly associated with GC [ 39 ] . These findings suggest that TIMELESS and CASP8 may play a role in the etiology and progression of GC by modulating pyroptosis through direct interaction. To identify targeted drugs through TIMELESS, we conducted a drug susceptibility prediction analysis, which indicated that Bortezomib had the lowest IC50 value. Bortezomib, a selective proteasome inhibitor, exerts a significant effect on various tumors, including multiple myeloma [ 40 ] . Bortezomib induces downregulation of telomerase reverse transcriptase expression and contributes to telomere dysfunction, playing a role in the elimination of leukemia and GC cells (BGC-823) [ 41 ] . TIMELESS has a protective effect on telomeres, and its absence can lead to telomere shortening in a non-telomerase-dependent manner [ 42 ] . Thus, we propose a mechanistic hypothesis that Bortezomib may compromise the telomere integrity of GC cells by targeting TIMELESS, leading to the loss of repetitive DNA. Concurrently, the depletion of TIMELESS disrupts its direct regulatory interaction with CASP8, promoting the pyroptotic death of GC cells and thereby exerting anti-tumor effects. Conclusion This study presents a multifaceted evidence base that underscores the potential of TIMELESS as a biomarker for gastric cancer and its significance in prognosticating GC outcomes. The overexpression of TIMELESS in GC may confer protection to cancer cells under replication stress. We have proposed a potential regulatory role for TIMELESS in the pyroptosis of tumor cells, specifically by targeting CASP8. With the ascendance of TIMELESS as a biomarker for cancer prognosis, the investigation of TIMELESS-targeted therapeutics in oncology assumes significant importance. Our research indicates that Bortezomib could be a promising molecular therapeutic agent targeting TIMELESS. These insights may pave the way for an immune-based antitumor strategy, potentially involving signaling pathways that modulate the tumor cell cycle or the invasive capabilities of the tumor microenvironment. Nevertheless, the ultimate objective of future research is to identify a TIMELESS-targeted therapy or drug for gastric cancer and to elucidate its mechanism of action. Declarations Author contributions WZ conceived the original idea and supervised the study. WZ, XM and QS prepared the manuscript, tables. ZL executed the data analysis. SC prepared the figures. CX undertook the literature collation work. LD paticipated in the study discussion. YW corrected the manuscript and completed the in vitro experiment. All authors have read and approved to submit the manuscript. Ethics approval and consent to participate Not applicable. Competing interests The authors declare that they have no competing financial interests or personal relationships that may have an impact on the work reported in this paper. Funding Source This work was supported by National Natural Science Foundation of China (Grant Nos. 82001715), Health and Family Planning Commission of Heilongjiang Provincial (Grant No. 2018286), The Fundamental Research Funds for the Provincial Universities (Grant No. 2017LCZX67). Shenzhen Science and Technology Program (No. JCYJ20220531103014033). Availability of data and materials The datasets generated and analysed during the current study are available in the TCGA, GEO, NCBI and GeneCards repository, [https://portal.gdc.cancer.gov/, https://www.ncbi.nlm.nih.gov/gds, https://www.ncbi.nlm.nih.gov/ and https://www.genecards.org/]. Consent for publication Not applicable. Acknowledgements Not applicable. References Fitzmaurice C, Abate D, Abbasi N, Abbastabar H, Abd-Allah F, Abdel-Rahman O, Abdelalim A, Abdoli A, Abdollahpour I, Abdulle ASM, et al. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2017: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol. 2019;5(12):1749–68. Thrift AP, El-Serag HB. Burden of Gastric Cancer. Clin Gastroenterol hepatology: official Clin Pract J Am Gastroenterological Association. 2020;18(3):534–42. Czabotar PE, Lessene G, Strasser A, Adams JM. Control of apoptosis by the BCL-2 protein family: implications for physiology and therapy. Nat Rev Mol Cell Biol. 2014;15(1):49–63. Nies AT, Magdy T, Schwab M, Zanger UM. Role of ABC transporters in fluoropyrimidine-based chemotherapy response. Adv Cancer Res. 2015;125:217–43. Stevens RG. Circadian disruption and breast cancer: from melatonin to clock genes. Epidemiol (Cambridge Mass). 2005;16(2):254–8. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74. Unsal-Kaçmaz K, Mullen TE, Kaufmann WK, Sancar A. Coupling of human circadian and cell cycles by the timeless protein. Mol Cell Biol. 2005;25(8):3109–16. Cao M, Wang Y, Xiao Y, Zheng D, Zhi C, Xia X, Yuan X. Activation of the clock gene TIMELESS by H3k27 acetylation promotes colorectal cancer tumorigenesis by binding to Myosin-9. J Exp Clin Cancer Res. 2021;40(1):162. Li B, Mu L, Li Y, Xia K, Yang Y, Aman S, Ahmad B, Li S, Wu H. TIMELESS inhibits breast cancer cell invasion and metastasis by down-regulating the expression of MMP9. Cancer Cell Int. 2021;21(1):38. Bianco JN, Bergoglio V, Lin YL, Pillaire MJ, Schmitz AL, Gilhodes J, Lusque A, Mazières J, Lacroix-Triki M, Roumeliotis TI, et al. Overexpression of Claspin and Timeless protects cancer cells from replication stress in a checkpoint-independent manner. Nat Commun. 2019;10(1):910. Neilsen BK, Frodyma DE, McCall JL, Fisher KW, Lewis RE. ERK-mediated TIMELESS expression suppresses G2/M arrest in colon cancer cells. PLoS ONE. 2019;14(1):e0209224. Yoshida K, Sato M, Hase T, Elshazley M, Yamashita R, Usami N, Taniguchi T, Yokoi K, Nakamura S, Kondo M, et al. TIMELESS is overexpressed in lung cancer and its expression correlates with poor patient survival. Cancer Sci. 2013;104(2):171–7. Zou X, Zhu C, Zhang L, Zhang Y, Fu F, Chen Y, Zhou J. MicroRNA-708 Suppresses Cell Proliferation and Enhances Chemosensitivity of Cervical Cancer Cells to cDDP by Negatively Targeting Timeless. OncoTargets therapy. 2020;13:225–35. Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, Li B, Liu XS. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509–14. Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods in molecular biology (Clifton, NJ) 2018, 1711:243–259. Goel MK, Khanna P, Kishore J. Understanding survival analysis: Kaplan-Meier estimate. Int J Ayurveda Res. 2010;1(4):274–8. Luo B, Song J, Zhang J, Han J, Zhou X, Chen L. The contribution of circadian clock to the biological processes. Front Mol Biosci. 2024;11:1387576. Serin Y, Acar Tek N. Effect of Circadian Rhythm on Metabolic Processes and the Regulation of Energy Balance. Ann Nutr Metab. 2019;74(4):322–30. Zhu Y, Zheng Y, Dai R, Gu X. Crosstalk between Circadian Rhythm Dysregulation and Tumorigenesis, Tumor Metabolism and Tumor Immune Response. Aging and disease; 2024. Wang F, Chen Q. The analysis of deregulated expression of the timeless genes in gliomas. J Cancer Res Ther. 2018;14(Supplement):S708–12. Yang Y, Tang X, Lin Z, Zheng T, Zhang S, Liu T, Yang X. An integrative evaluation of circadian gene TIMELESS as a pan-cancer immunological and predictive biomarker. Eur J Med Res. 2023;28(1):563. Zhou J, Zhang Y, Zou X, Kuai L, Wang L, Wang J, Shen F, Hu J, Zhang X, Huang Y, et al. Aberrantly Expressed Timeless Regulates Cell Proliferation and Cisplatin Efficacy in Cervical Cancer. Hum Gene Ther. 2020;31(5–6):385–95. Hoffman AE, Yi CH, Zheng T, Stevens RG, Leaderer D, Zhang Y, Holford TR, Hansen J, Paulson J, Zhu Y. CLOCK in breast tumorigenesis: genetic, epigenetic, and transcriptional profiling analyses. Cancer Res. 2010;70(4):1459–68. Hoffman AE, Zheng T, Yi CH, Stevens RG, Ba Y, Zhang Y, Leaderer D, Holford T, Hansen J, Zhu Y. The core circadian gene Cryptochrome 2 influences breast cancer risk, possibly by mediating hormone signaling. Cancer Prev Res (Philadelphia Pa). 2010;3(4):539–48. Zhang Y, Wen GM, Wu CA, Jing ZL, Li DZ, Liu GL, Wei XX, Tang MS, Li YH, Zhong Y, et al. PRKDC is a prognostic marker for poor survival in gastric cancer patients and regulates DNA damage response. Pathol Res Pract. 2019;215(8):152509. Andersen B, Duan J, Karri SS. How and Why the Circadian Clock Regulates Proliferation of Adult Epithelial Stem Cells. Stem Cells. 2023;41(4):319–27. Copeland WC. Defects in mitochondrial DNA replication and human disease. Crit Rev Biochem Mol Biol. 2012;47(1):64–74. Barlow JH, Nussenzweig A. Replication initiation and genome instability: a crossroads for DNA and RNA synthesis. Cell Mol Life Sci. 2014;71(23):4545–59. Sadiq Z, Varghese E, Büsselberg D. Cisplatin's dual-effect on the circadian clock triggers proliferation and apoptosis. Neurobiol sleep circadian rhythms. 2020;9:100054. Sancar A, Lindsey-Boltz LA, Kang TH, Reardon JT, Lee JH, Ozturk N. Circadian clock control of the cellular response to DNA damage. FEBS Lett. 2010;584(12):2618–25. Yang X, Wood PA, Hrushesky WJ. Mammalian TIMELESS is required for ATM-dependent CHK2 activation and G2/M checkpoint control. J Biol Chem. 2010;285(5):3030–4. Quail DF, Joyce JA. Microenvironmental regulation of tumor progression and metastasis. Nat Med. 2013;19(11):1423–37. Barcellos-Hoff MH, Newcomb EW, Zagzag D, Narayana A. Therapeutic targets in malignant glioblastoma microenvironment. Semin Radiat Oncol. 2009;19(3):163–70. Fu W, Wang W, Li H, Jiao Y, Weng J, Huo R, Yan Z, Wang J, Xu H, Wang S, et al. High Dimensional Mass Cytometry Analysis Reveals Characteristics of the Immunosuppressive Microenvironment in Diffuse Astrocytomas. Front Oncol. 2020;10:78. Cheng YQ, Wang SB, Liu JH, Jin L, Liu Y, Li CY, Su YR, Liu YR, Sang X, Wan Q, et al. Modifying the tumour microenvironment and reverting tumour cells: New strategies for treating malignant tumours. Cell Prolif. 2020;53(8):e12865. Galon J, Bruni D. Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nat Rev Drug Discovery. 2019;18(3):197–218. Salvesen GS. Caspase 8: igniting the death machine. Struct (London England: 1993). 1999;7(10):R225–229. Sun T, Gao Y, Tan W, Ma S, Shi Y, Yao J, Guo Y, Yang M, Zhang X, Zhang Q, et al. A six-nucleotide insertion-deletion polymorphism in the CASP8 promoter is associated with susceptibility to multiple cancers. Nat Genet. 2007;39(5):605–13. Azarkhazin F, Tehrani GA. Detecting promoter methylation pattern of apoptotic genes Apaf1 and Caspase8 in gastric carcinoma patients undergoing chemotherapy. J Gastrointest Oncol. 2018;9(2):295–302. Nakata W, Hayakawa Y, Nakagawa H, Sakamoto K, Kinoshita H, Takahashi R, Hirata Y, Maeda S, Koike K. Anti-tumor activity of the proteasome inhibitor bortezomib in gastric cancer. Int J Oncol. 2011;39(6):1529–36. Ci X, Li B, Ma X, Kong F, Zheng C, Björkholm M, Jia J, Xu D. Bortezomib-mediated down-regulation of telomerase and disruption of telomere homeostasis contributes to apoptosis of malignant cells. Oncotarget. 2015;6(35):38079–92. Gadaleta MC, Das MM, Tanizawa H, Chang YT, Noma K, Nakamura TM, Noguchi E. Swi1Timeless Prevents Repeat Instability at Fission Yeast Telomeres. PLoS Genet. 2016;12(3):e1005943. Additional Declarations No competing interests reported. Supplementary Files supplementarytable.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5397080","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374895573,"identity":"95bc807a-b839-4313-84a3-b53aa12d6f40","order_by":0,"name":"Xiangrong Meng","email":"","orcid":"","institution":"the Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiangrong","middleName":"","lastName":"Meng","suffix":""},{"id":374895574,"identity":"f28ba102-3e5a-4298-9a3a-67bb75dd140b","order_by":1,"name":"Qi sun","email":"","orcid":"","institution":"The First Affiliated Hospital of Heilong Jiang University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"sun","suffix":""},{"id":374895575,"identity":"7167d89b-f41d-46d0-9109-6dd5dc485d16","order_by":2,"name":"Zhongshuang Liu","email":"","orcid":"","institution":"Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy","correspondingAuthor":false,"prefix":"","firstName":"Zhongshuang","middleName":"","lastName":"Liu","suffix":""},{"id":374895576,"identity":"569034b0-744f-4ca8-b6cc-6acdd7d76e75","order_by":3,"name":"Shenqi Cao","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shenqi","middleName":"","lastName":"Cao","suffix":""},{"id":374895577,"identity":"bcd7c631-4871-40dc-8b21-0f1e0c759ed4","order_by":4,"name":"Chunyang Xu","email":"","orcid":"","institution":"the Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chunyang","middleName":"","lastName":"Xu","suffix":""},{"id":374895578,"identity":"59e29d51-ddb7-4d9c-bdea-eb0471312c16","order_by":5,"name":"Yan Wu","email":"","orcid":"","institution":"the Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Wu","suffix":""},{"id":374895579,"identity":"4df0ae15-681f-4019-bfb1-fa6897ac29b3","order_by":6,"name":"Wenjing Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYJACgwQQAWJ9bGBIAAvxEKuFcSaxWiD6gJiZlxgtBsd7DxQ83FFrb85+9vBr2x338nRnJDA+eNvGIG+OS8uZcwkGiWeOM1v25KVZ554pLja7kcBsOLeNwXBnA3YtZjdyDAwS246xGRzIMTPObUtI3HYjgU2at40hweAADi3334C18Bicf2NmbAnRwv4br5YbPCAtNRIGN3KMHzNCbWHGp8X+DNhhBwwMbrwxY+xtSyg2O/OwWXLOOQnDDTi0SLafMTP82VZnb3A+x/jDz7aEPLPjyQc/vCmzkcdlCxCwAaPkMJghARFgbAASEjjVAwHzAwaGOjDjAz5lo2AUjIJRMHIBAM+8Yrxs31IPAAAAAElFTkSuQmCC","orcid":"","institution":"the Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wenjing","middleName":"","lastName":"Zhang","suffix":""},{"id":374895580,"identity":"c6ebbe01-7975-48c4-ac41-b7dbade723d4","order_by":7,"name":"Longjiang Di","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Longjiang","middleName":"","lastName":"Di","suffix":""}],"badges":[],"createdAt":"2024-11-05 16:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5397080/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5397080/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70478116,"identity":"9340a5a3-993f-48f2-b43e-4b4529b9ac5d","added_by":"auto","created_at":"2024-12-03 14:36:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":354001,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flow chart.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5397080/v1/688f8b70253b184fe621ced5.jpg"},{"id":70479473,"identity":"0007835d-ebd7-4a40-bb59-fb3c2f0f53af","added_by":"auto","created_at":"2024-12-03 14:44:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6123698,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and functional enrichment of differentially expressed Clock genes in GC. (A-B) Comparison of the expression profiles of abnormal genes regulators across GC and normal tissues in combined dataset using volcano and heatmap. (C) The Venn diagram illustrates the intersection of differentially expressed clock genes common to the three datasets. (D) GO-Biological Process (E) GO-Cellular Component (F) GO-Molecular Function and (G) KEGG pathway functional enrichment analyses of the common genes (P \u0026lt;0.05). (H) Heatmap illustrating the correlation between enrichment functions and the involvement of each clock gene.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5397080/v1/dc6ebde1eb661defee35dce4.jpg"},{"id":70479471,"identity":"7f00613b-0b36-4728-aaba-d90ff5495ac2","added_by":"auto","created_at":"2024-12-03 14:44:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5675512,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of key Clock genes via machine learning. (A) DEGs identified through the LASSO algorithm. (B) DEGs selected by the Random Forest (RF) algorithm based on a Mean Decrease Gini score greater than 10. (C) XGBoost and (D) Neural Network (NNET) algorithms illustrate gene importance. The ROC curve assesses model performance. (E) Venn diagram displaying the intersection of two diagnostic biomarkers identified by the four algorithms. (F-G) The area under the ROC curve (AUC) and 95% confidence interval (CI) evaluate the diagnostic performance of TIMELESS and BHLHE41.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5397080/v1/897125e23e3a6b164d36aa53.jpg"},{"id":70479472,"identity":"af5dbaad-58f3-42ae-a3b7-b3011d8f438b","added_by":"auto","created_at":"2024-12-03 14:44:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7763073,"visible":true,"origin":"","legend":"\u003cp\u003eExpression analysis and GSEA of TIMELESS in GC. (A) Expression levels of TIMELESS across STAD tumors in the TCGA database as measured by the TIMER database. (B-C) Differential expression of TIMELESS in gastric cancer tissues. (D) Western blot validation of TIMELESS differential expression in gastric cancer. (E) Immunohistochemical observation of TIMELESS protein expression in gastric tissues using the THPA database. (F-G) Epigenetic modifications of TIMELESS in gastric cancer tissues. (H) Gene Ontology (GO) and (I) KEGG pathway functional GSEA enrichment analyses of TIMELESS, with a significance threshold of P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5397080/v1/87f7f1c7cb5f7e8bff9f6d87.jpg"},{"id":70478119,"identity":"bd1bd5da-4ec0-48d5-af76-4034005e9a90","added_by":"auto","created_at":"2024-12-03 14:36:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4756061,"visible":true,"origin":"","legend":"\u003cp\u003ePrognosis survival analysis and immune cell infiltration of TIMELESS in GC. (A-C) Analysis of overall survival (OS), first progression survival (FPS), and post-progression survival (PPS) in low and high TIMELESS expression groups. (D-E) Analysis of TIMELESS expression differences stratified by gender. (F-H) Analysis of TIMELESS expression differences across various degrees of tumor differentiation. (I-J) Analysis of TIMELESS expression differences under different treatment conditions. (K) Proportions of 22 immune cell subtypes in high and low TIMELESS expression groups in GC. (L) Correlation between TIMELESS and various immune cells analyzed using Spearman's rank correlation coefficient (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5397080/v1/a2eb8d7b417df995580e577a.jpg"},{"id":70478123,"identity":"fcf97cf9-ddd7-431d-bb29-495dd372b8f4","added_by":"auto","created_at":"2024-12-03 14:36:30","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5072778,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of TIMELESS with pyroptosis-associated genes in GC. (A) The Venn diagram displays the intersection of pyroptosis-related genes and differentially expressed genes in GC. (B-D) Spearman's correlation analysis between TIMELESS and individual pyroptosis-associated genes. (E-G) Molecular docking analysis depicting the interactions of TIMELESS with CASP8, PLCG1, and BAX. (H) Analysis of microRNA and long non-coding RNA (lncRNA) interactions between TIMELESS and CASP8.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5397080/v1/24118d3a1056dbb90ebef372.jpg"},{"id":70478118,"identity":"32f02cc4-5832-441a-a857-b4ef4d5379da","added_by":"auto","created_at":"2024-12-03 14:36:30","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1753448,"visible":true,"origin":"","legend":"\u003cp\u003eDrug Sensitivity Analysis in Relation to TIMELESS Expression. Green denotes the group with low TIMELESS expression, while orange indicates the group with high TIMELESS expression. The Wilcoxon test was employed to assess the differential expression of various drugs between the low and high TIMELESS expression groups, with statistical significance set at P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5397080/v1/f7beea08c546ea08af1efcae.jpg"},{"id":70748830,"identity":"9717daea-073e-4c6c-b300-ca5856f10dec","added_by":"auto","created_at":"2024-12-06 09:02:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":32123958,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5397080/v1/e12f0bab-85cc-40f2-a5cc-ad8cf5f4111c.pdf"},{"id":70478122,"identity":"890a00f3-1846-4819-b332-76e6a5724fe4","added_by":"auto","created_at":"2024-12-03 14:36:30","extension":"zip","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":51323,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable.zip","url":"https://assets-eu.researchsquare.com/files/rs-5397080/v1/6c9826e2c417b6e3128a2240.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"TIMELESS as a Prognostic Biomarker and Therapeutic Target in Gastric Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) is the fifth most commonly diagnosed cancer worldwide, with over one million new cases reported annually, and it is the third leading cause of cancer-related mortality\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Surgical resection is the optimal treatment for early-stage GC, offering the best chance for a favorable outcome. For patients with inoperable tumors or those presenting with advanced metastases, chemotherapy assumes a pivotal role in management\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The early stages of GC are often asymptomatic, leading to the majority of patients being diagnosed at an advanced stage with a concomitant poor prognosis\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The efficacy of current targeted therapies is suboptimal, attributable to the heterogeneity in clinical and biobehavioral factors, as well as the emergence of multi-drug resistance (MDR) in gastric cancer cells\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The molecular mechanisms underlying tumorigenesis and disease progression in GC remain poorly characterized, which significantly hampers the development of effective treatment strategies. A deeper understanding of the genomic underpinnings of tumor invasion, metastasis, and prognosis is crucial. Such insights may yield highly sensitive therapeutic approaches and facilitate the identification of novel prognostic biomarkers and therapeutic targets, potentially transforming the landscape of GC treatment.\u003c/p\u003e \u003cp\u003eCircadian rhythms exert a pervasive influence on the behavior and physiology of eukaryotic cells, including the regulation of cellular processes that are integral to maintaining homeostasis. Oncogenes have been shown to modulate the expression of circadian rhythm genes, effectively disrupting the circadian cycle and predisposing cells to neoplastic transformation\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The interplay between cancer biology and cellular circadian rhythms is pivotal for elucidating the pathogenic mechanisms underlying cancer and for advancing therapeutic strategies. A less-recognized circadian regulator, TIMELESS, serves as a critical component of the cell cycle checkpoint system\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. These genes regulate the auto-regulatory feedback loops that govern the core mammalian circadian rhythm, as well as exerting control over the negative limb of the circadian cycle\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Intriguingly, research into the expression patterns of circadian rhythm genes in cancer has revealed that TIMELESS are frequently overexpressed in a variety of malignancies, including breast\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, colorectal\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, lung\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, and cervical cancers\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The overexpression of clock genes in cancer and their role in high-fidelity and rapid DNA synthesis suggest that these genes may contribute to the dysregulated proliferation characteristic of cancer cells.\u003c/p\u003e \u003cp\u003eIn this study, we integrated STAD datasets from GEO and TCGA and clock genes from GeneCards and NCBI to identify TIMELESS as a potential diagnostic biomarker in STAD using four machine learning algorithms. We conducted multidimensional analyses to evaluate TIMELESS genomic alterations, protein-interaction networks, and their implications for prognosis and tumor immunity. Single-gene enrichment analysis revealed a link between TIMELESS and pyroptosis, leading to the identification of key pyroptosis genes and potential binding sites with TIMELESS via molecular docking. Additionally, we identified miRNAs and lncRNAs associated with TIMELESS and performed drug sensitivity analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Our results suggest the potential for TIMELESS to serve as a novel therapeutic and diagnostic target in gastric cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCollection and pretreatment of Gastric Cancer Dataset\u003c/h2\u003e \u003cp\u003eHigh-throughput gene expression datasets for stomach adenocarcinoma (STAD) patients and corresponding controls were obtained from The Cancer Genome Atlas database (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Gene Expression Omnibus database (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gds\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gds\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The pre-processing steps for these three datasets are detailed as follows: the TCGA dataset underwent log2 transformation, data from all three datasets were merged based on shared genes, batch effect correction was applied, and the data were ultimately normalized, resulting in a consolidated dataset that includes 698 samples and 14,969 genes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClock genes acquisition\u003c/h3\u003e\n\u003cp\u003eClock genes were identified through searches utilizing the keyword \"clock gene\" in the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the National Center for Biotechnology Information (NCBI, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) repository. The GeneCards screening was conducted based on correlation scores exceeding the average threshold and the criterion of protein-coding potential, yielding a total of 1840 genes. The NCBI screening yielded 270 genes. A comprehensive list of all searched clock genes is provided in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\n\u003ch3\u003eIdentification of the differentially expressed genes in STAD\u003c/h3\u003e\n\u003cp\u003eTo identify differentially expressed genes in stomach adenocarcinoma (STAD), we employed the limma R software package (version 4.3.1) to analyze and compare the gene expression profiles between STAD tissues and their normal counterparts. Its selection criteria are \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2| \u0026gt; 0.5. The visualization of the differential expression analysis was conducted using the \u0026lsquo;gplots\u0026rsquo; and \u0026lsquo;ggplot2\u0026rsquo; packages in R, generating heatmaps and volcano plots. Ultimately, a Venn diagram analysis was applied to identify the differentially expressed clock genes among the significant findings.\u003c/p\u003e\n\u003ch3\u003eFunction enrichment analysis\u003c/h3\u003e\n\u003cp\u003eEnrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) terms was conducted using the \"clusterProfiler\" R package. This analysis encompassed Gene Ontology biological functions, including biological processes (BP), molecular functions (MF), and cellular components (CC). \u003cem\u003eP\u003c/em\u003e-values were adjusted using the Benjamini-Hochberg method to control for multiple testing, and a threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied to determine statistical significance. Based on these criteria, a core gene was selected for further Gene Set Enrichment Analysis (GSEA). GSEA was employed to explore the functional enrichments associated with the hub gene, utilizing the \"clusterProfiler\" package in R.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMachine learning-identified characteristic Clock genes.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe employed four distinct machine learning algorithms-LASSO, Random Forest (RF), XGBoost, and Neural Network (NNET) models-to identify discriminative clock genes in STAD patients. These models were implemented using the \u0026ldquo;glmnet\u0026rdquo;, \u0026ldquo;randomForest\u0026rdquo;, \u0026ldquo;xgboost\u0026rdquo;, and \u0026ldquo;neuralnet\u0026rdquo; R packages, respectively. In the LASSO model, the coefficients of the top 10 significant variables were determined based on the optimal penalty parameter λ, selected through tenfold cross-validation. The RF algorithm, utilizing 500 trees per data point, was employed to identify the top 10 variables by importance. The NNET model, a nonlinear approach, establishes gene importance rankings through multiple hidden layers and activation functions. Both XGBoost and NNET models were configured to ascertain the significance of gene sequences. The combined dataset was randomly partitioned into a training set (70%) and a validation set (30%). The outcomes from the four machine learning algorithms were intersected to identify the definitive set of discriminative clock genes. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), calculated with the \u0026ldquo;pROC\u0026rdquo; R package. Additionally, boxplots illustrating the differential expression of the intersecting genes across the combined dataset were generated using the \u0026ldquo;ggplot2\u0026rdquo; package, with statistical significance determined by the Wilcoxon test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003ch3\u003eTIMER Analysis\u003c/h3\u003e\n\u003cp\u003eTIMER (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer/\u003c/span\u003e\u003cspan address=\"https://cistrome.shinyapps.io/timer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a comprehensive and publicly accessible platform designed for systematic analysis of targeted gene expression across various tumor types\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. We leveraged TIMER to explore the correlation between TIMELESS gene expression and STAD.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration level analysis of hub genes\u003c/h2\u003e \u003cp\u003eThe CIBERSORT algorithm was employed to investigate the cellular heterogeneity of target genes within the gastric cancer immune microenvironment\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. TCGA-STAD samples were stratified into high and low-expression groups based on the median expression levels of the hub genes. Utilizing the \"CIBERSORT\" package, we quantified the proportion of 22 different types of tumor-infiltrating immune cells in both high and low-expression groups. The correlations between the hub genes and immune-infiltrating cells were evaluated using Spearman's correlation coefficients, which were computed with the R package \"ggpubr\". Finally, boxplots and forest plots were generated using the \"ggpubr\" and \"vioplot\" packages, respectively. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSurvival Analysis\u003c/h3\u003e\n\u003cp\u003eThe Kaplan-Meier database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kmplot.com/analysis/\u003c/span\u003e\u003cspan address=\"https://kmplot.com/analysis/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is used to estimate the survival probabilities of study subjects over time, following specific treatment adjustments\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Accordingly, K-M survival analysis was conducted to investigate the correlation between the expression levels of the hub gene and the survival duration of STAD patients. This analysis involved the calculation of log-rank P-values and hazard ratios. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDocking analysis of core pyroptosis-associated molecules with TIMELESS.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA set of 52 pyroptosis-associated genes was curated based on prior literature (Supplementary Table\u0026nbsp;2). We conducted a correlation analysis between TIMELESS (TIM) and the intersecting genes derived from both pyroptosis and gastric cancer differential gene sets, utilizing R and applying Spearman's coefficient to determine the strength of these correlations. The Protein Data Bank (PDB) sequences for TIMELESS and its associated genes were retrieved from the PDB website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and potential docking data were obtained from the ZDOCK website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zdock.wenglab.org/\u003c/span\u003e\u003cspan address=\"https://zdock.wenglab.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Visualization of these data was performed using PyMOL. Corresponding microRNA and long non-coding RNA (lncRNA) targeting mRNA were identified from the TargetScan database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.targetscan.org/vert_80/\u003c/span\u003e\u003cspan address=\"https://www.targetscan.org/vert_80/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the ENCORI database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rnasysu.com/encori/\u003c/span\u003e\u003cspan address=\"https://rnasysu.com/encori/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003ePrediction of potential therapeutic drugs\u003c/h3\u003e\n\u003cp\u003eExpression data were sourced from the GDSC2 database, and 189 drug response profiles were retrieved from the Cancer Drug Sensitivity Genome (GDSC) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The TCGA samples were stratified into high and low-expression groups based on the median expression levels of the hub genes. The susceptibility of the hub genes to drugs in the high- and low-expression groups was predicted using the \u0026ldquo;calcPhenotype\u0026rdquo; function within the \u0026ldquo;oncoPredict\u0026rdquo; R package. Statistical differences in drug response between the two groups were assessed using the Wilcoxon test, with a significance threshold set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Finally, data visualization was carried out using the \u0026ldquo;ggplot2\u0026rdquo; R package.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemical validation in the THPA database\u003c/h2\u003e \u003cp\u003eThe Tissue Human Protein Atlas (THPA), a publicly accessible resource funded by Sweden, comprises over five million immunohistochemically stained tissue and cell distribution data points for 26,000 human proteins. THPA facilitates the examination of both normal and STAD tissues through antibody proteomics and is frequently utilized for the validation of hub gene expression. We employed this pathology tool to assess the expression levels of TIMELESS in normal gastric mucosal tissues and STAD tissues as recorded in the THPA database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell culture\u003c/h2\u003e \u003cp\u003eThe human gastric cancer cell line AGS was cultured in RPMI 1640 medium (Gibco, USA) supplemented with 10% fetal bovine serum (FBS), 100 \u0026micro;g/mL streptomycin, and 100 U/mL penicillin. The cells were incubated at 37\u0026deg;C in a humidified chamber with an atmosphere containing 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot\u003c/h2\u003e \u003cp\u003eAGS cells were lysed using RIPA lysis buffer, and western blot analysis was conducted following established protocols. Briefly, 30 \u0026micro;g of protein from each sample was loaded and subjected to separation on a 10% SDS-polyacrylamide gel electrophoresis (SDS-PAGE) gel, followed by transfer to a polyvinylidene fluoride (PVDF) membrane. After blocking to prevent non-specific binding, the membranes were incubated with primary antibodies against TIMELESS (Proteintech) and GAPDH, along with a horseradish peroxidase (HRP)-conjugated goat anti-rabbit IgG secondary antibody. Protein bands were detected and visualized using the ChemiDoc XRS gel documentation and analysis system with a chemiluminescence kit (Beyotime, Shanghai, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analysis and visualization were performed using R software version 4.3.1. The comparison of gene expression levels between the two groups was conducted using the unpaired \u003cem\u003et\u003c/em\u003e-test as implemented in GraphPad Prism version 9 (GraphPad Software, California, USA). Results are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations. Unless otherwise indicated, a \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eIdentification and functional Enrichment of differentially expressed Clock genes in GC.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe integrated expression profiles from the TCGA, GSE27342, and GSE63089 datasets and performed batch effect removal, and standardization to compile a dataset consisting of 161 normal tissue samples and 537 stomach adenocarcinoma (STAD) tissue samples. By Limma program analysis, 2287 differentially expressed genes (DEGs) were identified (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 \u0026amp; |logFC| \u0026gt; 0.5), of which 1694 were up-regulated and 593 were down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B). Subsequently, we obtained 2110 clock genes from GeneCards and NCBI databases and identified 29 DEGs clock genes through VENN analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). GO analysis shows that, the functions of differentially expressed clock genes in STAD are mainly concentrated in the regulation of inflammatory response, cellular response to UV-A, angiogenesis, oxidative stress, interleukin-1 signaling, NNS dependent protein nuclear input, Wnt signaling bodies, NADPH oxidase complexes, NF-kappaB binding, RAGE receptor binding, and toll-like receptor Body combination and other aspects (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-F). In addition, KEGG analysis showed that Th17 cell differentiation, leukocyte transendothelial migration, reactive oxygen chemical carcinogenesis, IL-17 signaling pathway, and HIF-1 signaling pathway were closely related to these clock regulators (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). The heat map further details the relationship between enrichment function and the inclusion of each clock gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). Differential gene enrichment analysis suggested that clock genes might be involved in gastric cancer progression through regulation of validation response, oxidative stress and signal transduction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMachine learning-identified Key Clock Genes.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLASSO, RF, XGBoost, and NNET techniques were employed to identify pivotal clock genes within the gastric cancer context. In the LASSO regression analysis, the optimal λ value of 0.072 facilitated the selection of the top 10 variables (KAT2B, BHLHE41, MAOA, PER1, RB1, TIMELESS, MMP9, AHR, GHRL, HIF1A) based on the ranking of their coefficient magnitudes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The RF algorithm, integrated with feature importance assessment, delineated the relationship between the error rate, the number of classification trees, and the 29 genes, culminating in the identification of the top five influential genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Both XGBoost and NNET algorithms independently ranked the top 10 variables according to their significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D). The performance of each model was rigorously evaluated using ROC curves for both training and validation datasets. Through Venn diagram analysis, we identified two key diagnostic clock genes, TIMELESS and BHLHE41, in STAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The ROC curves for TIMELESS (AUC, 0.802) and BHLHE41 (AUC, 0.662) suggested that TIMELESS is a potential valuable diagnostic biomarker in STAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-G).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eExpression level assessment and GSEA of TIMELESS in GC.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAnalysis with the TIMER database revealed a markedly elevated TIM mRNA expression in GC tissues relative to normal controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Examination of the RNA-seq dataset from TCGA patients showed increased TIM mRNA expression across all STAD tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C). Western blot analysis confirmed these findings, demonstrating a pronounced elevation in TIM expression in gastric cancer cells compared to normal gastric mucosa cells (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Immunohistochemical staining of tissue microarrays from THPA further corroborated this, with higher TIM expression levels in gastric cancer tissues compared to normal gastric mucosal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Through the use of cBioPortal, we identified significant copy number variations within the TIM genes of interest. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF details a total of 206 mutations within the sample, spanning from 0 to 1100 alteration sites, comprising 167 missense mutations and 23 truncation mutations. The mutational burden of TIM in gastric cancer, ranking fourth, was primarily characterized by amplifications and truncal mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF-G). GSEA was performed to elucidate TIM-related functional roles and signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH-I). GO analysis revealed that TIM is predominantly linked to DNA repair signaling pathways and cellular activities, including p53 mediator signal transduction, receptor regulatory activity, nucleocytoplasmic transport activity, and mRNA cleavage. KEGG enrichment analysis highlighted significant associations between TIM and pathways implicated in DNA damage repair, NOD-like receptor signaling, p53 signaling, nucleoplasmic transport, mRNA surveillance, and drug metabolism via cytochrome P450 pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTIMLESS modulates immune cell infiltration and GC Prognosis\u003c/h2\u003e \u003cp\u003eTo further investigate the correlation between TIM expression and patient prognosis from published gastric cancer microarrays, we employed the Kaplan\u0026ndash;Meier plotter, which disclosed that TIM overexpression was significantly associated with diminished overall survival (OS) (HR 1.68, 95% CI 1.41\u0026ndash;1.99, P\u0026thinsp;=\u0026thinsp;2.7\u003csup\u003ee\u0026thinsp;\u0026minus;\u0026thinsp;09\u003c/sup\u003e) and post-progression survival (PPS) (HR 1.8, 95% CI 1.43\u0026ndash;2.28, P\u0026thinsp;=\u0026thinsp;5.7\u003csup\u003ee\u0026thinsp;\u0026minus;\u0026thinsp;07\u003c/sup\u003e) in gastric cancer patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). We further appraised the impact of TIM expression on the survival of gastric cancer patients under various confounding factors. The findings indicated that elevated TIM expression was associated with poorer OS irrespective of gender or differentiation levels of gastric cancer patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-H). TIM overexpression curtailed OS in patients who underwent surgery alone or 5-FU adjuvant therapy at both 5 and 10 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI-J). Given the intimate link between tumorigenesis, invasion, and the dynamics of the immune microenvironment, we scrutinized the immunomodulatory role of TIM in gastric cancer. We stratified 412 gastric cancer tissue samples from the TCGA into high and low TIM expression cohorts based on the median expression level. The TIM high-expression group exhibited a significantly elevated proportion of CD4 memory activated T cells, follicular helper T cells, resting NK cells, M0 macrophages, and M1 macrophages compared to the low-expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eK). Conversely, the proportion of memory B cells, regulatory Tregs, CD4 memory resting T cells, resting mast cells, and monocytes was markedly reduced in the TIM high-expression group. Spearman's rank correlation analysis revealed that TIM correlated positively with follicular helper T cells, M1 macrophages, M0 macrophages, CD4 memory activated T cells, activated mast cells, and resting NK cells, and negatively with regulatory Tregs, resting mast cells, activated NK cells, monocytes, CD4 memory resting T cells, and memory B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eL).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of TIMELESS with pyroptosis in GC\u003c/h2\u003e \u003cp\u003eGiven the significant association of TIM with molecular pathways related to inflammation and DNA damage repair, and considering pyroptosis as a cell death mechanism intricately linked with these processes, we identified 52 common pyroptosis factors from the existing literature to explore the potential molecular interplay between TIM and pyroptosis molecules. Our analysis involved the intersection of 2287 differentially expressed GC genes with pyroptosis genes, yielding 7 overlapping genes (CASP5, CASP8, IL1A, IL18, GZMB, PLCG1, BAX) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Employing Spearman's correlation analysis within the gastric cancer combined dataset, we found significant positive correlations between TIM and three of these pyroptosis genes (PLCG1, CASP8, BAX) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-D), while no significant correlations were observed for the others. Subsequent molecular docking predictions using ZDOCK, visualized with PyMOL, indicated potential interaction sites between TIM and CASP8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE-G). To further elucidate the nuanced regulation of the TIM-CASP8 interaction, we identified microRNAs and long non-coding RNAs associated with TIM and CASP8 from TargetScan and ENCORI databases (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). This comprehensive approach provides insights into the intricate regulatory network involving TIM and pyroptosis in gastric cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDrug sensitivity analysis of TIMELESS\u003c/h2\u003e \u003cp\u003eTo elucidate the influence of TIM gene expression on drug treatment sensitivity in STAD, we conducted further drug sensitivity predictions, which are graphically represented as boxplots. The analysis revealed significant disparities in drug responsiveness between patients with high TIM expression and those with low TIM expression. Patients in the high TIM expression group demonstrated enhanced sensitivity to Bortezomib, Vinorelbine, Rapamycin, Dinaciclib, Daporinad, Eg5_9814, Sepantronium, Docetaxel, Vinblastine, MG-132, and Paclitaxel, whereas patients with low TIM expression exhibited greater sensitivity to AZD8055 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Notably, among the drugs exhibiting potent efficacy in the TIM high expression group, Bortezomib stood out with the most pronounced effect on gastric cancer treatment (IC50\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This finding implies that Bortezomib, potentially targeting TIM, could serve as a more effective guide for prognostic stratification among gastric cancer patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe circadian rhythm, a fundamental biological clock, exerts a pivotal influence on the periodic fluctuations of various biological processes and behaviors\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Perturbations within the circadian regulatory framework have been identified as potential contributors to the etiology of a spectrum of cancers, notably breast, endometrial, lung, glioma, and colorectal malignancies. These perturbations modulate key oncogenic properties, including angiogenesis, apoptosis, and cellular proliferation\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Nonetheless, the definitive function of the circadian clock in the prognostic assessment and therapeutic intervention of stomach adenocarcinoma (STAD) remains a necessitating further exploration to unravel its intricate mechanisms in this context.\u003c/p\u003e \u003cp\u003eThe analysis of GC datasets within TCGA, GSE27342 and GSE63089 databases, employing four machine learning (ML) algorithms, identified TIMELESS and BHLHE41 as signature genes. Our study confirmed that TIMELESS demonstrates enhanced diagnostic accuracy in GC, with this gene being markedly overexpressed in GC tissues and cell lines at both the transcriptional and translational levels. Extant literature indicates that TIMELESS expression is elevated in gliomas, with notably higher expression in high-grade gliomas compared to low-grade tumors\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Yang et al.\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e have proposed the circadian rhythm gene TIMELESS as a candidate for comprehensive assessment and a prognostic biomarker across various cancers. The aberrant expression of TIMELESS is significantly correlated with advanced tumor stages, unfavorable prognosis, and a spectrum of immune cell infiltration within neoplasms. Collectively, these findings, alongside our results, imply that TIMELESS expression is not confined to specific tissues, suggesting a widespread influence in oncogenesis. A recently study\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e documented that elevated TIMELESS expression in tissues characterized by active proliferation, suggesting that the dysregulation of TIMELESS may be a pivotal factor in the progression and spread of GC.\u003c/p\u003e \u003cp\u003eTIMELESS is a regulator of the circadian rhythm and serves as a crucial connecting molecule that \"directly couples\" the circadian rhythm with the cell cycle\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. It directly or indirectly modulates the activity of self-regulatory components central to mammalian circadian rhythms, including the CLOCK, Per, and Cry proteins, which are associated with the S-phase replication checkpoint proteins Claspin and Tipin\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. In the context of GC, we observed that TIMELESS carries amplification and motility mutations that are likely to disrupt the biological clock and cell cycle checkpoints, resulting in the loss of circadian patterns. Furthermore, single-gene Gene Set Enrichment Analysis (GSEA) results indicated that TIMELESS was predominantly enriched in DNA damage repair signaling pathways and intracellular transcriptional activity. A characteristic of GC is a high degree of genomic instability, which is linked to the dysregulation of DNA damage repair pathways\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. The efficiency of DNA damage induction and repair has been demonstrated to be influenced by the diurnal cycle\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Defects in DNA replication can lead to mutations or replication blockages that result in chromosome breakage, rearrangement, or incorrect segregation, which are chromosomal aberrations that can precipitate cancer and a spectrum of other diseases\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Kaplan-Meier analysis revealed that elevated TIMELESS expression is significantly correlated with an adverse prognosis in GC, impacting both overall survival and post-progression survival. This association holds true regardless of sex or the degree of tumor differentiation. Notably, even patients who have undergone surgery or 5-FU treatment and exhibit TIMELESS overexpression have a poorer prognosis at the 5- and 10-year follow-ups. The intracellular circadian clock was observed to directly regulate apoptosis\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, suggesting that TIMELESS suppression could enhance the cytotoxicity of chemotherapeutic drugs, particularly those that target DNA response pathways within cancer cells\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. In vitro studies have shown that the suppression of TIMELESS in doxorubicin-treated HCT116 colon cancer cells correlates with a decrease in G2/M cell cycle arrest and heightened sensitivity to doxorubicin-induced cytotoxic effects\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Therefore, the modulation of TIMELESS protein levels may impede aberrant cell growth. These findings collectively suggest that TIMELESS may be implicated in the modulation of critical oncogenic pathways.\u003c/p\u003e \u003cp\u003eThe composition of immune cells within the tumor microenvironment (TME) significantly influences the prognosis of cancer patients\u003csup\u003e[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Tumors are categorized into three fundamental immunophenotypes based on the spatial distribution of cytotoxic immune cells within the TME: immunoinflammatory, immune exclusion, and immune desert phenotypes\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Immunoinflammatory tumors, colloquially referred to as \"hot tumors,\" are distinguished by T lymphocytes infiltration and elevated tumor mutational burden (TMB). In contrast, immunorepulsive and immune desert tumors are classified as \"cold tumors\"\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. In immunologically rejected tumors, T lymphocytes, particularly CD8\u0026thinsp;+\u0026thinsp;T cells, fail to infiltrate effectively. These \"cold tumors\" are further characterized by a low mutational load, diminished major histocompatibility complex (MHC) class I expression, and reduced PD-L1 expression. Additionally, immunosuppressive cell populations, such as T-regulatory cells (Tregs) and myeloid-derived suppressor cells (MDSCs), are prevalent in cold tumors. In our study, the TME exhibited heightened infiltration of CD4 memory-activating T cells and follicular helper T cells, alongside a reduction in Treg cell infiltration. This profile aligns more closely with the characteristics of \"hot tumors,\" that is, immunoinflammatory tumors, suggesting that GC may exhibit greater sensitivity to immune checkpoint inhibitors.\u003c/p\u003e \u003cp\u003eBased on the characteristic pro-inflammatory tendencies of immunoinflammatory tumors in GC and the regulatory role of clock genes in inflammatory responses, as identified in the enrichment analysis of differentially expressed GC genes, we hypothesize the involvement of the pyroptosis pathway in GC development. Pyroptosis is the predominant mode of cell death in inflammatory contexts. We identified seven genes through the intersection of gastric cancer differential genes and pyroptosis-associated genes. It was confirmed that CASP8, BAX, and PLCG1 were significantly positively correlated with TIMELESS. However, only CASP8 is predicted to have potential docking sites with TIMELESS. CASP8, a member of the cysteine protease family, initiates death receptor-mediated apoptosis\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Variations in the casp8 gene are associated with reduced susceptibility to various cancers, including stomach, lung, esophageal, colorectal, cervical, and breast, acting in an allelic dose-dependent manner\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Hypermethylation of the casp8 gene promoter in cancer tissue and blood samples is significantly associated with GC\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. These findings suggest that TIMELESS and CASP8 may play a role in the etiology and progression of GC by modulating pyroptosis through direct interaction.\u003c/p\u003e \u003cp\u003eTo identify targeted drugs through TIMELESS, we conducted a drug susceptibility prediction analysis, which indicated that Bortezomib had the lowest IC50 value. Bortezomib, a selective proteasome inhibitor, exerts a significant effect on various tumors, including multiple myeloma\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Bortezomib induces downregulation of telomerase reverse transcriptase expression and contributes to telomere dysfunction, playing a role in the elimination of leukemia and GC cells (BGC-823)\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. TIMELESS has a protective effect on telomeres, and its absence can lead to telomere shortening in a non-telomerase-dependent manner\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Thus, we propose a mechanistic hypothesis that Bortezomib may compromise the telomere integrity of GC cells by targeting TIMELESS, leading to the loss of repetitive DNA. Concurrently, the depletion of TIMELESS disrupts its direct regulatory interaction with CASP8, promoting the pyroptotic death of GC cells and thereby exerting anti-tumor effects.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents a multifaceted evidence base that underscores the potential of TIMELESS as a biomarker for gastric cancer and its significance in prognosticating GC outcomes. The overexpression of TIMELESS in GC may confer protection to cancer cells under replication stress. We have proposed a potential regulatory role for TIMELESS in the pyroptosis of tumor cells, specifically by targeting CASP8. With the ascendance of TIMELESS as a biomarker for cancer prognosis, the investigation of TIMELESS-targeted therapeutics in oncology assumes significant importance. Our research indicates that Bortezomib could be a promising molecular therapeutic agent targeting TIMELESS. These insights may pave the way for an immune-based antitumor strategy, potentially involving signaling pathways that modulate the tumor cell cycle or the invasive capabilities of the tumor microenvironment. Nevertheless, the ultimate objective of future research is to identify a TIMELESS-targeted therapy or drug for gastric cancer and to elucidate its mechanism of action.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWZ conceived the original idea and supervised the study. WZ, XM and QS prepared the manuscript, tables. ZL executed the data analysis. SC prepared the figures. CX undertook the literature collation work. LD paticipated in the study discussion. YW corrected the manuscript and completed the in vitro experiment. All authors have read and approved to submit the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing financial interests or personal relationships that may have an impact on the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China (Grant Nos. 82001715), Health and Family Planning Commission of Heilongjiang Provincial (Grant No. 2018286), The Fundamental Research Funds for the Provincial Universities (Grant No. 2017LCZX67). Shenzhen Science and Technology Program (No. JCYJ20220531103014033).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available in the TCGA, GEO, NCBI and GeneCards repository, [https://portal.gdc.cancer.gov/, https://www.ncbi.nlm.nih.gov/gds, https://www.ncbi.nlm.nih.gov/ and https://www.genecards.org/].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFitzmaurice C, Abate D, Abbasi N, Abbastabar H, Abd-Allah F, Abdel-Rahman O, Abdelalim A, Abdoli A, Abdollahpour I, Abdulle ASM, et al. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2017: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol. 2019;5(12):1749\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThrift AP, El-Serag HB. Burden of Gastric Cancer. Clin Gastroenterol hepatology: official Clin Pract J Am Gastroenterological Association. 2020;18(3):534\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCzabotar PE, Lessene G, Strasser A, Adams JM. Control of apoptosis by the BCL-2 protein family: implications for physiology and therapy. Nat Rev Mol Cell Biol. 2014;15(1):49\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNies AT, Magdy T, Schwab M, Zanger UM. Role of ABC transporters in fluoropyrimidine-based chemotherapy response. Adv Cancer Res. 2015;125:217\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens RG. Circadian disruption and breast cancer: from melatonin to clock genes. Epidemiol (Cambridge Mass). 2005;16(2):254\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnsal-Ka\u0026ccedil;maz K, Mullen TE, Kaufmann WK, Sancar A. Coupling of human circadian and cell cycles by the timeless protein. Mol Cell Biol. 2005;25(8):3109\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao M, Wang Y, Xiao Y, Zheng D, Zhi C, Xia X, Yuan X. Activation of the clock gene TIMELESS by H3k27 acetylation promotes colorectal cancer tumorigenesis by binding to Myosin-9. J Exp Clin Cancer Res. 2021;40(1):162.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi B, Mu L, Li Y, Xia K, Yang Y, Aman S, Ahmad B, Li S, Wu H. TIMELESS inhibits breast cancer cell invasion and metastasis by down-regulating the expression of MMP9. Cancer Cell Int. 2021;21(1):38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBianco JN, Bergoglio V, Lin YL, Pillaire MJ, Schmitz AL, Gilhodes J, Lusque A, Mazi\u0026egrave;res J, Lacroix-Triki M, Roumeliotis TI, et al. Overexpression of Claspin and Timeless protects cancer cells from replication stress in a checkpoint-independent manner. Nat Commun. 2019;10(1):910.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeilsen BK, Frodyma DE, McCall JL, Fisher KW, Lewis RE. ERK-mediated TIMELESS expression suppresses G2/M arrest in colon cancer cells. PLoS ONE. 2019;14(1):e0209224.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshida K, Sato M, Hase T, Elshazley M, Yamashita R, Usami N, Taniguchi T, Yokoi K, Nakamura S, Kondo M, et al. TIMELESS is overexpressed in lung cancer and its expression correlates with poor patient survival. Cancer Sci. 2013;104(2):171\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou X, Zhu C, Zhang L, Zhang Y, Fu F, Chen Y, Zhou J. MicroRNA-708 Suppresses Cell Proliferation and Enhances Chemosensitivity of Cervical Cancer Cells to cDDP by Negatively Targeting Timeless. OncoTargets therapy. 2020;13:225\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, Li B, Liu XS. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling Tumor Infiltrating Immune Cells with CIBERSORT. \u003cem\u003eMethods in molecular biology (Clifton, NJ)\u003c/em\u003e 2018, 1711:243\u0026ndash;259.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoel MK, Khanna P, Kishore J. Understanding survival analysis: Kaplan-Meier estimate. Int J Ayurveda Res. 2010;1(4):274\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo B, Song J, Zhang J, Han J, Zhou X, Chen L. The contribution of circadian clock to the biological processes. Front Mol Biosci. 2024;11:1387576.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSerin Y, Acar Tek N. Effect of Circadian Rhythm on Metabolic Processes and the Regulation of Energy Balance. Ann Nutr Metab. 2019;74(4):322\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Y, Zheng Y, Dai R, Gu X. Crosstalk between Circadian Rhythm Dysregulation and Tumorigenesis, Tumor Metabolism and Tumor Immune Response. Aging and disease; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang F, Chen Q. The analysis of deregulated expression of the timeless genes in gliomas. J Cancer Res Ther. 2018;14(Supplement):S708\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y, Tang X, Lin Z, Zheng T, Zhang S, Liu T, Yang X. An integrative evaluation of circadian gene TIMELESS as a pan-cancer immunological and predictive biomarker. Eur J Med Res. 2023;28(1):563.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou J, Zhang Y, Zou X, Kuai L, Wang L, Wang J, Shen F, Hu J, Zhang X, Huang Y, et al. Aberrantly Expressed Timeless Regulates Cell Proliferation and Cisplatin Efficacy in Cervical Cancer. Hum Gene Ther. 2020;31(5\u0026ndash;6):385\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffman AE, Yi CH, Zheng T, Stevens RG, Leaderer D, Zhang Y, Holford TR, Hansen J, Paulson J, Zhu Y. CLOCK in breast tumorigenesis: genetic, epigenetic, and transcriptional profiling analyses. Cancer Res. 2010;70(4):1459\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffman AE, Zheng T, Yi CH, Stevens RG, Ba Y, Zhang Y, Leaderer D, Holford T, Hansen J, Zhu Y. The core circadian gene Cryptochrome 2 influences breast cancer risk, possibly by mediating hormone signaling. Cancer Prev Res (Philadelphia Pa). 2010;3(4):539\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Wen GM, Wu CA, Jing ZL, Li DZ, Liu GL, Wei XX, Tang MS, Li YH, Zhong Y, et al. PRKDC is a prognostic marker for poor survival in gastric cancer patients and regulates DNA damage response. Pathol Res Pract. 2019;215(8):152509.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersen B, Duan J, Karri SS. How and Why the Circadian Clock Regulates Proliferation of Adult Epithelial Stem Cells. Stem Cells. 2023;41(4):319\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCopeland WC. Defects in mitochondrial DNA replication and human disease. Crit Rev Biochem Mol Biol. 2012;47(1):64\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarlow JH, Nussenzweig A. Replication initiation and genome instability: a crossroads for DNA and RNA synthesis. Cell Mol Life Sci. 2014;71(23):4545\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadiq Z, Varghese E, B\u0026uuml;sselberg D. Cisplatin's dual-effect on the circadian clock triggers proliferation and apoptosis. Neurobiol sleep circadian rhythms. 2020;9:100054.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSancar A, Lindsey-Boltz LA, Kang TH, Reardon JT, Lee JH, Ozturk N. Circadian clock control of the cellular response to DNA damage. FEBS Lett. 2010;584(12):2618\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang X, Wood PA, Hrushesky WJ. Mammalian TIMELESS is required for ATM-dependent CHK2 activation and G2/M checkpoint control. J Biol Chem. 2010;285(5):3030\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuail DF, Joyce JA. Microenvironmental regulation of tumor progression and metastasis. Nat Med. 2013;19(11):1423\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarcellos-Hoff MH, Newcomb EW, Zagzag D, Narayana A. Therapeutic targets in malignant glioblastoma microenvironment. Semin Radiat Oncol. 2009;19(3):163\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu W, Wang W, Li H, Jiao Y, Weng J, Huo R, Yan Z, Wang J, Xu H, Wang S, et al. High Dimensional Mass Cytometry Analysis Reveals Characteristics of the Immunosuppressive Microenvironment in Diffuse Astrocytomas. Front Oncol. 2020;10:78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng YQ, Wang SB, Liu JH, Jin L, Liu Y, Li CY, Su YR, Liu YR, Sang X, Wan Q, et al. Modifying the tumour microenvironment and reverting tumour cells: New strategies for treating malignant tumours. Cell Prolif. 2020;53(8):e12865.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalon J, Bruni D. Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nat Rev Drug Discovery. 2019;18(3):197\u0026ndash;218.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalvesen GS. Caspase 8: igniting the death machine. Struct (London England: 1993). 1999;7(10):R225\u0026ndash;229.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun T, Gao Y, Tan W, Ma S, Shi Y, Yao J, Guo Y, Yang M, Zhang X, Zhang Q, et al. A six-nucleotide insertion-deletion polymorphism in the CASP8 promoter is associated with susceptibility to multiple cancers. Nat Genet. 2007;39(5):605\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzarkhazin F, Tehrani GA. Detecting promoter methylation pattern of apoptotic genes Apaf1 and Caspase8 in gastric carcinoma patients undergoing chemotherapy. J Gastrointest Oncol. 2018;9(2):295\u0026ndash;302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakata W, Hayakawa Y, Nakagawa H, Sakamoto K, Kinoshita H, Takahashi R, Hirata Y, Maeda S, Koike K. Anti-tumor activity of the proteasome inhibitor bortezomib in gastric cancer. Int J Oncol. 2011;39(6):1529\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCi X, Li B, Ma X, Kong F, Zheng C, Bj\u0026ouml;rkholm M, Jia J, Xu D. Bortezomib-mediated down-regulation of telomerase and disruption of telomere homeostasis contributes to apoptosis of malignant cells. Oncotarget. 2015;6(35):38079\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGadaleta MC, Das MM, Tanizawa H, Chang YT, Noma K, Nakamura TM, Noguchi E. Swi1Timeless Prevents Repeat Instability at Fission Yeast Telomeres. PLoS Genet. 2016;12(3):e1005943.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"TIMELESS, gastric cancer, prognosis, tumor-infiltrating, pyroptosis","lastPublishedDoi":"10.21203/rs.3.rs-5397080/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5397080/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGastric cancer, a prevalent malignancy, exhibits intricate etiological and pathological characteristics. Recent insights into the dysregulation of clock genes offer novel avenues for diagnosis, treatment, and prognosis in patients with gastric cancer. Methods: This study leveraged machine learning, Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, survival prognosis analysis, drug sensitivity analysis, and in vitro experiments to elucidate the role of core clock genes in gastric cancer. Results: By integrating TCGA, GEO datasets, and NCBI database, we identified 29 differentially expressed clock genes. Utilization of four machine learning algorithms revealed TIMELESS and BHLHE41 as critical genes, with TIMELESS (AUC, 0.802) showing enhanced diagnostic potential for GC. High levels of TIMELESS expression in gastric cancer were associated with poor tumor prognosis and immune cell infiltration. We identified a targeted interaction between TIMELESS and the pyroptosis-related molecule CASP8, suggesting their collaborative involvement in gastric cancer pathogenesis. Moreover, Bortezomib was found to be a potential targeted therapy for TIMELESS in gastric cancer. Conclusion: TIMELESS emerges as a significant biomarker and therapeutic target in gastric cancer, with considerable implications for patient prognosis and treatment.\u003c/p\u003e","manuscriptTitle":"TIMELESS as a Prognostic Biomarker and Therapeutic Target in Gastric Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-03 14:36:25","doi":"10.21203/rs.3.rs-5397080/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"77b0d067-f498-42c1-8244-2272d9a535f1","owner":[],"postedDate":"December 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-06T08:53:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-03 14:36:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5397080","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5397080","identity":"rs-5397080","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}