Identification of Hub Genes Associated with the Progression and Prognosis of Gastric Cancer through Systematic Bioinformatics Analysis

Identification of Hub Genes Associated with the Progression and Prognosis of Gastric Cancer through Systematic Bioinformatics Analysis | 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 Identification of Hub Genes Associated with the Progression and Prognosis of Gastric Cancer through Systematic Bioinformatics Analysis Wei Xu, Dandan Gong, Changfeng Man, Shiqi Zhang, Xiaoyan Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4573637/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 Objective: Gastric cancer (GC) is recognized as one of the prevailing solid malignant tumors globally, with a notable rate of recurrence and metastasis. Therefore, this study utilized database mining to analyze potential key genes (hub genes) that are associated with the progression and prognosis of GC, aiming to offer new clues for the prognosis and targeted treatment for GC. Methods: This study utilized the GSE79973 dataset from the GEO to conduct DEGs in conjunction with the WGCNA. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed on disease-characteristic differentially expressed genes. In addition, a PPI network through the STRING database to screen for characteristic genes involved in the molecular mechanisms of GC. The diagnostic capabilities of these characteristic genes were ascertained through ROC curve analysis, integrating the clinical data of GC from TCGA. Results: Systematic bioinformatics analysis pinpointed four genes—COL1A1, COL1A2, COL4A1, and TLR2—as closely related to the onset and progression of GC. ROC curve revealed their robust diagnostic and prognostic capabilities for GC (AUC (COL1A1) =0.9478, AUC (COL1A2) =0.8768, AUC (COL4A1) =0.8482, AUC (TLR2) =0.8452, all P < 0.0001), presenting significant clinical translational application value. Conclusion: As newly discovered functional genes closely related to the onset and progression of GC, COL1A1, COL1A2, COL4A1, and TLR2, can be deemed as novel biomarkers for clinical diagnosis of GC, paving the way for new effective targets in the treatment of GC. gastric cancer differential gene expression hub genes WGCNA transcriptomics biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Gastric cancer (GC) is a significant global public health concern. As a malignancy of the digestive tract, its incidence and mortality remain consistently elevated on a worldwide scale. With the rapid development of molecular biology and bioinformatics, increasing studies have indicated that the onset and progression of GC are multifactorial, multistep, and complex processes involving aberrant expression and regulation of numerous genes. Currently, our understanding of the molecular mechanisms driving the progression of GC is constrained. Studies have the pivotal biological involvement of the PI3K-Akt signaling pathway in driving the disease progression, which can regulate growth, proliferation, apoptosis, and energy metabolism of tumor cells. The activated PI3K-Akt signaling pathway can foster GC progression by amplifying glycolysis, stabilizing the membrane potential of mitochondria, and suppressing the apoptosis of tumor cells[ 1 ]. To date, mounting evidence indicates that cancer is a metabolic disorder, wherein cells relinquish normal regulatory mechanisms over cell proliferation, resulting in heightened requirements for bioenergy and biosynthesis[ 2 ]. Therefore, in-depth exploration of the expression profiles and regulatory mechanisms of genes associated with GC is crucial for revealing the pathogenesis of GC, identifying new therapeutic targets, and assessing prognostic indicators. In recent years, significant progress has been made in high-throughput sequencing technologies and bioinformatics analysis methods, resulting in the accumulation and public availability of a substantial volume of GC gene expression data. These data provide us with valuable information resources for investigating GC at the genomic level. Reports have emerged regarding the association of TINAGL1 with various tumors. In GC, the upregulation of TINAGL1 boosts matrix metalloproteinases (MMPs), particularly MMP2, through the JNK signaling pathway, thereby promoting tumor progression and metastasis[ 3 , 4 ]. Many studies on GC have found that DNA damage and oxidative stress reactions may be closely related to tumor progression[ 5 ]. In addition, studies have indicated that CCNB1 is associated with GC. Targeting the hnRNPR-CCNB1/CENPF axis could be a potential therapeutic strategy for treating GC[ 6 ]. Yet, one of the current challenges in GC research is how to mine more hub genes closely tied to the occurrence and progression of GC from extensive gene expression data. Additionally, validating the reliability of mechanism analyses with in vitro or in vivo experiments to reveal their functions and regulatory mechanisms remains a significant hurdle. Bioinformatics contributes greatly to the study of GC, akin to a key that unlocks the door to profound insights into the molecular mechanisms driving the disease. By utilizing bioinformatics tools and methods, we can conduct a more comprehensive analysis of molecular alterations in GC, thereby identifying potential diagnostic biomarkers and therapeutic targets. Transcriptomics, an important subfield of bioinformatics, concentrates on the investigation of gene expression and regulation. In the field of GC, transcriptomics technology can help us obtain the gene expression profiles of GC tissues, unveiling hub genes and pathways crucial in GC onset and progression. By thoroughly investigating these hub genes and pathways, we can deepen our comprehension of GC's biological traits, thereby offering novel insights and methodologies for diagnosing and treating the disease. However, despite some existing research that has provided certain clues for the diagnosis of GC, the accuracy, stability, and specificity of these biomarkers in real-world clinical applications still require further improvement. Therefore, our study is committed to digging deeper into the transcriptomic data of GC to find those hub genes that can precisely reflect the mechanisms underlying the onset and progression of GC. The RNA sequencing data and corresponding clinical information related to GC were derived from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The Weighted Gene Co-expression Network Analysis (WGCNA) was utilized to explore the correlation between differential gene expression and GC progression. This bioinformatics method describes the correlation between genomics and clinical characteristics across different samples. In addition, KEGG pathway enrichment analysis (PEA) and protein-protein interaction (PPI) network mapping were conducted to discuss the potential mechanisms of hub genes related to GC. Finally, a group of hub genes associated with the prognosis and progression of GC was identified. The diagnostic capabilities of these hub genes for GC were clarified through receiver operating characteristic (ROC) curve analysis. We believe that these hub genes have the potential to be biomarkers for the prognosis and diagnosis of GC. Data and methods Acquisition of data This study selected microarrays of gene expression profiles from the NCBI-GEO database, using the keywords "gastric cancer, Homo sapiens". The GSE79973 dataset (sequencing platform GPL570) was included in this study, which includes samples from 10 normal gastric tissues and 10 GC tissues. In the TCGA data, information about GC samples included 410 GC patients and 36 normal controls. Differential gene screening In R (v4.3.2), the probe IDs in the platform files were first extracted and replaced with gene names. Probes not matching with specific gene symbols were excluded. In cases where different probes matched the same gene symbol, the expression value for each gene was determined by taking the mean value. Using the "limma" package in R, genes were analyzed to identify expression changes. Those with a logFC > 0.25 and a P < 0.05 were deemed upregulated. Conversely, those with a logFC < -0.25 and a P < 0.05 were deemed downregulated. In addition, significantly differentially expressed genes (DEGs) between normal gastric tissue samples and GC samples were identified. Disease characteristic gene screening WGCNA was utilized to detect co-expressed gene modules, allowing for the exploration of the relationship between phenotypes and gene networks, and the investigation of core genes within the network. The WGCNA package in R was employed to conduct WGCNA on the top 50% of genes with the highest expression variation in the GEO dataset for GC. Then, scatter plots of the scale-free fit index and average connectivity were created to determine an appropriate soft threshold. This threshold selection ensured that the constructed weighted gene co-expression network conformed to scale-free network distribution. Using the TOM matrix, highly correlated gene clusters were identified, and the plotDendroAndColors function was used to draw the dendrograms and corresponding module colors. A subset of genes was randomly selected for further analysis. Heatmaps were generated to display the expression patterns of module genes, the network relationships of selected genes, and the correlations between module traits and clinical data. Biological function prediction analysis and PPI network mapping An intersection of DEGs and genes from the WGCNA module with the strongest correlation was taken to obtain disease-characteristic differential genes. These genes were subjected to KEGG PEA using an R package (significant enrichment was deemed when P < 0.05). PPI analysis was conducted using the STRING database. A PPI network diagram encoded by differentially expressed genes was constructed by excluding isolated proteins without interaction under the screening condition of an interaction score of > 0.4. Based on biological function annotation results from KEGG and GO, the key regulatory signaling pathways potentially involved in the onset of GC by these characteristic proteins were analyzed. ROC curve plotting to predict the diagnostic capability of hub genes for GC Clinical data, including information on 410 GC patients and 36 normal controls, were derived from the TCGA database. Clinical sample information and expression matrix of hub genes in the TCGA data were extracted. ROC curves were plotted using GraphPad Prism v9 to assess the diagnostic value of these characteristic genes for GC based on their area under the curve (AUC) values. Results Data principal component analysis and differential gene identification In this study, the dataset GSE79973, encompassing high-throughput sequencing data of GC, was downloaded from the GEO database. The project comprised a total of 20 samples, including 10 GC tissues and 10 adjacent normal tissues serving as normal controls. To ensure the stability of the data, we performed principal component analysis (PCA) using the factoextra package. We identified that two control samples, GSM2109542 and GSM2109548, and one disease sample, GSM2109549, exhibited significant abnormalities. Therefore, these abnormal samples were removed, and PCA was re-performed, which then illustrated improved inter-group clustering, as depicted in Fig. 1 A. With the limma R package, the threshold for logFC was set at 0.25. Genes with a logFC > 0.25 and a P < 0.05 were deemed upregulated; those with a logFC < -0.25 and a P < 0.05 were deemed downregulated. A total of 2169 upregulated genes and 3919 downregulated genes were identified from the GSE79973 dataset, and their volcano plots are presented in Fig. 1 B. These differentials were subjected to further biological function analysis. Clarification of disease characteristic module genes through WGCNA To delve deeper into the association between genes exhibiting different expression patterns and disease characteristics, WGCNA was conducted on the top 50% of genes with the highest expression variance. The key parameter, soft threshold β, was established at 11 (Fig. 2 A) to assure the overall connectivity of the co-expression modules. Subsequently, several co-expression modules were acquired, with different colors representing gene modules shown in Fig. 2 B or Fig. 2 C. The colors in the figure indicate the distances between genes, with the depth of color indicating the magnitude of distance. Genes were arranged as per the results of hierarchical clustering, with genes belonging to the same module indicated by the same color. The correlation and significance between module eigengenes and phenotypes were calculated, and a heatmap of module correlations with trait data was drawn (Fig. 2 D), which can be used to determine if a module is related to a specific phenotype. The most highly correlated module genes, that is, the blue modules, were selected for subsequent biological function analysis, which then exhibited a positive correlation with the disease occurrence (correlation = 0.91, P = 4e-07). By intersecting DEGs with those from identified modules, 935 differentially expressed disease characteristic genes were identified. Subsequently, an in-depth analysis of these genes was conducted (Fig. 3 ) to further understand their roles and interactions in the disease process. GO and KEGG PEA KEGG PEA was conducted on the aforementioned 935 differentially expressed specific genes (Figs. 4 A and 4 B). Results showed significant enrichment in the PI3K-Akt signaling pathway, protein digestion and absorption, and cytokine-cytokine receptor interaction. These pathways are closely related to the pathogenesis of GC. The PI3K-Akt pathway plays a pivotal role in intracellular signaling, governing cellular growth, proliferation, survival, and apoptosis. In GC cells, the abnormal activation of the PI3K-Akt pathway often leads to uncontrolled cell proliferation and impaired apoptosis, thereby promoting the onset and progression of GC. Several studies have pointed out a significant association between the activation of the PI3K-Akt pathway and the malignancy and prognosis of GC. Elevated expressions of PI3K and Akt are evident in some patients with GC, and this elevation correlates positively with pathological features such as invasion depth, lymph node metastasis, and TNM staging. Moreover, PI3K-Akt pathway activation is also associated with enhanced invasive and metastatic capabilities of GC cells, further underscoring the importance of this pathway in the progression of GC. Individual genetic variations, such as single nucleotide polymorphisms (SNPs) in the coding or promoter regions of cytokines and cytokine receptors, may affect the transcription and expression of cytokines, thereby regulating the onset of GC. In one study, by examining the IL-1β and IL-8 expression levels in both GC and normal gastric mucosa tissues, researchers discovered a significant increase in their expressions within GC tissues. Their expressions were also closely associated with the malignancy and prognosis of GC. Another study explored the expression of cytokine receptors in GC cells and their relationship with angiogenesis, finding that overexpression of the IL-8 receptor in GC cells was closely associated with tumor angiogenesis. A total of 54 genes involved in these signaling pathways were further extracted for subsequent analysis. PPI network analysis PPI network diagrams were plotted using the STRING website regarding genes implicated in the PI3K-Akt, ECM-receptor interaction, protein digestion and absorption, and cytokine-cytokine receptor interaction pathways, the following results were obtained: The PPI network related to the PI3K-Akt signaling pathway included 24 nodes and 101 edges. The PPI network from genes related to ECM-receptor interaction comprised 15 nodes and 84 edges. Meanwhile, the PPI network originating from genes involved in protein digestion and absorption encompassed 20 nodes and 112 edges. The PPI network from genes involved in cytokine-cytokine receptor interaction included 17 nodes and 13 edges. Using the number of nodes as a screen criterion, 9 genes that are involved in the most nodes were identified. These genes were considered hub genes because they have a high number of nodes, indicating significant biological importance within the network. These genes were COL1A1, COL1A2, COL4A1, COL4A2, COL6A3, FN1, ITGA5, THBS1, and TLR2. Based on the biological function annotation results from KEGG and GO, these genes were analyzed for their potential involvement in key regulatory signaling pathways that may be related to the onset of GC. These pathways could involve cell proliferation, apoptosis, migration, invasion, and angiogenesis. ROC curve analysis of hub genes Data from the TCGA database, including clinical information, were downloaded to perform ROC analysis on the aforementioned 9 hub genes to evaluate their diagnostic and predictive capabilities for GC. ROC curves for these hub genes were created using GraphPad Prism 9 (Fig. 5 ). The AUC for COL1A1 was the highest at 0.9478, with a 95% confidence interval (CI) of 0.9225–0.9730, and a P less than 0.0001. The AUC for COL1A2 was 0.8768, with a 95% CI of 0.8287–0.9249, and a P less than 0.0001. The AUC for COL4A2 was 0.8482, with a 95% CI of 0.7707–0.9257, and a P less than 0.0001. The AUC for TLR2 was 0.8452, with a 95% CI of 0.7966–0.8938, and a P less than 0.0001. The COL family proteins play a complex and multifaceted role in the molecular mechanisms of GC. Firstly, COL family proteins are vital components of collagen, which provide essential structural support and participate in extracellular signaling within the extracellular matrix. During the onset and progression of GC, COL family proteins may exert influence on cellular behavior through diverse mechanisms. For instance, these genes may contribute to the GC progression by affecting biological processes like proliferation, apoptosis, invasion, and metastasis of cells. Moreover, COL family proteins may also interact with other molecules such as growth factors, receptors, or signaling transduction molecules to regulate pathways related to GC. Specifically, COL family proteins may participate in GC molecular mechanisms through the following signaling pathways, including the integrin, Wnt/β-catenin, PI3K/Akt, and TGF-β signaling pathways. The remaining 5 hub genes had AUC values ranging from 0.5392 to 0.7792, showing notably weaker diagnostic and predictive capabilities for GC compared to COL1A1, COL1A2, COL4A1, and TLR2. Therefore, it is suggested that COL1A1, COL1A2, COL4A1, and TLR2 hold significant potential as clinical biomarkers for the diagnosis and prognosis of GC. However, further validation with more clinical samples is necessary. Discussion GC is a malignant neoplasm affecting the gastrointestinal tract, characterized by a relatively high incidence and mortality. A significant proportion of patients present at an advanced stage upon diagnosis of GC, resulting in a 5-year survival rate ranging from only 11–40%[ 7 ]. Therefore, there is an urgent need for sensitive and specific biomarkers for GC detection. In this study, bioinformatics methods for analyzing hub genes and pathways showed promise for providing new insights into the diagnosis, treatment, and prognosis of GC. Based on the GSE79973 expression profile from the GEO database, we identified 935 common differentially expressed genes and conducted an in-depth analysis using R and bioinformatics tools. Using the STRING online database, 9 important regulatory genes were ultimately screened out: COL1A1, COL1A2, COL4A1, COL4A2, COL6A3, FN1, ITGA5, THBS1, and TLR2. Based on TCGA database data, COL1A1, COL1A2, COL4A1, and TLR2 were found to potentially have good prognostic value for GC, with their high expression indicating a lower survival rate for GC. Therefore, they were considered hub genes for GC with prognostic value. The PI3K signaling pathway plays a pivotal role in governing a multitude of fundamental cellular activities, encompassing proliferation, apoptosis, and migration of cells. With frequent alterations found in the PTEN/PI3K/AKT pathway in GC, this pathway is closely associated with the onset and progression of GC. The dysregulation in intricate networks comprising key proteins and signal cascades results in an imbalance between cell growth and apoptosis, thereby leading to tumor development. The PTEN/PI3K/AKT pathway is essential in determining the fundamental roles of cell death and survival. The pro-survival role of the Akt signaling pathway has been confirmed in previous studies using anti-tumor factors. Research has found that the overexpression of tumor suppressor gene growth inhibitor 3 in GC cells, in addition to inducing apoptosis, can also reduce the proliferation of G2/M phase cells and block the cell cycle. Dysfunction within the PI3K/AKT signaling pathway is implicated in this mechanism. Beyond promoting the onset of GC, abnormally activated PI3K signaling can also promote the progression of GC into a highly malignant type characterized by metastasis and chemotherapy resistance. Metastasis represents a primary cause of the recurrence and death in patients with GC and is a significant potential risk. Throughout this multistep process, many cell biological activities are controlled, including adhesion, migration, invasion, and angiogenesis. The onset, progression, invasion, and metastasis of malignant tumors are frequently accompanied by alterations in the expression profiles of the extracellular matrix (ECM) and cell-surface receptors[ 8 ]. Collagen constitutes a prominent constituent of the ECM, serving as both an attachment site and a scaffold for cellular growth. It is able to induce proliferation, differentiation and migration of epithelial cells. Additionally, it holds significance in maintaining cell-cell adhesion, tissue integrity, and regeneration[ 9 ]. COL1A1 is a major component of collagen I; it is found in most connective tissues including cartilage. Some studies have shown that the overexpression of COL1A1 is associated with poor clinical outcomes, tumor aggressiveness, and metastasis. Furthermore, studies have demonstrated that GC cells synthesize and secrete COL1 at a rate markedly faster than fibroblasts. In addition, research has discovered that the α1 chain of type I collagen (COL1A1) activates the Rac1-GTP, p-JNK, and RhoA-GTP pathways through the WNT/planar cell polarity pathway. RhoGTPases and the JNK pathway relay signals from the cell surface frizzled gene and ROR2/RYK co-receptor to the cell nucleus, a crucial process in the metastasis of tumor cells[ 10 ]. Chemotherapy is deemed the cornerstone of treatment for patients with metastatic GC. In addition to standard first-line and second-line treatments, further chemotherapy results have not effectively prolonged the survival of patients. Hence, there exists a clinical imperative to explore novel therapeutic approaches to manage the progression of GC and improve the prognosis for patients with metastatic GC. Apatinib significantly contributes to various tumor treatments owing to its favorable side effects and enhanced structure. However, a primary practical limitation of anti-angiogenic treatment strategies is the inevitable development of drug resistance[ 11 ]. Yet, the molecular mechanisms underlying apatinib resistance against GC remain unclear. As the most abundant matrix protein in the tumor stroma, COL1 can promote tumor progression by fostering cancer cell growth, invasion, metastasis, and resistance to anti-tumor drugs[ 12 ]. The interaction between COL1 and resistance against cisplatin in ovarian cancer has been confirmed by KW von Rekowski et al[ 13 ]. Moreover, KEGG PEA showed that COL1A1 regulates carboplatin resistance in ovarian cancer cells via "ECM-receptor interaction" and "focal adhesion" pathways[ 14 ]. Thus, COL1A1 may contribute to the metastatic process of GC. Prior research has revealed that the COL1A2 gene influences the proliferation, differentiation, adhesion, and metastasis of cells by encoding the most abundantly expressed COL1 in the fibrillar collagen family. Studies have indicated that the COL1A2 overexpression in GC is attributed to the coordination between EP300 and TWIST1[ 15 ]. In addition, a similar mechanism has been noted in multiple myeloma cells, where the proliferation of cancer cells is promoted by the SP1/EP300 complex through the regulation of the IQGAP1 transcription[ 16 ]. Type IV collagen α1 (COL4A1) significantly contributes to tumor invasion by inducing tumor budding. Heterotrimers, comprising COL4A1 and COL4A2, represent one of the predominant constituents in nearly all basement membranes. Therefore, COL4A1 or COL4A2 mutations are multifunctional[ 17 ], yet the molecular mechanism of COL4A2 in GC remains elusive. Endothelial TLR2 promotes angiogenesis by recruiting pro-angiogenic immune cells and assumes a pathological role in pro-inflammatory diseases. Fibroblasts that are involved in cancers serve as pivotal factors in their malignant progression, with COL6A3 primarily expressed in these cells. Knockout experiments have validated the involvement of COL6A3 in the proliferation and invasion of CRC cells[ 18 ]. Gene ontology (GO) annotations showed that co-expressed genes of FN1 are mainly involved in the organization of extracellular structures, metabolism of collagens, integrin-mediated signaling pathways, migration of substrate-dependent cells, peptide cross-linking, artery agenesis, and the migration of muscle cells[ 19 ]. Furthermore, genomics studies have suggested the involvement of FN1 in alterations of GC immune checkpoints and macrophage biomarkers[ 19 ]. Within the tumor microenvironment, immune cell infiltration has been shown to exert a pivotal impact on the progression of cancers[ 20 , 21 ]. FN1 expression is essential in the invasion and migration of tumors. Studies have demonstrated a correlation between the expression of FN1 and the extent of GC-related immune infiltration. The expression of FN1 exhibits a close association with tumor-infiltrating immune cells, such as macrophages, Treg cells, NK cells, CD8 + T cells, and dendritic cells[ 22 , 23 ]. Integrin subunit α 5 (ITGA5) primarily participates in "integrin-mediated signaling pathway", "leukocyte migration", and "cell-substrate adhesion"[ 24 ]. Thrombospondin 1 (THBS1) promotes metastasis by inducing the exhaustion of cytotoxic T cells and disrupting vascularization[ 25 ]. This study, through GEO and TCGA data mining, identified differential expression of COL1A1, COL1A2, COL4A1, and TLR2 in tumor and non-tumor tissues, which is intricately linked to the onset and progression of GC. Moreover, the ROC curve analysis has confirmed its biological significance as a major advantage in prognostic prediction for GC. These investigations have also, for the first time, unveiled the prognostic value of these four genes in patients with GC, offering novel avenues for subsequent disease diagnosis and treatment research. Inevitably, our study has certain limitations. To begin with, as a retrospective study, additional prospective studies are required to validate our findings. Secondly, further comprehensive experiments are needed to validate the reliability of the mechanism analysis. Therefore, extensive studies will carried out in the future to elucidate the mechanistic relevance of these genes to the progression and prognosis of GC. Conclusions In conclusion, by means of a series of bioinformatics analyses, four potential hub genes associated with the progression and prognosis of GC have been pinpointed. As per the results obtained, the involvement of collagen family proteins in the PI3K-Akt signaling pathway is believed to be significant in the pathogenesis of GC. Our findings not only provide valuable insights for GC biomarkers selection but also present novel targets for the treatment of GC. Declarations Funding This work was supported by Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX23_2104); China International Medical Exchange Fund(Z-2014-06-2104), Zhenjiang 169 Talent Project Scientific Research Project (2021-169-XX-19), Zhenjiang Key Research and Development Fund (SH2022032, SH2021090), Jiangsu Maternal and Child Health Care Project(F20232). The fund was not involved in any study design, data collection, analysis and interpretation, report writing, and article submission for publication. Author Contributions Wei Xu: Conceptualization, Formal analysis, Investigation, Writing - original draft preparation. Dandan Gong: Conceptualization, Formal analysis, Writing - review and editing . Changfeng Man: Conceptualization, Formal analysis, Writing - review and editing. Shiqi Zhang: Conceptualization, Resources, Methodology, Writing - review and editing. Xiaoyan Wang: Conceptualization, Formal analysis, Writing - review and editing. Yu Fan: Writing - original draft preparation, Writing - review and editing, Methodology, Funding acquisition, Supervision. All authors read and approved the final manuscript. Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval All the data were obtained from an open-access database, and therefore no approval was needed from the Medical Ethics Committee. Consent to participate Not applicable. Consent to publish Not applicable. References Yu Z, Liang C, Tu H, Qiu S, Dong X, Zhang Y, et al. Common Core Genes Play Vital Roles in Gastric Cancer With Different Stages. Front Genet. 2022;13:881948. 10.3389/fgene.2022.881948 . Xu L, Chen J, Jia L, Chen X, Awaleh Moumin F, Cai J. SLC1A3 promotes gastric cancer progression via the PI3K/AKT signalling pathway. J Cell Mol Med. 2020;24(24):14392–404. 10.1111/jcmm.16060 . Lee D, Ham IH, Oh HJ, Lee DM, Yoon JH, Son SY, et al. 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THBS1-producing tumor-infiltrating monocyte-like cells contribute to immunosuppression and metastasis in colorectal cancer. Nat Commun. 2023;14(1):5534. 10.1038/s41467-023-41095-y . Additional Declarations No competing interests reported. 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-4573637","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":326048260,"identity":"624c876f-807c-4b65-ae87-e40b744bd7c2","order_by":0,"name":"Wei Xu","email":"","orcid":"","institution":"Affiliated People's Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Xu","suffix":""},{"id":326048261,"identity":"032d858b-f50f-4f69-860a-d5c2e9bea390","order_by":1,"name":"Dandan Gong","email":"","orcid":"","institution":"Affiliated People's Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Gong","suffix":""},{"id":326048262,"identity":"203fd1eb-b1d1-4f03-9ef8-977cad7dc085","order_by":2,"name":"Changfeng Man","email":"","orcid":"","institution":"Affiliated People's Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Changfeng","middleName":"","lastName":"Man","suffix":""},{"id":326048263,"identity":"5a531ba3-15c1-4bca-bf4f-4cfa58617db1","order_by":3,"name":"Shiqi Zhang","email":"","orcid":"","institution":"The Affiliated Suqian First People's Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shiqi","middleName":"","lastName":"Zhang","suffix":""},{"id":326048264,"identity":"f194212d-d8cc-45da-ad02-401c02400a94","order_by":4,"name":"Xiaoyan Wang","email":"","orcid":"","institution":"The Affiliated Suqian First People's Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Wang","suffix":""},{"id":326048265,"identity":"faad6a24-e030-4f2b-9203-e7471c73028e","order_by":5,"name":"Yu Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYFACxgcHPvxgq+dnYEgA8piJ0cJseHBmD1+CZAMJWowP87DJJRgcgPAIazC4kcxwgIfHLM/4RsLjDwwV1okN7GcPENYiYZFWbHYjIU2C4Ux6YgNPXgIBLfkHDhjwHGPcBtTCwNh2OLFBgseAsC0JbP8ZN89ISP7A+I9YLQfY2BI3SCQkSDA2EKFF8sxjhoONPWzGEmcepEkkHEs3buPJwa+F73gy8+c/P9jk+Ntzkj98qLGW7Wc/g1+LwgE4kycBHJlseNUDgXwDnMl+AKeqUTAKRsEoGNkAAHAJS8jeb1frAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated People's Hospital of Jiangsu University","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2024-06-13 05:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4573637/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4573637/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60321302,"identity":"50adb5e0-0961-4c00-9880-387fa229ee1d","added_by":"auto","created_at":"2024-07-15 14:11:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":146747,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Principal component analysis (PCA). Each point in the PCA plot represents a sample, and the distance between samples reflects the differences. After correction, significant stratification was observed between gastric cancer and paracancerous tissue samples, with significant differences between groups. (B) Volcano plot. Each colored point represents a differentially expressed gene based on the following criteria: P\u0026lt;0.05 and |logFC|\u0026gt;0.25; pink: up-regulated genes, green: down-regulated genes, black: genes with no difference.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4573637/v1/129e5a9fe8c778203b3c7f0d.png"},{"id":60320762,"identity":"627f0165-44eb-4f5d-aed9-0216b99461f8","added_by":"auto","created_at":"2024-07-15 14:03:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":254490,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Left: Scatter plot of fitting index and power value; Right: Scatter plot of average connectivity and power value. (B) Hierarchical clustering tree of genes and corresponding module colors.(C) Network heatmap of selected genes. (D) Heatmap of correlations between modules and trait data.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4573637/v1/11ffb596d7de1de67bf7b8a4.png"},{"id":60320761,"identity":"ac36f119-f96f-4ce7-94ec-1c983867a335","added_by":"auto","created_at":"2024-07-15 14:03:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43570,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram of differentially expressed genes.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4573637/v1/b996b481f923ce4dd706c961.png"},{"id":60320764,"identity":"5fc639da-d39c-4e9c-91d3-1bc37f2330a3","added_by":"auto","created_at":"2024-07-15 14:03:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":187897,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Histogram of KEGG enrichment analysis of differentially expressed genes. The abscissa indicates the number, the ordinate indicates the entry names, and the strip color from blue to red indicates that the P value gradually decreases. (B) Bubble diagram of KEGG enrichment analysis of differentially expressed genes. The abscissa represents the ratio of the number of differentially expressed genes to the number of genes enriched for this entry, the ordinate represents the entry names. The strip color from blue to red indicates that the P value gradually decreases , and the size of the bubble indicates its number.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4573637/v1/feef1dfd84c41c87e6bf38a6.png"},{"id":60321303,"identity":"323fd716-0118-4c26-a8e2-833281d0b80f","added_by":"auto","created_at":"2024-07-15 14:11:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":535536,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network diagram. Nodes represent proteins, and edges mean protein interactions\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4573637/v1/d2da8c3ebeff2fd2dc9a1c54.png"},{"id":60320765,"identity":"268ace2f-4379-456d-b0dc-211f6c982385","added_by":"auto","created_at":"2024-07-15 14:03:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":139747,"visible":true,"origin":"","legend":"\u003cp\u003e(A-I) ROC curve. AUC: the area under the curve; 95%CI: 95% confidence interval.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4573637/v1/839ae7d674697b8219181660.png"},{"id":64662732,"identity":"82a0e26a-dc41-4774-8c00-a0e1382a4946","added_by":"auto","created_at":"2024-09-17 08:21:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2195199,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4573637/v1/6d565317-8a9c-4bec-bb93-2fafb2841e2a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Hub Genes Associated with the Progression and Prognosis of Gastric Cancer through Systematic Bioinformatics Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) is a significant global public health concern. As a malignancy of the digestive tract, its incidence and mortality remain consistently elevated on a worldwide scale. With the rapid development of molecular biology and bioinformatics, increasing studies have indicated that the onset and progression of GC are multifactorial, multistep, and complex processes involving aberrant expression and regulation of numerous genes. Currently, our understanding of the molecular mechanisms driving the progression of GC is constrained. Studies have the pivotal biological involvement of the PI3K-Akt signaling pathway in driving the disease progression, which can regulate growth, proliferation, apoptosis, and energy metabolism of tumor cells. The activated PI3K-Akt signaling pathway can foster GC progression by amplifying glycolysis, stabilizing the membrane potential of mitochondria, and suppressing the apoptosis of tumor cells[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. To date, mounting evidence indicates that cancer is a metabolic disorder, wherein cells relinquish normal regulatory mechanisms over cell proliferation, resulting in heightened requirements for bioenergy and biosynthesis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, in-depth exploration of the expression profiles and regulatory mechanisms of genes associated with GC is crucial for revealing the pathogenesis of GC, identifying new therapeutic targets, and assessing prognostic indicators.\u003c/p\u003e \u003cp\u003eIn recent years, significant progress has been made in high-throughput sequencing technologies and bioinformatics analysis methods, resulting in the accumulation and public availability of a substantial volume of GC gene expression data. These data provide us with valuable information resources for investigating GC at the genomic level. Reports have emerged regarding the association of TINAGL1 with various tumors. In GC, the upregulation of TINAGL1 boosts matrix metalloproteinases (MMPs), particularly MMP2, through the JNK signaling pathway, thereby promoting tumor progression and metastasis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Many studies on GC have found that DNA damage and oxidative stress reactions may be closely related to tumor progression[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition, studies have indicated that CCNB1 is associated with GC. Targeting the hnRNPR-CCNB1/CENPF axis could be a potential therapeutic strategy for treating GC[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Yet, one of the current challenges in GC research is how to mine more hub genes closely tied to the occurrence and progression of GC from extensive gene expression data. Additionally, validating the reliability of mechanism analyses with in vitro or in vivo experiments to reveal their functions and regulatory mechanisms remains a significant hurdle.\u003c/p\u003e \u003cp\u003eBioinformatics contributes greatly to the study of GC, akin to a key that unlocks the door to profound insights into the molecular mechanisms driving the disease. By utilizing bioinformatics tools and methods, we can conduct a more comprehensive analysis of molecular alterations in GC, thereby identifying potential diagnostic biomarkers and therapeutic targets. Transcriptomics, an important subfield of bioinformatics, concentrates on the investigation of gene expression and regulation. In the field of GC, transcriptomics technology can help us obtain the gene expression profiles of GC tissues, unveiling hub genes and pathways crucial in GC onset and progression. By thoroughly investigating these hub genes and pathways, we can deepen our comprehension of GC's biological traits, thereby offering novel insights and methodologies for diagnosing and treating the disease.\u003c/p\u003e \u003cp\u003eHowever, despite some existing research that has provided certain clues for the diagnosis of GC, the accuracy, stability, and specificity of these biomarkers in real-world clinical applications still require further improvement. Therefore, our study is committed to digging deeper into the transcriptomic data of GC to find those hub genes that can precisely reflect the mechanisms underlying the onset and progression of GC. The RNA sequencing data and corresponding clinical information related to GC were derived from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The Weighted Gene Co-expression Network Analysis (WGCNA) was utilized to explore the correlation between differential gene expression and GC progression. This bioinformatics method describes the correlation between genomics and clinical characteristics across different samples. In addition, KEGG pathway enrichment analysis (PEA) and protein-protein interaction (PPI) network mapping were conducted to discuss the potential mechanisms of hub genes related to GC. Finally, a group of hub genes associated with the prognosis and progression of GC was identified. The diagnostic capabilities of these hub genes for GC were clarified through receiver operating characteristic (ROC) curve analysis. We believe that these hub genes have the potential to be biomarkers for the prognosis and diagnosis of GC.\u003c/p\u003e"},{"header":"Data and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition of data\u003c/h2\u003e \u003cp\u003eThis study selected microarrays of gene expression profiles from the NCBI-GEO database, using the keywords \"gastric cancer, Homo sapiens\". The GSE79973 dataset (sequencing platform GPL570) was included in this study, which includes samples from 10 normal gastric tissues and 10 GC tissues. In the TCGA data, information about GC samples included 410 GC patients and 36 normal controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene screening\u003c/h2\u003e \u003cp\u003eIn R (v4.3.2), the probe IDs in the platform files were first extracted and replaced with gene names. Probes not matching with specific gene symbols were excluded. In cases where different probes matched the same gene symbol, the expression value for each gene was determined by taking the mean value. Using the \"limma\" package in R, genes were analyzed to identify expression changes. Those with a logFC\u0026thinsp;\u0026gt;\u0026thinsp;0.25 and a \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were deemed upregulated. Conversely, those with a logFC \u0026lt; -0.25 and a \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were deemed downregulated. In addition, significantly differentially expressed genes (DEGs) between normal gastric tissue samples and GC samples were identified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDisease characteristic gene screening\u003c/h2\u003e \u003cp\u003eWGCNA was utilized to detect co-expressed gene modules, allowing for the exploration of the relationship between phenotypes and gene networks, and the investigation of core genes within the network. The WGCNA package in R was employed to conduct WGCNA on the top 50% of genes with the highest expression variation in the GEO dataset for GC. Then, scatter plots of the scale-free fit index and average connectivity were created to determine an appropriate soft threshold. This threshold selection ensured that the constructed weighted gene co-expression network conformed to scale-free network distribution. Using the TOM matrix, highly correlated gene clusters were identified, and the plotDendroAndColors function was used to draw the dendrograms and corresponding module colors. A subset of genes was randomly selected for further analysis. Heatmaps were generated to display the expression patterns of module genes, the network relationships of selected genes, and the correlations between module traits and clinical data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBiological function prediction analysis and PPI network mapping\u003c/h2\u003e \u003cp\u003eAn intersection of DEGs and genes from the WGCNA module with the strongest correlation was taken to obtain disease-characteristic differential genes. These genes were subjected to KEGG PEA using an R package (significant enrichment was deemed when \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). PPI analysis was conducted using the STRING database. A PPI network diagram encoded by differentially expressed genes was constructed by excluding isolated proteins without interaction under the screening condition of an interaction score of \u0026gt;\u0026thinsp;0.4. Based on biological function annotation results from KEGG and GO, the key regulatory signaling pathways potentially involved in the onset of GC by these characteristic proteins were analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eROC curve plotting to predict the diagnostic capability of hub genes for GC\u003c/h2\u003e \u003cp\u003eClinical data, including information on 410 GC patients and 36 normal controls, were derived from the TCGA database. Clinical sample information and expression matrix of hub genes in the TCGA data were extracted. ROC curves were plotted using GraphPad Prism v9 to assess the diagnostic value of these characteristic genes for GC based on their area under the curve (AUC) values.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData principal component analysis and differential gene identification\u003c/h2\u003e \u003cp\u003eIn this study, the dataset GSE79973, encompassing high-throughput sequencing data of GC, was downloaded from the GEO database. The project comprised a total of 20 samples, including 10 GC tissues and 10 adjacent normal tissues serving as normal controls. To ensure the stability of the data, we performed principal component analysis (PCA) using the factoextra package. We identified that two control samples, GSM2109542 and GSM2109548, and one disease sample, GSM2109549, exhibited significant abnormalities. Therefore, these abnormal samples were removed, and PCA was re-performed, which then illustrated improved inter-group clustering, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. With the limma R package, the threshold for logFC was set at 0.25. Genes with a logFC\u0026thinsp;\u0026gt;\u0026thinsp;0.25 and a \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were deemed upregulated; those with a logFC \u0026lt; -0.25 and a \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were deemed downregulated. A total of 2169 upregulated genes and 3919 downregulated genes were identified from the GSE79973 dataset, and their volcano plots are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB. These differentials were subjected to further biological function analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eClarification of disease characteristic module genes through WGCNA\u003c/h2\u003e \u003cp\u003eTo delve deeper into the association between genes exhibiting different expression patterns and disease characteristics, WGCNA was conducted on the top 50% of genes with the highest expression variance. The key parameter, soft threshold β, was established at 11 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) to assure the overall connectivity of the co-expression modules. Subsequently, several co-expression modules were acquired, with different colors representing gene modules shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB or Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC. The colors in the figure indicate the distances between genes, with the depth of color indicating the magnitude of distance. Genes were arranged as per the results of hierarchical clustering, with genes belonging to the same module indicated by the same color. The correlation and significance between module eigengenes and phenotypes were calculated, and a heatmap of module correlations with trait data was drawn (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), which can be used to determine if a module is related to a specific phenotype. The most highly correlated module genes, that is, the blue modules, were selected for subsequent biological function analysis, which then exhibited a positive correlation with the disease occurrence (correlation\u0026thinsp;=\u0026thinsp;0.91, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4e-07). By intersecting DEGs with those from identified modules, 935 differentially expressed disease characteristic genes were identified. Subsequently, an in-depth analysis of these genes was conducted (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) to further understand their roles and interactions in the disease process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGO and KEGG PEA\u003c/h2\u003e \u003cp\u003eKEGG PEA was conducted on the aforementioned 935 differentially expressed specific genes (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Results showed significant enrichment in the PI3K-Akt signaling pathway, protein digestion and absorption, and cytokine-cytokine receptor interaction. These pathways are closely related to the pathogenesis of GC. The PI3K-Akt pathway plays a pivotal role in intracellular signaling, governing cellular growth, proliferation, survival, and apoptosis. In GC cells, the abnormal activation of the PI3K-Akt pathway often leads to uncontrolled cell proliferation and impaired apoptosis, thereby promoting the onset and progression of GC. Several studies have pointed out a significant association between the activation of the PI3K-Akt pathway and the malignancy and prognosis of GC. Elevated expressions of PI3K and Akt are evident in some patients with GC, and this elevation correlates positively with pathological features such as invasion depth, lymph node metastasis, and TNM staging. Moreover, PI3K-Akt pathway activation is also associated with enhanced invasive and metastatic capabilities of GC cells, further underscoring the importance of this pathway in the progression of GC. Individual genetic variations, such as single nucleotide polymorphisms (SNPs) in the coding or promoter regions of cytokines and cytokine receptors, may affect the transcription and expression of cytokines, thereby regulating the onset of GC. In one study, by examining the IL-1β and IL-8 expression levels in both GC and normal gastric mucosa tissues, researchers discovered a significant increase in their expressions within GC tissues. Their expressions were also closely associated with the malignancy and prognosis of GC. Another study explored the expression of cytokine receptors in GC cells and their relationship with angiogenesis, finding that overexpression of the IL-8 receptor in GC cells was closely associated with tumor angiogenesis. A total of 54 genes involved in these signaling pathways were further extracted for subsequent analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePPI network analysis\u003c/h2\u003e \u003cp\u003ePPI network diagrams were plotted using the STRING website regarding genes implicated in the PI3K-Akt, ECM-receptor interaction, protein digestion and absorption, and cytokine-cytokine receptor interaction pathways, the following results were obtained: The PPI network related to the PI3K-Akt signaling pathway included 24 nodes and 101 edges. The PPI network from genes related to ECM-receptor interaction comprised 15 nodes and 84 edges. Meanwhile, the PPI network originating from genes involved in protein digestion and absorption encompassed 20 nodes and 112 edges. The PPI network from genes involved in cytokine-cytokine receptor interaction included 17 nodes and 13 edges. Using the number of nodes as a screen criterion, 9 genes that are involved in the most nodes were identified. These genes were considered hub genes because they have a high number of nodes, indicating significant biological importance within the network. These genes were COL1A1, COL1A2, COL4A1, COL4A2, COL6A3, FN1, ITGA5, THBS1, and TLR2. Based on the biological function annotation results from KEGG and GO, these genes were analyzed for their potential involvement in key regulatory signaling pathways that may be related to the onset of GC. These pathways could involve cell proliferation, apoptosis, migration, invasion, and angiogenesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eROC curve analysis of hub genes\u003c/h2\u003e \u003cp\u003eData from the TCGA database, including clinical information, were downloaded to perform ROC analysis on the aforementioned 9 hub genes to evaluate their diagnostic and predictive capabilities for GC. ROC curves for these hub genes were created using GraphPad Prism 9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The AUC for COL1A1 was the highest at 0.9478, with a 95% confidence interval (CI) of 0.9225\u0026ndash;0.9730, and a \u003cem\u003eP\u003c/em\u003e less than 0.0001. The AUC for COL1A2 was 0.8768, with a 95% CI of 0.8287\u0026ndash;0.9249, and a \u003cem\u003eP\u003c/em\u003e less than 0.0001. The AUC for COL4A2 was 0.8482, with a 95% CI of 0.7707\u0026ndash;0.9257, and a \u003cem\u003eP\u003c/em\u003e less than 0.0001. The AUC for TLR2 was 0.8452, with a 95% CI of 0.7966\u0026ndash;0.8938, and a \u003cem\u003eP\u003c/em\u003e less than 0.0001. The COL family proteins play a complex and multifaceted role in the molecular mechanisms of GC. Firstly, COL family proteins are vital components of collagen, which provide essential structural support and participate in extracellular signaling within the extracellular matrix. During the onset and progression of GC, COL family proteins may exert influence on cellular behavior through diverse mechanisms. For instance, these genes may contribute to the GC progression by affecting biological processes like proliferation, apoptosis, invasion, and metastasis of cells. Moreover, COL family proteins may also interact with other molecules such as growth factors, receptors, or signaling transduction molecules to regulate pathways related to GC. Specifically, COL family proteins may participate in GC molecular mechanisms through the following signaling pathways, including the integrin, Wnt/β-catenin, PI3K/Akt, and TGF-β signaling pathways. The remaining 5 hub genes had AUC values ranging from 0.5392 to 0.7792, showing notably weaker diagnostic and predictive capabilities for GC compared to COL1A1, COL1A2, COL4A1, and TLR2. Therefore, it is suggested that COL1A1, COL1A2, COL4A1, and TLR2 hold significant potential as clinical biomarkers for the diagnosis and prognosis of GC. However, further validation with more clinical samples is necessary.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGC is a malignant neoplasm affecting the gastrointestinal tract, characterized by a relatively high incidence and mortality. A significant proportion of patients present at an advanced stage upon diagnosis of GC, resulting in a 5-year survival rate ranging from only 11\u0026ndash;40%[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, there is an urgent need for sensitive and specific biomarkers for GC detection. In this study, bioinformatics methods for analyzing hub genes and pathways showed promise for providing new insights into the diagnosis, treatment, and prognosis of GC. Based on the GSE79973 expression profile from the GEO database, we identified 935 common differentially expressed genes and conducted an in-depth analysis using R and bioinformatics tools. Using the STRING online database, 9 important regulatory genes were ultimately screened out: COL1A1, COL1A2, COL4A1, COL4A2, COL6A3, FN1, ITGA5, THBS1, and TLR2. Based on TCGA database data, COL1A1, COL1A2, COL4A1, and TLR2 were found to potentially have good prognostic value for GC, with their high expression indicating a lower survival rate for GC. Therefore, they were considered hub genes for GC with prognostic value.\u003c/p\u003e \u003cp\u003eThe PI3K signaling pathway plays a pivotal role in governing a multitude of fundamental cellular activities, encompassing proliferation, apoptosis, and migration of cells. With frequent alterations found in the PTEN/PI3K/AKT pathway in GC, this pathway is closely associated with the onset and progression of GC. The dysregulation in intricate networks comprising key proteins and signal cascades results in an imbalance between cell growth and apoptosis, thereby leading to tumor development. The PTEN/PI3K/AKT pathway is essential in determining the fundamental roles of cell death and survival. The pro-survival role of the Akt signaling pathway has been confirmed in previous studies using anti-tumor factors. Research has found that the overexpression of tumor suppressor gene growth inhibitor 3 in GC cells, in addition to inducing apoptosis, can also reduce the proliferation of G2/M phase cells and block the cell cycle. Dysfunction within the PI3K/AKT signaling pathway is implicated in this mechanism. Beyond promoting the onset of GC, abnormally activated PI3K signaling can also promote the progression of GC into a highly malignant type characterized by metastasis and chemotherapy resistance. Metastasis represents a primary cause of the recurrence and death in patients with GC and is a significant potential risk. Throughout this multistep process, many cell biological activities are controlled, including adhesion, migration, invasion, and angiogenesis.\u003c/p\u003e \u003cp\u003eThe onset, progression, invasion, and metastasis of malignant tumors are frequently accompanied by alterations in the expression profiles of the extracellular matrix (ECM) and cell-surface receptors[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Collagen constitutes a prominent constituent of the ECM, serving as both an attachment site and a scaffold for cellular growth. It is able to induce proliferation, differentiation and migration of epithelial cells. Additionally, it holds significance in maintaining cell-cell adhesion, tissue integrity, and regeneration[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. COL1A1 is a major component of collagen I; it is found in most connective tissues including cartilage. Some studies have shown that the overexpression of COL1A1 is associated with poor clinical outcomes, tumor aggressiveness, and metastasis. Furthermore, studies have demonstrated that GC cells synthesize and secrete COL1 at a rate markedly faster than fibroblasts.\u003c/p\u003e \u003cp\u003eIn addition, research has discovered that the α1 chain of type I collagen (COL1A1) activates the Rac1-GTP, p-JNK, and RhoA-GTP pathways through the WNT/planar cell polarity pathway. RhoGTPases and the JNK pathway relay signals from the cell surface frizzled gene and ROR2/RYK co-receptor to the cell nucleus, a crucial process in the metastasis of tumor cells[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Chemotherapy is deemed the cornerstone of treatment for patients with metastatic GC. In addition to standard first-line and second-line treatments, further chemotherapy results have not effectively prolonged the survival of patients. Hence, there exists a clinical imperative to explore novel therapeutic approaches to manage the progression of GC and improve the prognosis for patients with metastatic GC. Apatinib significantly contributes to various tumor treatments owing to its favorable side effects and enhanced structure. However, a primary practical limitation of anti-angiogenic treatment strategies is the inevitable development of drug resistance[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Yet, the molecular mechanisms underlying apatinib resistance against GC remain unclear. As the most abundant matrix protein in the tumor stroma, COL1 can promote tumor progression by fostering cancer cell growth, invasion, metastasis, and resistance to anti-tumor drugs[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The interaction between COL1 and resistance against cisplatin in ovarian cancer has been confirmed by KW von Rekowski et al[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, KEGG PEA showed that COL1A1 regulates carboplatin resistance in ovarian cancer cells via \"ECM-receptor interaction\" and \"focal adhesion\" pathways[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Thus, COL1A1 may contribute to the metastatic process of GC. Prior research has revealed that the COL1A2 gene influences the proliferation, differentiation, adhesion, and metastasis of cells by encoding the most abundantly expressed COL1 in the fibrillar collagen family. Studies have indicated that the COL1A2 overexpression in GC is attributed to the coordination between EP300 and TWIST1[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition, a similar mechanism has been noted in multiple myeloma cells, where the proliferation of cancer cells is promoted by the SP1/EP300 complex through the regulation of the IQGAP1 transcription[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Type IV collagen α1 (COL4A1) significantly contributes to tumor invasion by inducing tumor budding. Heterotrimers, comprising COL4A1 and COL4A2, represent one of the predominant constituents in nearly all basement membranes. Therefore, COL4A1 or COL4A2 mutations are multifunctional[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], yet the molecular mechanism of COL4A2 in GC remains elusive. Endothelial TLR2 promotes angiogenesis by recruiting pro-angiogenic immune cells and assumes a pathological role in pro-inflammatory diseases.\u003c/p\u003e \u003cp\u003eFibroblasts that are involved in cancers serve as pivotal factors in their malignant progression, with COL6A3 primarily expressed in these cells. Knockout experiments have validated the involvement of COL6A3 in the proliferation and invasion of CRC cells[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Gene ontology (GO) annotations showed that co-expressed genes of FN1 are mainly involved in the organization of extracellular structures, metabolism of collagens, integrin-mediated signaling pathways, migration of substrate-dependent cells, peptide cross-linking, artery agenesis, and the migration of muscle cells[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, genomics studies have suggested the involvement of FN1 in alterations of GC immune checkpoints and macrophage biomarkers[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Within the tumor microenvironment, immune cell infiltration has been shown to exert a pivotal impact on the progression of cancers[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. FN1 expression is essential in the invasion and migration of tumors. Studies have demonstrated a correlation between the expression of FN1 and the extent of GC-related immune infiltration. The expression of FN1 exhibits a close association with tumor-infiltrating immune cells, such as macrophages, Treg cells, NK cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, and dendritic cells[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Integrin subunit α 5 (ITGA5) primarily participates in \"integrin-mediated signaling pathway\", \"leukocyte migration\", and \"cell-substrate adhesion\"[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Thrombospondin 1 (THBS1) promotes metastasis by inducing the exhaustion of cytotoxic T cells and disrupting vascularization[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study, through GEO and TCGA data mining, identified differential expression of COL1A1, COL1A2, COL4A1, and TLR2 in tumor and non-tumor tissues, which is intricately linked to the onset and progression of GC. Moreover, the ROC curve analysis has confirmed its biological significance as a major advantage in prognostic prediction for GC. These investigations have also, for the first time, unveiled the prognostic value of these four genes in patients with GC, offering novel avenues for subsequent disease diagnosis and treatment research.\u003c/p\u003e \u003cp\u003eInevitably, our study has certain limitations. To begin with, as a retrospective study, additional prospective studies are required to validate our findings. Secondly, further comprehensive experiments are needed to validate the reliability of the mechanism analysis. Therefore, extensive studies will carried out in the future to elucidate the mechanistic relevance of these genes to the progression and prognosis of GC.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, by means of a series of bioinformatics analyses, four potential hub genes associated with the progression and prognosis of GC have been pinpointed. As per the results obtained, the involvement of collagen family proteins in the PI3K-Akt signaling pathway is believed to be significant in the pathogenesis of GC. Our findings not only provide valuable insights for GC biomarkers selection but also present novel targets for the treatment of GC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis work was supported by Postgraduate Research \u0026amp; Practice Innovation Program of Jiangsu Province (KYCX23_2104); China International Medical Exchange Fund(Z-2014-06-2104), Zhenjiang 169 Talent Project Scientific Research Project (2021-169-XX-19), Zhenjiang Key Research and Development Fund (SH2022032, SH2021090), Jiangsu Maternal and Child Health Care Project(F20232). The fund was not involved in any study design, data collection, analysis and interpretation, report writing, and article submission for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWei Xu:\u003c/strong\u003e Conceptualization, Formal analysis, Investigation, Writing - original draft preparation. \u003cstrong\u003eDandan Gong:\u003c/strong\u003e Conceptualization, Formal analysis, Writing - review and editing\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e \u003cstrong\u003eChangfeng Man:\u0026nbsp;\u003c/strong\u003eConceptualization, Formal analysis, Writing - review and editing. \u003cstrong\u003eShiqi Zhang:\u0026nbsp;\u003c/strong\u003eConceptualization, Resources, Methodology, Writing - review and editing.\u003cstrong\u003eXiaoyan Wang:\u003c/strong\u003e Conceptualization, Formal analysis, Writing - review and editing. \u003cstrong\u003eYu Fan:\u0026nbsp;\u003c/strong\u003eWriting - original draft preparation, Writing - review and editing, Methodology, Funding acquisition, Supervision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data were obtained from an open-access database, and therefore no approval was needed from the Medical Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYu Z, Liang C, Tu H, Qiu S, Dong X, Zhang Y, et al. 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Nat Commun. 2023;14(1):5534. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-023-41095-y\u003c/span\u003e\u003cspan address=\"10.1038/s41467-023-41095-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"gastric cancer, differential gene expression, hub genes, WGCNA, transcriptomics, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-4573637/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4573637/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: Gastric cancer (GC) is recognized as one of the prevailing solid malignant tumors globally, with a notable rate of recurrence and metastasis. Therefore, this study utilized database mining to analyze potential key genes (hub genes) that are associated with the progression and prognosis of GC, aiming to offer new clues for the prognosis and targeted treatment for GC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: This study utilized the GSE79973 dataset from the GEO to conduct DEGs in conjunction with the WGCNA. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed on disease-characteristic differentially expressed genes. In addition, a PPI network through the STRING database to screen for characteristic genes involved in the molecular mechanisms of GC. The diagnostic capabilities of these characteristic genes were ascertained through ROC curve analysis, integrating the clinical data of GC from TCGA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Systematic bioinformatics analysis pinpointed four genes—COL1A1, COL1A2, COL4A1, and TLR2—as closely related to the onset and progression of GC. ROC curve revealed their robust diagnostic and prognostic capabilities for GC (AUC\u003csub\u003e(COL1A1)\u003c/sub\u003e=0.9478, AUC\u003csub\u003e(COL1A2)\u003c/sub\u003e=0.8768, AUC\u003csub\u003e(COL4A1)\u003c/sub\u003e=0.8482, AUC\u003csub\u003e(TLR2)\u003c/sub\u003e=0.8452, all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001), presenting significant clinical translational application value.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: As newly discovered functional genes closely related to the onset and progression of GC, COL1A1, COL1A2, COL4A1, and TLR2, can be deemed as novel biomarkers for clinical diagnosis of GC, paving the way for new effective targets in the treatment of GC.\u003c/p\u003e","manuscriptTitle":"Identification of Hub Genes Associated with the Progression and Prognosis of Gastric Cancer through Systematic Bioinformatics Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-15 14:03:22","doi":"10.21203/rs.3.rs-4573637/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":"c4511cf8-5d66-4df9-8c1d-160814415788","owner":[],"postedDate":"July 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-17T08:12:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-15 14:03:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4573637","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4573637","identity":"rs-4573637","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}