Establishment and validation of a prognostic scoring model based on disulfidptosis-related long non-coding RNAs in stomach adenocarcinoma.

Background: Stomach adenocarcinoma (STAD), a frequently occurring gastrointestinal tumour, is often detected late and has a poor prognosis. Long non-coding RNAs (lncRNAs) significantly affect tumour development. Recent studies have identified disulfidptosis as a previously unexplained form of cell death. Herein, we aimed to examine the predictive value of disulfidptosis-related lncRNA models for the clinical prognosis and immunotherapy of STAD. Methods: STAD-related transcriptomic data were obtained from The Cancer Genome Atlas (TCGA), whereas genes associated with disulfidptosis were identified from previously published papers. A risk prediction model for disulfidptosis-related lncRNAs was developed using the Cox regression and least absolute shrinkage selection algorithm methods. The accuracy of the model was confirmed using calibration curves, and the biological functions were analysed using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA). Finally, the tumour mutation burden (TMB) and tumour immune dysfunction and exclusion (TIDE) algorithms were used to screen drugs that are sensitive to STAD. Results: The risk prediction models were constructed using seven disulfidptosis-related lncRNAs. The validated results were consistent with the predicted ones, with significant survival differences. When combined with clinical data, the risk scores were used as independent prognostic markers. Based on the tumour mutation load, the high-risk patient group had a poorer survival rate as compared with the low-risk patient group. Further studies were conducted to understand the different groups' inconsistent responses to immune status; subsequently, relatively sensitive drugs were identified. Conclusions: Overall, seven markers of disulfidptosis-related lncRNAs associated with STAD were found to facilitate prognostic prediction, suggesting new ideas for immunotherapy and clinical applications.

Construction of a disulfidptosis-related glycolysis gene risk model to predict the prognosis and immune infiltration analysis of gastric adenocarcinoma.

BACKGROUND: The pattern of cell death known as disulfidptosis was recently discovered. Disulfidptosis, which may affect the growth of tumor cells, represents a potential new approach to treating tumors. Glycolysis affects tumor proliferation, invasion, chemotherapy resistance, the tumor microenvironment (TME), and immune evasion. However, the efficacy and therapeutic significance of disulfidptosis-related glycolysis genes (DRGGs) in stomach adenocarcinoma (STAD) remain uncertain. METHODS: STAD clinical data and RNA sequencing data were downloaded from the TCGA database. DRGGs were screened using Cox regression and Lasso regression analysis to construct a prognostic risk model. The accuracy of the model was verified using survival studies, receiver operating characteristic (ROC) curves, column plots, and calibration curves. Additionally, our study investigated the relationships between the risk scores and immune cell infiltration, tumor mutational burden (TMB), and anticancer drug sensitivity. RESULTS: We have successfully developed a prognosis risk model with 4 DRGGs (NT5E, ALG1, ANKZF1, and VCAN). The model showed excellent performance in predicting the overall survival of STAD patients. The DRGGs prognostic model significantly correlated with the TME, immune infiltrating cells, and treatment sensitivity. CONCLUSIONS: The risk model developed in this work has significant clinical value in predicting the impact of immunotherapy in STAD patients and assisting in the choice of chemotherapeutic medicines. It can correctly estimate the prognosis of STAD patients.

Differential genetic mutations and immune cell infiltration in high- and low-risk STAD: Implications for prognosis and immunotherapy efficacy.

This study investigates genetic mutations and immune cell dynamics in stomach adenocarcinoma (STAD), focusing on identifying prognostic markers and therapeutic targets. Analysis of TCGA-STAD samples revealed C > A as the most common single nucleotide variant (SNV) in both high and low-risk groups. Key mutated driver genes included TTN, TP53 and MUC16, with frame-shift mutations more prevalent in the low-risk group and missense mutations in the high-risk group. Interaction analysis of hub genes such as C1QA and CD68 showed significant correlations, impacting immune cell infiltration patterns. Using ssGSEA, we found higher immune cell infiltration (B cells, CD4+ T cells, CD8+ T cells, DC cells, NK cells) in the high-risk group, correlated with increased risk scores. xCell algorithm results indicated distinct immune infiltration levels between the groups. The study's risk scoring model proved effective in prognosis prediction and immunotherapy efficacy assessment. Key molecules like CD28, CD27 and SLAMF7 correlated significantly with risk scores, suggesting potential targets for high-risk STAD patients. Drug sensitivity analysis showed a negative correlation between risk scores and sensitivity to certain treatments, indicating potential therapeutic options for high-risk STAD patients. We also validated the carcinogenic role of RPL14 in gastric cancer through phenotypic experiments, demonstrating its influence on cancer cell proliferation, invasion and migration. Overall, this research provides crucial insights into the genetic and immune aspects of STAD, highlighting the importance of a risk scoring model for personalized treatment strategies and clinical decision-making in gastric cancer management.

Uncovering the ceRNA Network Related to the Prognosis of Stomach Adenocarcinoma Among 898 Patient Samples.

Stomach adenocarcinoma (STAD) patients are often associated with significantly high mortality rates and poor prognoses worldwide. Among STAD patients, competing endogenous RNAs (ceRNAs) play key roles in regulating one another at the post-transcriptional stage by competing for shared miRNAs. In this study, we aimed to elucidate the roles of lncRNAs in the ceRNA network of STAD, uncovering the molecular biomarkers for target therapy and prognosis. Specifically, a multitude of differentially expressed lncRNAs, miRNAs, and mRNAs (i.e., 898 samples in total) was collected and processed from TCGA. Cytoplasmic lncRNAs were kept for evaluating overall survival (OS) time and constructing the ceRNA network. Differentially expressed mRNAs in the ceRNA network were also investigated for functional and pathological insights. Interestingly, we identified one ceRNA network including 13 lncRNAs, 25 miRNAs, and 9 mRNAs. Among them, 13 RNAs were found related to the patient survival time; their individual risk score can be adopted for prognosis inference. Finally, we constructed a comprehensive ceRNA regulatory network for STAD and developed our own risk-scoring system that can predict the OS time of STAD patients by taking into account the above.

Analysis of IGFBP7 expression characteristics in pan-cancer and its clinical relevance to stomach adenocarcinoma.

Background: Insulin-like growth factor (IGF) binding proteins (IGFBPs) are involved in tumorigenesis and cancer progression. IGFBP7 has been shown to act as either a tumor suppressive gene or an oncogene in many tumors, including stomach adenocarcinoma (STAD). To provide a more systematic and comprehensive understanding of IGFBP7 gene, we performed an integrative pan-cancer analysis and explored further with the case of STAD. Methods: We compared the expression data of IGFBP7 in various cancer and normal tissues obtained from The Cancer Genome Atlas (TCGA) database and the Genotype-Tissue Expression (GTEx) database. The TISIDB web portal was used to analyze the associations of IGFBP7 with cancer molecular subtypes and immune subtypes. We also analyzed the predictive ability and prognostic values of IGFBP7 in pan-cancer, as well as explored its targeted binding proteins and their biological functions. Additionally, we examined the relationship between IGFBP7 and the clinical characteristics of STAD, investigated the co-expression genes and biological functions of differentially expressed genes (DEGs), and validated the mRNA and protein expression levels of IGFBP7 using gastric cancer (GC) and adjacent normal tissues in a small self-case-control study. Results: IGFBP7 was found to be overexpressed in STAD and downregulated in many other cancers. The mRNA and protein expression levels of IGFBP7 were also significantly higher in the collected GC tissues compared with adjacent tissues. Expression of IGFBP7 varied significantly across molecular subtypes of nine different cancer types and immune subtypes of eight types, with the highest expression observed in the genomically stable molecular subtype and C3 inflammatory immune subtype in STAD. IGFBP7 demonstrated an area under the curve (AUC) >0.7 for predicting 16 cancer types, and an AUC >0.9 for seven types. Patients in the higher IGFBP7 expression group showed a poorer prognosis for adrenal cortical carcinoma (ACC) and low-grade glioma (LGG), while demonstrating a more favorable prognosis for kidney renal clear cell carcinoma (KIRC). IGFBP7 expression in STAD was significantly associated with T stage, pathological stage, histologic grade, and Helicobacter pylori infection. Conclusions: IGFBP7 showed promise as a biomarker for prediction and prognosis in pan-cancer. IGFBP7 was found to be overexpressed in STAD, and its expression was closely associated with the clinical characteristics of STAD.

Based on the Cancer Genome Atlas Database Development of a prognostic model of RNA binding protein in stomach adenocarcinoma.

The purpose of this study was to identify potential RNA binding proteins associated with the survival of gastric adenocarcinoma, as well as the corresponding biological characteristics and signaling pathways of these RNA binding proteins. RNA sequencing and clinical data were obtained from the cancer genome map (N = 32, T = 375) and the comprehensive gene expression database (GSE84437, N = 433). The samples in The Cancer Genome Atlas were randomly divided into a development group and a test group. A total of 1495 RNA binding protein related genes were extracted. Using nonparametric tests to analyze the difference of RNA binding protein related genes, 296 differential RNA binding proteins were obtained, 166 were up-regulated and 130 were down regulated. Twenty prognosis-related RNA binding proteins were screened using Cox regression, including 14 high-risk genes (hazard ratio > 1.0) and 6 low-risk genes (hazard ratio < 1.0). Seven RNA binding protein related genes were screened from the final prognostic model and used to construct a new prognostic model. Using the development group and test group, the model was verified with survival analysis, receiver operating characteristics curves and prognosis analysis curves. A prediction nomogram was finally developed and showed good prediction performance.

Bioinformatics analysis of the prognostic and immunotherapeutic significance of NPRL2 in stomach adenocarcinoma.

Background: Stomach adenocarcinoma (STAD) is a major type of gastric cancer with high morbidity and mortality. NPRL2, a candidate cancer suppressor gene, has been shown to have anti-cancer effects in various types of cancers. Therefore, comprehensive analyses of NPRL2 in STAD may provide a potential prognostic marker and clinical target for the management of gastric cancer. Methods: Genomic expression and methylation were analysed based on data from the Human Protein Atlas, Gene Expression Omnibus and Oncomine database. Survival analyses were conducted with the Kaplan-Meier method, using data from The Cancer Genome Atlas database. Immune correlation analyses and prediction of response to immunotherapy were performed using the online Immune Cell Abundance Identifier. Co-expression analyses, functional clustering analyses and construction of a prognostic risk model were conducted in R, with the clinical covariates balanced by the inverse probability treatment weighting method. Results: NPRL2 was abnormally downregulated in STAD (P<0.05). Survival analysis highlighted a positive association between the expression of NPRL2 and clinical outcomes for patients (P<0.05). Based on co-expression analyses, we found that NPRL2 may be involved in epithelial-mesenchymal transition, gastric cancer stem cells, and responsiveness to chemotherapeutic agents in STAD (P<0.05). Furthermore, functional clustering analysis revealed that NPRL2 was involved in the mTOR signalling pathway, autophagy, and the amino acid starvation response (adjust P<0.05). In addition, NPRL2 was negatively associated with tumour-infiltrating immune cells while positively associated with immunotherapeutic biomarkers in STAD (P<0.05). Meanwhile, patients with high NPRL2 expression were predicted to have a better response to immunotherapy (P<0.05). Finally, a prognostic model constructed based on NPRL2-related genes could predict the prognosis of STAD patients (AUC =0.641), and the risk score was an independent prognostic factor for STAD patients (HR =4.855, 95% CI: 2.683-8.785, P<0.001). Conclusions: The present study provided a comprehensive analysis of the role and potential mechanisms of NPRL2 in STAD, suggesting that NPRL2 is a potential biomarker for the survival and prediction of immunotherapy response in STAD.

Identification of 8-gene risk prediction signature associated with NOTCH1 in stomach adenocarcinoma based on bioinformatics analysis.

Background: Stomach adenocarcinoma (STAD), is the most common histological type of gastric cancer (GC) with high mortality and poor prognosis. We sought to investigate the contribution of Notch receptor 1 (NOTCH1) to STAD immunity. Methods: The profiles of immune cells in STAD cohorts were compared, and a correlation analysis between the NOTCH1 gene and tumor immune cell infiltration was then conducted. The immune-related genes (IRGs) associated with the NOTCH1 gene were identified. Based on the NOTCH1-associated IRGs, multiple-gene risk prediction signatures were established. The relationship between the expression levels of the selected IRGs and overall survival (OS) was analyzed by a univariate analysis. The risk score was calculated using the formula of ß1x1 + ß2x2 +... + ßixi. A prognostic nomogram was constructed to predict individuals' survival probabilities. Results: In STAD, NOTCH1 expression levels were significantly negatively associated with tumor-infiltrating lymphocyte (TIL) Act dendritic cells (DCs) (r=-0.196, P value =6.24e-05), TIL cluster of differentiation (CD) 56 bright cells (r=-0.115, P value =0.0193), TIL immature DCs (r=-0.293, P value =1.16e-09), TIL monocyte cells (r=-0.185, P value =0.000149), TIL central memory T CD4 cells (r=-0.126, P value =0.0103), and TIL gamma delta T cells (r=-0.149, P value =0.00229). The resulting risk scores of the 8-gene risk prediction signature (corticotrophin releasing hormone receptor 2 (CRHR2) (HR =1.858, P value =0.048), fms related receptor tyrosine kinase 1 (FLT1) (HR =1.268, P value =0.048), fms related receptor tyrosine kinase 4 (FLT4) (HR =1.334, P value =0.031), glial fibrillary acidic protein (GFAP) (HR =2.739, P value =0.008), platelet-derived growth factor receptor beta (PDGFRB) (HR =1.192, P value =0.02), prostaglandin D2 receptor (PTGDR) (HR =1.564, P value =0.049), semaphorin 5B (SEMA5B) (HR =1.154, P value =0.029), and tyrosine kinase 2 (TYK2) (HR =0.734, P value =0.041) were independent prognostic predictors for STAD patients. Conclusions: NOTCH1 could be a potential target for STAD. The mechanisms underpinning NOTCH1-medicated prognostic values of immune signatures should be further explored.

Identification of the key genes controlling stomach adenocarcinoma stem cell characteristics via an analysis of stemness indices.

Background: In-depth research on tumors has shown that cancer stem cells (CSCs) play a crucial role in tumorigenesis. However, the mechanisms underlying the growth and maintenance of CSCs in stomach adenocarcinoma (STAD) are unclear. This study sought to investigate the expression of stem cell-related genes in STAD. Methods: We identified key genes related to STAD stem cell characteristics by combining gene expression data obtained from The Cancer Genome Atlas to define a messenger ribonucleic acid expression-based stemness index (mRNAsi) based on mRNA expression. The correlations between the mRNAsi and STAD clinical characteristics, including age, tumor grade, pathological stage, and survival status, were explored. Additionally, a weighted gene co-expression network analysis was conducted to identify relevant modules and key genes. The expression verification and functional analysis of the key genes was carried out using multiple databases, including the TIMER (https://cistrome.shinyapps.io/timer/), and Gene Expression Profiling Integrative Analysis, and Gene Expression Omnibus databases. Results: The mRNAsi score was closely related to the clinical characteristics of STAD, including age, tumor grade, pathological stage, and survival status. Similarly, the mRNAsi score was significantly higher in STAD tissues than normal tissues, and the score decreased with tumor stage. The higher the mRNAsi score, the higher the overall survival rate. We screened a module of interest and found a strong correlation between 19 key genes. Among these 19 key genes, 16 had previously been shown to be closely related to STAD survival. The functional analysis showed that these key genes were linked to cell-cycle events, such as chromosome separation, mitosis, and microtubule movement. Conclusions: We identified 19 key genes that play an important role in the maintenance of STAD stem cells. Among these genes, 16 play a role in predicting the prognosis of STAD patients. The cell-cycle pathway was the most important signaling pathway for the key genes associated with STAD stem cells. These findings may provide a new rationale for screening therapeutic targets and the characterization of STAD stem cells.

Immune Infiltration and Clinical Outcome of Super-Enhancer-Associated lncRNAs in Stomach Adenocarcinoma.

Super-enhancers (SEs) comprise large clusters of enhancers that highly enhance gene expression. Long non-coding RNAs (lncRNAs) tend to be dysregulated in cases of stomach adenocarcinoma (STAD) and are vital for balancing tumor immunity. However, whether SE-associated lncRNAs play a role in the immune infiltration of STAD remains unknown. In the present study, we identified SE-associated lncRNAs in the H3K27ac ChIP-seq datasets from 11 tumor tissues and two cell lines. We found that the significantly dysregulated SE-associated lncRNAs were strongly correlated with immune cell infiltration through the application of six algorithms (ImmuncellAI, CIBERSORT, EPIC, quantiSeq, TIMER, and xCELL), as well as immunomodulators and chemokines. We found that the expression of SE-associated lncRNA TM4SF1-AS1 was negatively correlated with the proportion of CD8+ T cells present in STAD. TM4SF1-AS1 suppresses T cell-mediated immune killing function and predicts immune response to anti-PD1 therapy. ChIP-seq, Hi-C and luciferase assay results verified that TM4SF1-AS1 was regulated by its super-enhancer. RNA-seq data showed that TM4SF1-AS1 is involved in immune and cancer-related processes or pathways. In conclusion, SE-associated lncRNAs are involved in the tumor immune microenvironment and act as indicators of clinical outcomes in STAD. This study highlights the importance of SE-associated lncRNAs in the immune regulation of STAD.

Assessment of POLE and POLD1 mutations as prognosis and immunotherapy biomarkers for stomach adenocarcinoma.

Background: Cancer patients with POLE or POLD1 mutations may be excellent candidates for immune checkpoint inhibitors (ICIs) therapy and have favorable prognosis, but their potential in stomach adenocarcinoma (STAD) remains unknown. Therefore, the clinical significance of POLE and POLD1 mutations in STAD was evaluated. Methods: A summary of POLE/POLD1 mutations and clinical characteristics was performed on all 613 STAD samples, from which 360 samples were screened for analysis of the potential clinical relevance of POLE/POLD1 mutations to prognosis and immunotherapy. Results: The total frequency of both POLE and POLD1 mutations was 7.99% in STAD patients, correlating with an older age of onset and more frequently in the antrum anatomic subdivisions. Several genes that related to prognosis and immunotherapy also had high mutation frequencies in POLE/POLD1-mutant STADs. Furthermore, the STAD subgroup with POLE/POLD1 mutations had longer progression free survival (PFS) and overall survival (OS) in the subpopulation under 80. More importantly, STAD patients with POLE/POLD1 mutations exhibited adaptive immune resistance tumor microenvironment (TME) and deficient mismatch repair (dMMR) status, and possessed significantly higher PD-L1 expression level, higher tumor mutational load (TMB), higher microsatellite instability (MSI) percentage, and lower aneuploidy score, all of which may have potential implications for better ICIs treatment outcomes. Conclusions: POLE and POLD1 mutations are promising useful biomarkers to improve the clinical efficiency of practicing precision medicine in STAD patients, including as positive prognostic markers and predictive biomarkers of immunotherapy outcomes for STAD patients.

Constructing and validating of m6a-related genes prognostic signature for stomach adenocarcinoma and immune infiltration: Potential biomarkers for predicting the overall survival.

Background: Stomach adenocarcinoma (STAD) arises from the mutations of stomach cells and has poor overall survival. Chemotherapy is commonly indicated for patients with stomach cancer following surgical resection. The most prevalent alteration that affects cancer growth is N6-methyladenosine methylation (m6A), although the possible function of m6A in STAD prognosis is not recognized. Method: The research measured predictive FRGs in BLCA samples from the TCGA and GEO datasets. Data on the stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and corresponding clinical characteristics were obtained from TCGA and GEO. STAD from TCGA and GEO at 24 m6A was investigated. Lasso regression was used to construct the prediction model to assess the m6A prognostic signals in STAD. In addition, the correlation between m6a and immune infiltration in STAD patients was discussed using GSVA and ssGSEA analysis. Based on these genes, GO and KEGG analyses were performed to identify key biological functions and key pathways. Result: A significant relationship was discovered between numerous m6A clusters and the tumor immune microenvironment, as well as three m6A alteration patterns with different clinical outcomes. Furthermore, GSVA and ssGSEA showed that m6A clusters were significantly associated with immune infiltration in the STAD. The low-m6Ascore group had a lower immunotherapeutic response than the high-m6Ascore group. ICIs therapy was more effective in the group with a higher m6Ascore. Three writers (VIRMA, ZC3H13, and METTL3) showed significantly lower expression, whereas five authors (METTL14, METTL16, WTAP, RBM15, and RBM15B) showed considerably higher expression. Three readers (YTHDC2, YTHDF2, and LRPPRC) had higher levels of expression, whereas eleven readers (YTHDC1, YTHDF1, YTHDF3, HNRNPC, FMR1, HNRNPA2B1, IGFBP1, IGFBP2, IGFBP3, and RBMX) had lower levels. As can be observed, the various types of m6 encoders have varied ramifications for STAD control. Conclusion: STAD occurrence and progression are linked to m6A-genes. Corresponding prognostic models help forecast the prognosis of STAD patients. m6A-genes and associated immune cell infiltration in the tumor microenvironment (TME) may serve as potential therapeutic targets in STAD, which requires further trials. In addition, the m6a-related gene signature offers a viable alternative to predict bladder cancer, and these m6A-genes show a prospective research area for STAD targeted treatment in the future.

Prognostic value of nicotinamide adenine dinucleotide (NAD+) metabolic genes in patients with stomach adenocarcinoma based on bioinformatics analysis.

Background: Because stomach adenocarcinoma (STAD) has a poor prognosis, it is necessary to explore new prognostic genes to stratify patients to guide existing individualized treatments. Methods: Survival and clinical information, RNA-seq data and mutation data of STAD were acquired from The Cancer Genome Atlas (TCGA) database. Fifty-one nicotinamide adenine dinucleotide (NAD+) metabolism-related genes (NMRGs) were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Differentially expressed NMRGs (DE-NMRGs) between STAD and normal samples were screened, and consistent clustering analysis of STAD patients was performed based on the DE-NMRGs. Survival analysis, Gene Set Enrichment Analysis (GSEA), mutation frequency analysis, immune microenvironment analysis and drug prediction were performed among different clusters. Additionally, the differentially expressed genes (DEGs) among different clusters were selected, and the intersections of DEGs and DE-NMRGs were selected as the prognostic genes. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was performed on a human gastric mucosa epithelial cell line and cancer cell line to verify the expression of the prognostic genes. Results: A total of 27 DE-NMRGs and two clusters were selected. There was a difference in survival between clusters 1 and 2. Furthermore, 18 DE-NMRGs were significantly different between clusters 1 and 2. The different Gene Ontology (GO) biological processes and KEGG pathways between clusters 1 and 2 were mainly enriched in cyclic nucleotide mediated signaling, synaptic signaling and hedgehog signaling pathway, etc. The somatic mutation frequencies were different between the two clusters, and TTN was the highest mutated gene in the patients of the clusters 1 and 2. Additionally, eight immune cells, immune score, stromal score, and estimate score were different between clusters 1 and 2. The patients in cluster 2 were sensitive to CTLA4 inhibitor treatment. Furthermore, the top five drugs (AP.24534, BX.795, Midostaurin, WO2009093927 and CCT007093) were significantly higher in cluster 1 than in cluster 2. Finally, three genes (AOX1, NNMT and PTGIS) were acquired as prognostic, and their expressions were consistent with the results of bioinformatics analysis. Conclusions: Three prognostic genes related to NAD+ metabolism in STAD were screened out, which provides a theoretical basis and reference value for future treatment and prognosis of STAD.

Comprehensive Analysis of IGFBPs as Biomarkers in Gastric Cancer.

OBJECTIVE: Gastric cancer is the fifth most common cancer worldwide and the third leading cause of cancer-related deaths. Insulin-like growth-factor-binding proteins (IGFBPs) were initially identified as passive inhibitors that combined with insulin-like growth factors (IGFs) in serum. However, more recent data have shown that they have different expression patterns and a variety of functions in the development and occurrence of cancers. Thus, their various roles in cancer still need to be elucidated. This study aimed to explore the IGFBPs and their prognostic value as markers in gastric cancer. METHODS: Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), Kaplan-Meier Plotter, cBioPortal, GeneMANIA, and TIMER were used to analyze the differential expression, prognostic value, genetic alteration, and association with immune cell infiltration of IGFPBs in gastric cancer. RESULTS: Expression levels of IGFBP3, IGFBP4, and IGFBP7 were significantly elevated in gastric cancer tissues, whereas those of IGFBP1 were reduced in normal tissues. IGFBP1/5/7 expression was significantly associated with overall survival whereas IGFBP6/7 expression was significantly correlated with disease-free survival in gastric cancer patients. IGFBP3/5/6/7 were associated with clinical cancer stage. Gene ontology and Kyoto Encyclopedia of Genes and Genome analyses showed that IGFBP3/5/7 were mainly enriched in focal adhesion, extracellular matrix structural constituent, cell-substratist junction, extracellular structure, and matrix organization. Stomach adenocarcinoma (STAD) and gastric cancer had more IGFBP1-7 mutations than other tumor types. Hub gene analysis showed that TP53 and IGF2 expression was significantly elevated in STAD patients; PLG, PAPPA, AFP, and CYR61 were associated with overall survival rate; and IGFALS, PLG, IGF1, AHSG, and FN1 were associated with disease-free survival. Finally, IGFBP3-7 were all associated with cancer-associated fibroblast infiltration in STAD, colon adenocarcinoma, and rectal adenocarcinoma. CONCLUSION: Our study provides a comprehensive analysis and selection of IGFBPs as prognostic biomarkers in STAD. This was the first bioinformatic analysis study to describe the involvement of IGFBPs, especially IGFBP7, in gastric cancer development through the extracellular matrix.

Identification and Validation of a Malignant Cell Subset Marker-Based Polygenic Risk Score in Stomach Adenocarcinoma Through Integrated Analysis of Bulk and Single-Cell RNA Sequencing Data.

Objectives: The aim of the present study was to construct a polygenic risk score (PRS) for poor survival among patients with stomach adenocarcinoma (STAD) based on expression of malignant cell markers. Methods: Integrated analyses of bulk and single-cell RNA sequencing (scRNA-seq) of STAD and normal stomach tissues were conducted to identify malignant and non-malignant markers. Analyses of the scRNA-seq profile from early STAD were used to explore intratumoral heterogeneity (ITH) of the malignant cell subpopulations. Dimension reduction, cell clustering, pseudotime, and gene set enrichment analyses were performed. The marker genes of each malignant tissue and cell clusters were screened to create a PRS using Cox regression analyses. Combined with the PRS and routine clinicopathological characteristics, a nomogram tool was generated to predict prognosis of patients with STAD. The prognostic power of the PRS was validated in two independent external datasets. Results: The malignant and non-malignant cells were identified according to 50 malignant and non-malignant cell markers. The malignant cells were divided into nine clusters with different marker genes and biological characteristics. Pseudotime analysis showed the potential differentiation trajectory of these nine malignant cell clusters and identified genes that affect cell differentiation. Ten malignant cell markers were selected to generate a PRS: RGS1, AADAC, NPC2, COL10A1, PRKCSH, RAMP1, PRR15L, TUBA1A, CXCR6, and UPP1. The PRS was associated with both overall and progression-free survival (PFS) and proved to be a prognostic factor independent of routine clinicopathological characteristics. PRS could successfully divide patients with STAD in three datasets into high- or low-risk groups. In addition, we combined PRS and the tumor clinicopathological characteristics into a nomogram tool to help predict the survival of patients with STAD. Conclusion: We revealed limited but significant intratumoral heterogeneity in STAD and proposed a malignant cell subset marker-based PRS through integrated analysis of bulk sequencing and scRNA-seq data.

Identification of Early Diagnostic and Prognostic Biomarkers via WGCNA in Stomach Adenocarcinoma.

Stomach adenocarcinoma (STAD) is a leading cause of cancer deaths, and the outcome of the patients remains dismal for the lack of effective biomarkers of early detection. Recent studies have elucidated the landscape of genomic alterations of gastric cancer and reveal some biomarkers of advanced-stage gastric cancer, however, information about early-stage biomarkers is limited. Here, we adopt Weighted Gene Co-expression Network Analysis (WGCNA) to screen potential biomarkers for early-stage STAD using RNA-Seq and clinical data from TCGA database. We find six gene clusters (or modules) are significantly correlated with the stage-I STADs. Among these, five hub genes, i.e., MS4A1, THBS2, VCAN, PDGFRB, and KCNA3 are identified and significantly de-regulated in the stage-I STADs compared with the normal stomach gland tissues, which suggests they can serve as potential early diagnostic biomarkers. Moreover, we show that high expression of VCAN and PDGFRB is associated with poor prognosis of STAD. VCAN encodes a large chondroitin sulfate proteoglycan that is the main component of the extracellular matrix, and PDGFRB encodes a cell surface tyrosine kinase receptor for members of the platelet-derived growth factor (PDGF) family. Consistently, Gene Ontology (GO) analysis of differentially expressed genes in the STADs indicates terms associated with extracellular matrix and receptor ligand activity are significantly enriched. Protein-protein network interaction analysis (PPI) and Gene Set Enrichment Analysis (GSEA) further support the core role of VCAN and PDGFRB in the tumorigenesis. Collectively, our study identifies the potential biomarkers for early detection and prognosis of STAD.

Neuropilin1 Expression Acts as a Prognostic Marker in Stomach Adenocarcinoma by Predicting the Infiltration of Treg Cells and M2 Macrophages.

Neuropilin1 (NRP1) plays a critical role in tumor progression and immune responses. Although the roles of NRP1 in various tumors have been investigated, the clinical relevance of NRP1 expression in stomach adenocarcinoma (STAD) has not been studied. To investigate the use of NRP1 as a prognostic biomarker of STAD, we analyzed NRP1 mRNA expression and its correlation with patient survival and immune cell infiltration using various databases. NRP1 mRNA expression was significantly higher in STAD than normal tissues, and Kaplan-Meier survival analysis showed that NRP1 expression was significantly associated with poor prognosis in patients with STAD. To elucidate the related mechanism, we analyzed the correlation between NRP1 expression and immune cell infiltration level. In particular, the infiltration of immune-suppressive cells, such as regulatory T (Treg) cells and M2 macrophage, was significantly increased by NRP1 expression. In addition, the expression of interleukin (IL)-35, IL-10, and TGF-ß1 was also positively correlated with NRP1 expression, resulting in the immune suppression. Collectively in this study, our integrated analysis using various clinical databases shows that the significant correlation between NRP1 expression and the infiltration of Treg cells and M2 macrophage explains poor prognosis mechanism in STAD, suggesting the clinical relevance of NRP1 expression as a prognostic biomarker for STAD patients.

Screening of differential expressed genes from gene chip and sequencing data and evaluate its prognostic values in males with digestive system neoplasms.

BACKGROUND: Digestive system neoplasm is a common cancer in males worldwide. This study aimed to explore the commonalities in males with digestive system neoplasms (MDSN) and its clinical relevance. METHODS: A total of 46 differential expressed genes (DEGs) in MDSN were identified shared in TCGA and GEO databases. RESULTS: These DEGs significantly affected a variety of cell function and signaling pathways. Of which, a hub of 7 genes (CCNB1, MAD2L1, BUB1, CHEK1, MCM2, CCNA2 and CDC25B) were interacted with each other in protein level and significantly enriched in cell cycle pathway. Further methylation analysis, we found that BUB1, MAD2L1 and MCM2 were hypomethylation via m6A modification. Besides, BUB1, MAD2L1 and MCM2 were co-expressed in mRNA level and up-regulation of them led to worse prognostic in hepatocellular carcinoma, while caused a better prognostic in stomach adenocarcinoma (STAD), and had a race difference between white and Asian people in STAD. Medicine molecules, I-threonine (ID: PA451673) and enzymes (ID: PA164712734) might be efficient medicines in BUB1, MAD2L1 and MCM2 up-regulated MDSN patients. CONCLUSIONS: Taken together, hypomethylation via m6A modification might cause BUB1, MAD2L1 and MCM2 up-regulation in MDSN. Dysregulation of BUB1, MAD2L1 and MCM2 function as contrast prognostic in liver hepatocellular carcinoma and STAD. Our study provides more accurate therapeutic targets and prognostic biomarkers for specific cancer types.