Weighted correlation network analysis identifies FN1, COL1A1 and SERPINE1 associated with the progression and prognosis of gastric cancer

Construction of a prognostic model via WGCNA combined with the LASSO algorithm for stomach adenocarcinoma patients

Objective

This study aimed to identify prognostic signatures to predict the prognosis of patients with stomach adenocarcinoma (STAD), which is necessary to improve poor prognosis and offer possible treatment strategies for STAD patients.

Methods

The overlapping genes between the key model genes that were screened by the weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) whose expression was different with significance between normal and tumor tissues were extracted to serve as co-expression genes. Then, enrichment analysis was performed on these genes. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression was performed to screen the hub genes among overlapping genes. Finally, we constructed a model to explore the influence of polygenic risk scores on the survival probability of patients with STAD, and interaction effect and mediating analyses were also performed.

Results

DEGs included 2,899 upregulated genes and 2,896 downregulated genes. After crossing the DEGs and light-yellow module genes that were obtained by WGCNA, a total of 39 overlapping genes were extracted. The gene enrichment analysis revealed that these genes were enriched in the prion diseases, biosynthesis of unsaturated fatty acids, RNA metabolic process, hydrolase activity, etc. PIP5K1P1, PTTG3P, and SNORD15B were determined by LASSO-Cox. The prognostic prediction of the three-gene model was established. The Cox regression analysis showed that the comprehensive risk score for three genes was an independent prognosis factor.

Conclusion

PIP5K1P1, PTTG3P, and SNORD15B are related to the prognosis and overall survival of patients. The three-gene risk model constructed has independent prognosis predictive ability for STAD.

ExpOmics: a comprehensive web platform empowering biologists with robust multi-omics data analysis capabilities

MOTIVATION

High-throughput technologies yield a broad spectrum of multi-omics datasets, which offer unparalleled insights into complex biological systems. However, effectively analyzing this diverse array of data presents challenges, considering factors such as species diversity, data types, costs, and limitations of the available tools.

RESULTS

Herein, we present ExpOmics, a comprehensive web platform featuring 7 applications and 4 toolkits, with 28 customizable analysis functions spanning various analyses of differential expression, co-expression, Weighted Gene Co-expression Network Analysis (WGCNA), feature selection, and functional enrichment. ExpOmics allows users to upload and explore multi-omics data without organism restrictions, supporting various expression data, including genes, mRNAs, lncRNAs, miRNAs, circRNAs, piRNAs, and proteins and is compatible with diverse gene nomenclatures and expression values. Moreover, ExpOmics enables users to analyze 22 427 transcriptomic datasets of 196 cancer subtypes sourced from 63 projects of The Cancer Genome Atlas Program (TCGA) to identify cancer biomarkers. The analysis results from ExpOmics are presented in high-quality graphical formats suitable for publication and are available for free download. A case study using ExpOmics identified two potential oncogenes, SERPINE1 and SLC43A1, that may regulate colorectal cancer through distinct biological processes. In summary, ExpOmics can serves as a robust platform for global researchers to explore multi-omics data, gain biological insights, and formulate testable hypotheses.

AVAILABILITY AND IMPLEMENTATION

ExpOmics is available at http://www.biomedical-web.com/expomics.

miR-34b-3p-mediated regulation of STC2 and FN1 enhances chemosensitivity and inhibits proliferation in cervical cancer

Dysregulation of microRNA (miRNA) expression in cancer is a significant factor contributing to the progression of chemoresistance. The objective of this study is to explore the underlying mechanisms by which miR-34b-3p regulates chemoresistance in cervical cancer (CC). Previous findings have demonstrated low expression levels of miR-34b-3p in both CC chemoresistant cells and tissues. In this study, we initially characterize the behavior of SiHa/DDP cells which are CC cells resistant to the chemotherapeutic drug cisplatin (DDP). Subsequently, miR-34b-3p mimics are transfected into SiHa/DDP cells. It is observed that overexpression of miR-34b-3p substantially inhibits the proliferation, migration, and invasion abilities of SiHa/DDP cells and also enhances their sensitivity to DDP-induced cell death. Quantitative RT-PCR and western blot analysis further reveal elevated expression levels of STC2 and FN1 in SiHa/DDP cells, contrary to the expression pattern of miR-34b-3p. Moreover, STC2 and FN1 contribute to DDP resistance, proliferation, migration, invasion, and decreased apoptosis in CC cells. Through dual-luciferase assay analysis, we confirm that STC2 and FN1 are direct targets of miR-34b-3p in CC. Finally, rescue experiments demonstrate that overexpression of either STC2 or FN1 can partially reverse the inhibitory effects of miR-34b-3p overexpression on chemoresistance, proliferation, migration and invasion in CC cells. In conclusion, our findings support the role of miR-34b-3p as a tumor suppressor in CC. This study indicates that targeting the miR-34b-3p/STC2 or FN1 axis has potential therapeutic implications for overcoming chemoresistance in CC patients.

Analysis of cancer-associated fibroblasts related genes identifies COL11A1 associated with lung adenocarcinoma prognosis

BACKGROUND

The treatment of lung adenocarcinoma is difficult due to the limited therapeutic options. Cancer-associated fibroblasts play an important role in the development of cancers. This study aimed to identify a promising molecular target associated with cancer-associated fibroblasts for the treatment of lung adenocarcinoma.

METHODS

The Cancer Genome Atlas lung adenocarcinoma dataset was used to screen hub genes associated with cancer-associated fibroblasts via the EPIC algorithm and Weighted Gene Co-expression Network Analysis. Multiple databases were used together with our data to verify the differential expression and survival of COL11A1. Functional enrichment analysis and the single-cell TISCH database were used to elucidate the mechanisms underlying COL11A1 expression. The correlation between COL11A1 and immune checkpoint genes in human cancers was also evaluated.

RESULTS

Using the EPIC algorithm and Weighted Gene Co-expression Network Analysis, 13 hub genes associated with cancer-associated fibroblasts in lung adenocarcinoma were screened. Using the GEPIA database, Kaplan-Meier Plotter database, GSE72094, GSE75037, GSE32863, and our immunohistochemistry experiment data, we confirmed that COL11A1 overexpresses in lung adenocarcinoma and that high expression of COL11A1 is associated with a poor prognosis. COL11A1 has a genetic alteration frequency of 22% in patients with lung adenocarcinoma. COL11A1 is involved in the extracellular matrix activities of lung adenocarcinoma. Using the TISCH database, we found that COL11A1 is mainly expressed by cancer-associated fibroblasts in the tumor microenvironment rather than by lung adenocarcinoma cells. Finally, we found that COL11A1 is positively correlated with HAVCR2(TIM3), CD274 (PD-L1), CTLA4, and LAG3 in lung adenocarcinoma.

CONCLUSION

COL11A1 may be expressed and secreted by cancer-associated fibroblasts, and a high expression of COL11A1 may result in T cell exhaustion in the tumor microenvironment of lung adenocarcinoma. COL11A1 may serve as an attractive biomarker to provide new insights into cancer therapeutics.

Identification of differentially expressed genes associated with the pathogenesis of gastric cancer by bioinformatics analysis

AIM

Gastric cancer (GC) is one of the most diagnosed cancers worldwide. GC is a heterogeneous disease whose pathogenesis has not been entirely understood. Besides, the GC prognosis for patients remains poor. Hence, finding reliable biomarkers and therapeutic targets for GC patients is urgently needed.

METHODS

GSE54129 and GSE26942 datasets were downloaded from Gene Expression Omnibus (GEO) database to detect differentially expressed genes (DEGs). Then, gene set enrichment analyses and protein-protein interactions were investigated. Afterward, ten hub genes were identified from the constructed network of DEGs. Then, the expression of hub genes in GC was validated. Performing survival analysis, the prognostic value of each hub gene in GC samples was investigated. Finally, the databases were used to predict microRNAs that could regulate the hub genes. Eventually, top miRNAs with more interactions with the list of hub genes were introduced.

RESULTS

In total, 203 overlapping DEGs were identified between both datasets. The main enriched KEGG pathway was "Protein digestion and absorption." The most significant identified GO terms included "primary alcohol metabolic process," "basal part of cell," and "extracellular matrix structural constituent conferring tensile strength." Identified hub modules were COL1A1, COL1A2, TIMP1, SPP1, COL5A2, THBS2, COL4A1, MUC6, CXCL8, and BGN. The overexpression of seven hub genes was associated with overall survival. Moreover, among the list of selected miRNAs, hsa-miR-27a-3, hsa-miR-941, hsa-miR-129-2-3p, and hsa-miR-1-3p, were introduced as top miRNAs targeting more than five hub genes.

CONCLUSIONS

The present study identified ten genes associated with GC, which may help discover novel prognostic and diagnostic biomarkers as well as therapeutic targets for GC. Our results may advance the understanding of GC occurrence and progression.

Identification and validation of key genes associated with pathogenesis and prognosis of gastric cancer

Background

Gastric cancer (GC) is the fourth leading cause of cancer-related death worldwide. However, the precise mechanisms and specific biomarkers of GC have not been fully elucidated. We therefore sought to identify and validate the genes associated with GC.

Methods

RNA sequencing was performed on gastric tissue specimens from 10 cases each of non-atrophic gastritis (NAG), intestinal metaplasia (IM), and GC. Validation of gene expression was conducted through immunohistochemistry (IHC) staining. The Kaplan-Meier Plotter database was utilized to screen genes associated with prognosis, while protein-protein interaction analysis was conducted to identify hub genes.

Results

In GC-IM, the differentially expressed genes (DEGs) were predominantly enriched in pathways related to ECM-receptor interaction, focal adhesion, PI3K-Akt pathway, and pathways in cancer. Conversely, in IM-NAG, the DEGs were primarily enriched in pathways associated with fat digestion and absorption, pancreatic secretion, and retinol metabolism. IHC staining revealed elevated expression levels of KLK7 and KLK10 in GC. Specifically, KLK7 expression was found to be correlated with differentiation (P = 0.025) and depth of invasion (P = 0.007) in GC, while both KLK7 and KLK10 were associated with the overall survival (P < 0.05). Furthermore, a total of ten hub genes from DEGs in GC-NAG (COL6A2, COL1A1, COL4A1, COL1A2, SPARC, COL4A2, FN1, PCOLCE, SERPINH1, LAMB1) and five hub genes in IM-NAG (SI, DPP4, CLCA1, MEP1A, OLFM4) were demonstrated to have a significant correlation with the prognosis of GC.

Conclusions

The present study successfully identified and validated crucial genes associated with GC, providing valuable insights into the underlying mechanisms of this disease. The findings of this study have the potential to inform clinical practice.

The predictive effect of immune therapy and chemotherapy under T cell-related gene prognostic index for Gastric cancer

Background: Gastric cancer (GC) is one of the most common malignancies in the human digestive tract. CD4+T cells can eliminate tumor cells directly through the mechanism of cytolysis, they can also indirectly attack tumor cells by regulating the tumor TME. A prognostic model of CD4+T cells is urgently needed to improve treatment strategies and explore the specifics of this interaction between CD4+T cells and gastric cancer cells. Methods: The detailed data of GC samples were downloaded from the Cancer Genome Atlas (TCGA), GSE66229, and GSE84437 datasets. CD4⁺ T cell-related genes were identified to construct a risk-score model by using the Cox regression method and validated with the Gene Expression Omnibus (GEO) dataset. In addition, postoperative pathological tissues of 139 gastric cancer patients were randomly selected for immunohistochemical staining, and their prognostic information were collected for external verification. Immune and molecular characteristics of these samples and their predictive efficacy in immunotherapy and chemotherapy were analysed. Results: The training set and validation set had consistent results, with GC patients of high PROC and SERPINE1 expression having poorer prognosis. In order to improve their clinical application value, we constructed a risk scoring model and established a high-precision nomogram. Low-risk patients had a better overall survival (OS) than high-risk patients, consistent with the results from the GEO cohort. Furthermore, the risk-score model can predict infiltration of immune cells in the tumor microenvironment of GC, as well as the response of immunotherapy. Correlations between the abundance of immune cells with PROC and SERPINE1 genes were shown in the prognostic model according to the training cohort. Finally, sensitive drugs were identified for patients in different risk subgroup. Conclusion: The risk model not only provides a basis for better prognosis in GC patients, but also is a potential prognostic indicator to distinguish the molecular and immune characteristics of the tumor, and its response to immune checkpoint inhibitor (ICI) therapy and chemotherapy.

Evaluation of CEP55, SERPINE1 and SMPD3 genes and proteins as diagnostic and prognostic biomarkers in gastric carcinoma in Egyptian patients

Background

Gastric carcinoma (GC) is a fatal disease. Detection of new biomarkers that can be utilized in the early diagnosis of GC is a pressing need. This present study assessed centrosomal protein-55 (CEP55)’ serpin family E member 1 (SERPINE1) and sphingomyelin phosphodiesterase 3 (SMPD3) genes and proteins in gastric adenocarcinoma with different tumor progression features. Thirty surgically resected gastric tissue samples from thirty patients suffered from gastric cancers were obtained. The gastric tissue samples were divided into tumorous (with different stages and grades) and adjacent non-tumorous samples. CEP55, SERPINE1 and SMPD3 genes were assessed by quantitative qRT-PCR, and their proteins were assessed by ELISA in the gastric tissue samples.

Results

As regards SERPINE1, CEP55 genes and proteins, results revealed significant elevations in the GC samples (p < 0.0001). On the contrary, SMPD3 gene and protein revealed significant decreases as compared to non-tumorous samples. The studied genes and proteins showed highly significant specificity and sensitivity in the early detection of GC. SERPINE1 gene and protein revealed highly significant increases and positive correlations, while SMPD3 gene and protein revealed highly significant decreases and negative correlations as the tumor progresses.

Conclusion

CEP55, SERPINE1 and SMPD3 genes and proteins could be used as useful biomarkers for the early detection of GC. SERPINE1 and SMPD3 genes and proteins might be used as risk and protective prognostic factors in GC, respectively.

Inflammation-Related Genes Serve as Prognostic Biomarkers and Involve in Immunosuppressive Microenvironment to Promote Gastric Cancer Progression

Gastric cancer (GC) is a typical inflammatory-related malignant tumor which is closely related to helicobacter pylori infection. Tumor inflammatory microenvironment plays a crucial role in tumor progression and affect the clinical benefit from immunotherapy. In recent years, immunotherapy for gastric cancer has achieved promising outcomes, but not all patients can benefit from immunotherapy due to tumor heterogeneity. In our study, we identified 29 differentially expressed and prognostic inflammation-related genes in GC and normal samples. Based on those genes, we constructed a prognostic model using a least absolute shrinkage and selection operator (LASSO) algorithm, which categorized patients with GC into two groups. The high-risk group have the characteristics of "cold tumor" and have a poorer prognosis. In contrast, low-risk group was "hot tumor" and had better prognosis. Targeting inflammatory-related genes and remodeling tumor microenvironment to turn "cold tumor" into "hot tumor" may be a promising solution to improve the efficacy of immunotherapy for patients with GC.

Identification of Potential Diagnostic and Prognostic Biomarkers for Gastric Cancer Based on Bioinformatic Analysis

Background: Gastric cancer (GC) is one of the most prevalent cancers all over the world. The molecular mechanisms of GC remain unclear and not well understood. GC cases are majorly diagnosed at the late stage, resulting in a poor prognosis. Advances in molecular biology techniques allow us to get a better understanding of precise molecular mechanisms and enable us to identify the key genes in the carcinogenesis and progression of GC.

Methods: The present study used datasets from the GEO database to screen differentially expressed genes (DEGs) between GC and normal gastric tissues. GO and KEGG enrichments were utilized to analyze the function of DEGs. The STRING database and Cytoscape software were applied to generate protein–protein network and find hub genes. The expression levels of hub genes were evaluated using data from the TCGA database. Survival analysis was conducted to evaluate the prognostic value of hub genes. The GEPIA database was involved to correlate key gene expressions with the pathological stage. Also, ROC curves were constructed to assess the diagnostic value of key genes.

Results: A total of 607 DEGs were identified using three GEO datasets. GO analysis showed that the DEGs were mainly enriched in extracellular structure and matrix organization, collagen fibril organization, extracellular matrix (ECM), and integrin binding. KEGG enrichment was mainly enriched in protein digestion and absorption, ECM-receptor interaction, and focal adhesion. Fifteen genes were identified as hub genes, one of which was excluded for no significant expression between tumor and normal tissues. COL1A1, COL5A2, P4HA3, and SPARC showed high values in prognosis and diagnosis of GC.

Conclusion: We suggest COL1A1, COL5A2, P4HA3, and SPARC as biomarkers for the diagnosis and prognosis of GC.