Diagnostic and prognostic value of MATN3 expression in gastric carcinoma: TCGA database mining

Identification of an autophagy- and macropinocytosis-related prognostic signature for the prediction of prognosis and therapeutic response in gastric cancer

BACKGROUND

Traditional liquid biopsy markers show a low rate of positivity and accurate in gastric cancer. With the rapid advancement of sequencing technology, scientists have identified promising research avenues in this field. Autophagy and macropinocytosis utilize diverse pathways and mechanisms to supply resources and fuel for tumor growth. Nonetheless, their potential interplay introduces an untapped avenue for the discovery of novel tumor biomarkers.

OBJECTIVE

To develop an innovative prognostic signature based on autophagy- and micropinocytosis-related genes, with the aim to predict the outcome and therapeutic response of gastric cancer patients. Additionally, to validate the prognostic impact of this signature, and elucidate the role of representative molecules in gastric cancer.

METHODS

To construct and validate a prognostic signature for gastric cancer, bioinformatics methods such as COX regression, LASSO regression, survival analysis, ROC curve, and nomogram were utilized based on the sequencing and clinical data of gastric cancer patients retrieved from the TCGA and GEO databases. GSEA functional enrichment analyses were employed to predict the biological functions. Meanwhile, qRT-PCR and Western blot experiments were utilized to quantify the mRNA and protein expression levels. Furthermore, the EdU assay and colony formation assay were utilized to examine the cell proliferation ability while the Transwell assays were conducted to assess the migration and invasion abilities of gastric cancer cells.

RESULTS

Through consistency clustering and univariate COX analyses, potential prognostic genes involved in both autophagy and macropinocytosis were identified. Based on these genes, a 9-gene signature was constructed, which demonstrated high accuracy in predicting gastric cancer patients' survival period, immunotherapeutic response, and chemotherapy drug tolerance. Furthermore, qRT-PCR analyses of gastric cancer tissue samples showed that the representative genes of this signature were aberrantly overexpressed in gastric cancer, with MATN3, as the most notable molecule, exhibiting significant carcinogenic effects on cancer cells by actively regulating their proliferation, migration, and invasion abilities.

CONCLUSION

Our newly created prognostic signature possesses significant potential as a biomarker for gastric cancer, while MATN3 is identified as an oncogenic factor in gastric cancer. This brings to light new perspectives, which can contribute to enhancing the diagnosis and treatment of gastric cancer.

Identification of angiogenesis-related subtypes and risk models for predicting the prognosis of gastric cancer patients

Gastric cancer (GC) is a leading cause of cancer-related mortality and is characterized by significant heterogeneity, highlighting the need for further studies aimed at personalized treatment strategies. Tumor angiogenesis is critical for tumor development and metastasis, yet its role in molecular subtyping and prognosis prediction remains underexplored. This study aims to identify angiogenesis-related subtypes and develop a prognostic model for GC patients. Using data from The Cancer Genome Atlas (TCGA), we performed consensus cluster analysis on differentially expressed angiogenesis-related genes (ARGs), identifying two patient subtypes with distinct survival outcomes. Differentially expressed genes between the subtypes were analyzed via Cox and LASSO regression, leading to the establishment of a subtype-based prognostic model using a machine learning algorithm. Patients were classified into high- and low-risk groups based on the risk score. Validation was performed using independent datasets (ICGC and GSE15459). We utilized a deconvolution algorithm to investigate the tumor immune microenvironment in different risk groups and conducted analyses on genetic profiling, sensitivity and combination of anti-tumor drug. Our study identified ten prognostic signature genes, enabling the calculation of a risk score to predict prognosis and overall survival. This provides critical data for stratified diagnosis and treatment upon patient admission, monitoring disease progression throughout the entire course, evaluating immunotherapy efficacy, and selecting personalized medications for GC patients.

Investigating MATN3 and ASPN as novel drivers of gastric cancer progression via EMT pathways

Gastric cancer (GC) is a leading cause of cancer-related deaths globally, necessitating the identification of novel therapeutic targets. This study investigates the roles of MATN3 and ASPN in GC progression via the epithelial-mesenchymal transition (EMT) pathway. Analysis of the Cancer Genome Atlas-Stomach Adenocarcinoma (TCGA-STAD) dataset revealed that both MATN3 and ASPN are significantly upregulated in GC tissues and correlate with poor patient survival. Protein-protein interaction and co-expression analyses confirmed a direct interaction between MATN3 and ASPN, suggesting their synergistic role in EMT activation. Functional assays demonstrated that MATN3 promotes GC cell proliferation, migration, and invasion, while its knockdown inhibits these malignant behaviors and induces apoptosis. ASPN overexpression further amplified these oncogenic effects. In vivo, studies in a mouse model corroborated that co-overexpression of MATN3 and ASPN enhances tumor growth and metastasis. These findings highlight the MATN3-ASPN axis as a potential therapeutic target in GC, offering new insights into the molecular mechanisms driving GC progression.

[Relationship Between the Expression of Human Matricellular Protein 3 and the Pathological Features, Drug Resistance, and Prognosis of Gastric Cancer Based on Immunohistochemical Method]

Objective

To observe the relationship between the expression of human matricellular protein 3 (MATN3) and the pathological features, drug resistance, and prognosis of gastric cancer based on immunohistochemical method.

Methods

A total of 100 gastric cancer patients treated at the First Affiliated Hospital of Bengbu Medical College from January 2022 to December 2022 were included. MATN3 expression in gastric cancer tissues and paracancerous tissues was assessed by immunohistochemistry. The expression of MATN3 was compared across pathological features. Patients were divided into sensitive and resistant groups based on chemotherapy resistance, and MATN3 expression was compared between these groups. The relationship between MATN3 and recurrence-free survival (RFS) and overall survival (OS) of gastric cancer patients was analyzed using Kaplan-Meier survival curves. Univariate and multifactorial Cox regression analyses were used to analyze the factors affecting the prognosis of gastric cancer patients. Human gastric cancer cells MGC803 were transfected with MATN3. The cells were divided into a high expression group (LV-MATN3 group) and its control group (LV-NC group) and a low expression group (sh-MATN3 group) and its control group (sh-NC group). Cell proliferation was assessed using the CCK8 assay, cell migration and invasion were assessed using the Transwell assay, and MATN3 mRNA expression levels were measured using RT-qPCR. A nude mouse xenograft model was constructed by hypodermic injection of MGC-803 cells transfected with MATN3, and MATN3 mRNA expression levels in tumor tissues were measured using RT-qPCR.

Results

Immunohistochemical results showed a significantly higher rate of high MATN3 expression in gastric cancer tissues (64.00%, 64/100) compared to adjacent non-cancerous tissues (31.00%, 31/100) (P<0.05). High MATN3 expression was associated with age ≥60 years old, tumor location in the gastric body, tumor size ≥5 cm, lymph node metastasis (N1-N3), histological differentiation (moderate to high), tumor invasion depth (T3-T4), TNM stage (Ⅲ-Ⅳ), distant organ metastasis, recurrence, and mortality (P<0.05). Among patients with chemotherapy resistance, the high MATN3 expression rate was 79.49% (31/39) in the resistant group compared to 54.10% (33/61) in the sensitive group (P<0.05). Follow-up duration ranged from 11 to 22 months, with a 97.00% follow-up rate and 3 cases lost to follow-up. Kaplan-Meier survival curve analysis showed that patients with high MATN3 expression had significantly lower RFS and OS compared to those with low MATN3 expression (RFS: log-rank=17.291, P<0.001; OS: log-rank=21.719, P<0.001). Multivariate Cox analysis identified high MATN3 expression (hazard ratio [HR]=2.291, 95% confidence interval [CI]: 1.268-5.392), tumor location in the gastric body (HR=2.057, 95% CI: 1.441-5.666), lymph node metastasis (N1-N3) (HR=2.011, 95% CI: 1.010-2.274), tumor invasion depth (T3-T4) (H=2.977, 95% CI: 1.032-7.853), TNM stage Ⅲ-Ⅳ (HR=2.008, 95% CI: 1.049-3.902), and distant organ metastasis (HR=2.505, 95% CI: 1.529-5.000) as independent risk factors affecting RFS and OS (P<0.05). Cell and animal experiments demonstrated that compared to the LV-NC group, the LV-MATN3 group exhibited significantly higher cell proliferation, migration, and invasion (P<0.05), as well as increased tumor volume and MATN3 mRNA expression in tumor tissues (P<0.05). Conversely, the sh-MATN3 group showed significantly reduced cell proliferation, migration, and invasion, along with decreased tumor volume and MATN3 mRNA levels compared to the sh-NC group (P<0.05).

Conclusion

MATN3 is highly expressed in gastric cancer tissues and is associated with various pathological features, drug resistance and poor prognosis. MATN3 holds potential as a diagnostic marker for poor prognosis and may play a role in the malignant behaviors of gastric cancer cells, including proliferation, migration, and invasion.

Construction of a prognostic risk model for Stomach adenocarcinoma based on endoplasmic reticulum stress genes

OBJECTIVE

Stomach adenocarcinoma (STAD) is caused by malignant transformation of gastric glandular cells and is characterized by a high incidence rate and a poor prognosis. This study was designed to establish a prognostic risk model for STAD according to endoplasmic reticulum (ER) stress feature genes as cancer cells are susceptible to ER stress.

METHODS

The TCGA-STAD dataset was downloaded to screen differentially expressed genes (DEGs). By intersecting DEGs with ER stress genes retrieved from GeneCards, ER stress-related DEGs in STAD were obtained. Kmeans cluster analysis of STAD subtypes and Single sample gene set enrichment analysis (ssGSEA) analysis of immune infiltration were performed. Cox regression analysis was utilized to construct a risk prognostic model. Samples were split into high-risk and low-risk groups according to the median risk score. Survival analysis and Receiver Operating Characteristic (ROC) curves were conducted to assess the validity of the model. Gene set enrichment analysis (GSEA) was performed to investigate differential pathways in the two risk groups. Cox analysis was performed to verify the independence of the risk model, and a nomogram was generated.

RESULTS

A total of 162 ER stress-related DEGs in STAD were identified by bioinformatics analysis. Kmeans cluster analysis showed that STAD was divided into 3 subgroups. The ssGSEA showed that the levels of immune infiltration in subgroups 2 and 3 were significantly higher than subgroup 1. With 12 prognostic genes (MATN3, ATP2A1, NOX4, AQP11, HP, CAV1, STARD3, FKBP10, EGF, F2, SERPINE1, CNGA3) selected from ER stress-related DEGs using Cox regression analysis, we then constructed a prognostic model. Kaplan-Meier (K‑M) survival curves and ROC curves showed good prediction performance of the model. Significant enrichment of genes in the high-risk group was found in extracellular matrix (ECM) receptor interaction. Cox regression analysis combined with clinical factors showed that the risk model could be used as an independent prognostic factor. The prediction correction curve showed that the good prediction ability of the nomogram.

CONCLUSION

The STAD could be divided into three subgroups, and the 12-gene model constructed by ER stress signatures had a good prognostic performance for STAD patients.

Unusual Suspects: Bone and Cartilage ECM Proteins as Carcinoma Facilitators

The extracellular matrix (ECM) is the complex three-dimensional network of fibrous proteins and proteoglycans that constitutes an essential part of every tissue to provide support for normal tissue homeostasis. Tissue specificity of the ECM in its topology and structure supports unique biochemical and mechanical properties of each organ. Cancers, like normal tissues, require the ECM to maintain multiple processes governing tumor development, progression and spread. A large body of experimental and clinical evidence has now accumulated to demonstrate essential roles of numerous ECM components in all cancer types. Latest findings also suggest that multiple tumor types express, and use to their advantage, atypical ECM components that are not found in the cancer tissue of origin. However, the understanding of cancer-specific expression patterns of these ECM proteins and their exact roles in selected tumor types is still sketchy. In this review, we summarize the latest data on the aberrant expression of bone and cartilage ECM proteins in epithelial cancers and their specific functions in the pathogenesis of carcinomas and discuss future directions in exploring the utility of this selective group of ECM components as future drug targets.

Significance of platelet adhesion-related genes in colon cancer based on non-negative matrix factorization-based clustering algorithm

Background

Although surgical methods are the most effective treatments for colon adenocarcinoma (COAD), the cure rates remain low, and recurrence rates remain high. Furthermore, platelet adhesion-related genes are gaining attention as potential regulators of tumorigenesis. Therefore, identifying the mechanisms responsible for the regulation of these genes in patients with COAD has become important. The present study aims to investigate the underlying mechanisms of platelet adhesion-related genes in COAD patients.

Methods

The present study was an experimental study. Initially, the effects of platelet number and related genomic alteration on survival were explored using real-world data and the cBioPortal database, respectively. Then, the differentially expressed platelet adhesion-related genes of COAD were analyzed using the TCGA database, and patients were further classified by employing the non-negative matrix factorization (NMF) analysis method. Afterward, some of the clinical and expression characteristics were analyzed between clusters. Finally, least absolute shrinkage and selection operator regression analysis was used to establish the prognostic nomogram. All data analyses were performed using the R package.

Results

High platelet counts are associated with worse survival in real-world patients, and alternations to platelet adhesion-related genes have resulted in poorer prognoses, based on online data. Based on platelet adhesion-related genes, patients with COAD were classified into two clusters by NMF-based clustering analysis. Cluster2 had a better overall survival, when compared to Cluster1. The gene copy number and enrichment analysis results revealed that two pathways were differentially enriched. In addition, the differentially expressed genes between these two clusters were enriched for POU6F1 in the transcription factor signaling pathway, and for MATN3 in the ceRNA network. Finally, a prognostic nomogram, which included the ALOX12 and ACTG1 genes, was established based on the platelet adhesion-related genes, with a concordance (C) index of 0.879 (0.848-0.910).

Conclusion

The mRNA expression-based NMF was used to reveal the potential role of platelet adhesion-related genes in COAD. The series of experiments revealed the feasibility of targeting platelet adhesion-associated gene therapy.

Constructing a novel gene signature derived from oxidative stress specific subtypes for predicting survival in stomach adenocarcinoma

Oxidative stress (OS) response is crucial in oncogenesis and progression of tumor. But the potential prognostic importance of OS-related genes (OSRGs) in stomach adenocarcinoma (STAD) lacked comprehensive study. STAD clinical information and transcriptome data were retrieved from the Gene Expression Omnibus and The Cancer Genome Atlas databases. The prognostic OSRGs were filtered via the univariate Cox analysis and OSRG-based molecular subtypes of STAD were developed using consensus clustering. Weighted gene co-expression network analysis (WGCNA) was subsequently conducted to filter molecular subtype-associated gene modules. The prognosis-related genes were screened via univariate and least absolute shrinkage and selection operator Cox regression analysis were used to construct a prognostic risk signature. Finally, a decision tree model and nomogram were developed by integrating risk signature and clinicopathological characteristics to analyze individual STAD patient’s survival. Four OSRG-based molecular subtypes with significant diversity were developed based on 36 prognostic OSRGs for STAD, and an OSRGs-based subtype-specific risk signature with eight genes for prognostic prediction of STAD was built. Survival analysis revealed a strong prognostic performance of the risk signature exhibited in predicting STAD survival. There were significant differences in mutation patterns, chemotherapy sensitivity, clinicopathological characteristics, response to immunotherapy, biological functions, immune microenvironment, immune cell infiltration among different molecular subtypes and risk groups. The risk score and age were verified as independent risk factors for STAD, and a nomogram integrating risk score and age was established, which showed superior predictive performance for STAD prognosis. We developed an OSRG-based molecular subtype and identified a novel risk signature for prognosis prediction, providing a useful tool to facilitate individual treatment for patients with STAD.

MDN1 Mutation Is Associated With High Tumor Mutation Burden and Unfavorable Prognosis in Breast Cancer

Background: Breast cancer (BRCA) is the most common cancer worldwide and a serious threat to human health. MDN1 mutations have been observed in several cancers. However, the associations of MDN1 mutation with tumor mutation burden (TMB) and prognosis of BRCA have not been investigated.

Methods: Genomic, transcriptomic, and clinical data of 973 patients with BRCA from The Cancer Genome Atlas (TCGA) database were analyzed. The clinical attributes of BRCA based on the MDN1 mutation status were assessed by comparing TMB and tumor infiltrating immune cells. Gene ontology analysis and gene set enrichment analysis (GSEA) were conducted to identify the key signaling pathways associated with MDN1 mutation. Moreover, univariate and multivariate Cox regression analyses were performed to assess the association between prognostic factors and survival outcomes. Finally, nomograms were used to determine the predictive value of MDN1 mutation on clinical outcomes in patients with BRCA.

Results: MDN1 was found to have a relatively high mutation rate (2.77%). Compared to the MDN1 wild-type patients, the TMB value was significantly higher in MDN1 mutant patients (p < 0.001). Prognostic analysis revealed that MDN1 mutant patients had a worse survival probability than MDN1 wild-type patients (hazard ratio = 2.91; 95% CI:1.07–7.92; p = 0.036). GSEA revealed samples with MDN1 mutation enriched in retinol metabolism, drug metabolism cytochrome P450, glucuronidation, miscellaneous transport, and binding event pathways.

Conclusion: MDN1 mutation was found to be associated with high TMB and inferior prognosis, suggesting that MDN1 mutation may play a potential role in prognosis prediction and immunotherapy guidance in BRCA.