Identification of MATN3 as a Novel Prognostic Biomarker for Gastric Cancer through Comprehensive TCGA and GEO Data Mining

MATN4 as a target gene of HIF-1α promotes the proliferation and metastasis of osteosarcoma

BACKGROUND

Osteosarcoma is a highly malignant bone tumor that exhibits rapid growth and early metastasis. Hypoxia plays a pivotal role in promoting the proliferation and metastasis of osteosarcoma through a series of molecular events, which are partially mediated and regulated by HIF-1α. However, the regulatory network associated with HIF-1α in osteosarcoma remains limited. Therefore, the objective of this study was to identify critical hypoxia-associated genes and investigate their effects and molecular mechanisms in osteosarcoma cells.

METHODS

Through bioinformatics analysis, matrilin-4 (MATN4) was identified as a crucial gene associated with hypoxia. The expression of MATN4 and HIF-1α was assessed using immunohistochemistry, RT-qPCR, and western blotting. The proliferative capacity of osteosarcoma cells was assessed through the utilization of CCK-8, EDU staining, and colony formation assays. The effects of MATN4 on the mobility of OS cells were evaluated using wound-healing assays and transwell assays. The interaction between MATN4 and HIF-1α was detected through chromatin immunoprecipitation.

RESULTS

MATN4 is overexpressed in osteosarcoma tissue and cells, particularly in osteosarcoma cells with high metastatic potential. Knockdown of MATN4 inhibits the proliferation, migration, and invasion abilities of osteosarcoma cells and reverses the promoting effects of hypoxia on these functions. Additionally, HIF-1α binds to MATN4 and upregulates its expression. Interestingly, knockdown of HIF-1α reduces the stimulatory effects of MATN4 overexpression on the proliferation, migration, and invasion of osteosarcoma cells under hypoxic conditions.

CONCLUSIONS

Taken together, our results suggest that MATN4 is regulated by HIF-1α and confers a more aggressive phenotype on OS cells. This evidence suggests that MATN4 may act as a potential target for OS diagnosis and treatment.

MATN2 overexpression suppresses tumor growth in ovarian cancer via PTEN/PI3K/AKT pathway

The incidence rate of developing ovarian cancer decreases over the years; however, mortality ranks top among malignancies of women, mainly metastasis through local invasion. Matrilin-2 (MATN2) is a member of the matrilin family that plays an important role in many cancers. However, its relationship with ovarian cancer remains unknown. Our study aimed to explore the function and possible mechanism of MATN2 in ovarian cancer. Human ovarian cancer tissue microarrays were used to detect the MATN2 expression in different types of ovarian cancer using immunohistochemistry (IHC). CCK-8, wound scratch healing assay, transwell assay, and flow cytometry were used to detect cell mobility. Gene and protein expression were detected using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. MATN2 interacts with phosphatase, and the tensin homolog (PTEN) deleted on chromosome 10 was analyzed using TCGA database and co-immunoprecipitation (Co-IP). In vivo experiments were conducted using BALB/c nude mice, and tumor volume and weight were recorded. Tumor growth was determined using hematoxylin and eosin (H&E) and IHC staining. MATN2 was significantly downregulated in ovarian cancer cells. The SKOV3 and A2780 cell mobility was significantly inhibited by MATN2 overexpression, while the cell apoptosis rate was significantly increased. MATN2 overexpression decreased transplanted tumor size in vivo. These results were reversed by inhibiting MATN2. Furthermore, we found that PTEN closely interacted with MATN2 using bioinformatics and Co-IP. MATN2 overexpression significantly inhibited the PI3K/AKT pathway, however, PTEN suppression reversed this effect of MATN2 overexpression. These results indicated that MATN2 may play a critical role in ovarian cancer development by inhibiting cells proliferation and migration. The mechanism was related to interacting with PTEN, thus inhibiting downstream effectors in the PI3K/AKT pathway, which may be a novel target for treating ovarian cancer.

Machine learning-based cell death signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is a one of most common malignancies with high mortality-to-incidence ratio. Programmed cell death (PCD) exerts vital functions in the progression of cancer. The role of PCD-related genes (PRGs) in STAD are not fully clarified. Using TCGA, GSE15459, GSE26253, GSE62254 and GSE84437 datasets, PCD-related signature (PRS) was constructed with an integrative procedure including 10 machine learning methods. The role of PRS in predicting the immunotherapy benefits was evaluated by several predicting score and 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210). The model developed by Lasso + CoxBoost algorithm having a highest average C-index of 0.66 was considered as the optimal PRS. As an independent risk factor for STAD patients, PRS had a good performance in predicting the overall survival rate of patients, with an AUC of 1-, 3-, and 5-year ROC curve being 0.771, 0.751 and 0.827 in TCGA cohort. High PRS score demonstrated a lower gene set score of some immune-activated cells and immune-activated activities. Patient with high PRS score had a higher TIDE score, higher immune escape score, lower PD1&CTLA4 immunophenoscore, lower TMB score, lower response rate and poor prognosis, indicating a less immunotherapy response. The IC50 value of some drugs correlated with chemotherapy and targeted therapy was higher in high PRS score group. Our investigation developed an optimal PRS in STAD and it acted as an indicator for predicting the prognosis, stratifying risk and guiding treatment for STAD patients.

The phase separation of extracellular matrix protein matrilin-3 from cancer-associated fibroblasts contributes to gastric cancer invasion

Cancer-associated fibroblast (CAF) has emerged as a key contributor to the remodeling of tumor microenvironment through the expression and secretion of extracellular matrix (ECM) proteins, thereby promoting carcinogenesis. However, the precise contribution of ECM proteins from CAFs to gastric carcinogenesis remains poorly understood. In this study, we find that matrilin-3 (MATN3), an upregulated ECM protein associated with poorer prognosis in gastric cancer patients, originates from CAFs in gastric cancer tissues. Ectopic expression of MATN3 in CAFs significantly promotes the invasion of gastric cancer cells, which can be attenuated by neutralizing MATN3 with its antibody. Notably, a portion of MATN3 protein is found to form puncta in gastric cancer tissues ECM. MATN3 undergoes phase separation, which is mediated by its low complexity (LC) and coiled-coil (CC) domains. Moreover, overexpression of MATN3 deleted with either LC or CC in CAFs is unable to promote the invasion of gastric cancer cells, suggesting that LC or CC domain is required for the effect of CAF-secreted MATN3 in gastric cancer cell invasion. Additionally, orthotopic co-injection of gastric cancer cells and CAFs expressing MATN3, but not its ΔLC and ΔCC mutants, leads to enhanced gastric cancer cell invasion in mouse models. Collectively, our works suggest that MATN3 is secreted by CAFs and undergoes phase separation, which promotes gastric cancer invasion.

Integrative analysis of cancer-associated fibroblast signature in gastric cancer

Background

CAFs regulate the signaling of GC cells by promoting their migration, invasion, and proliferation and the function of immune cells as well as their location and migration in the TME by remodeling the extracellular matrix (ECM). This study explored the understanding of the heterogeneity of CAFs in TME and laid the groundwork for GC biomarker and precision treatment development.

Methods

The scRNA-seq and bulk RNA-seq datasets were obtained from GEO and TCGA. The prognostic significance of various CAFs subtypes was investigated using ssGSEA combined with Kaplan-Meier analysis. POSTN expression in GC tissues and CAFs was detected using immunohistochemistry, immunofluorescence, and Western blotting. Differential expression analysis identified the differentially expressed genes (DEGs) between normal and tumor samples in TCGA-STAD. Pearson correlation analysis identified DEGs associated with adverse prognosis CAF subtype, and univariate Cox regression analysis determined prognostic genes associated with CAFs. LASSO regression analysis and Multivariate Cox regression were used to build a prognosis model for CAFs.

Results

We identified five CAFs subtypes in GC, with the CAF_0 subtype associated with poor prognosis. The abundance of CAF_0 correlated with T stage, clinical stage, histological type, and immune cell infiltration levels. Periostin (POSTN) exhibited increased expression in both GC tissues and CAFs and was linked to poor prognosis in GC patients. Through LASSO and multivariate Cox regression analysis, three genes (CXCR4, MATN3, and KIF24) were selected to create the CAFs-score. We developed a nomogram to facilitate the clinical application of the CAFs-score. Notably, the CAFs signature showed significant correlations with immune cells, stromal components, and immunological scores, suggesting its pivotal role in the tumor microenvironment (TME). Furthermore, CAFs-score demonstrated prognostic value in assessing immunotherapy outcomes, highlighting its potential as a valuable biomarker to guide therapeutic decisions.

Conclusion

CAF_0 subtype in TME is the cause of poor prognosis in GC patients. Furthermore, CAFs-score constructed from the CAF_0 subtype can be used to determine the clinical prognosis, immune infiltration, clinicopathological characteristics, and assessment of personalized treatment of GC patients.

Identification of a Novel Protein-Based Prognostic Model in Gastric Cancers

Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide. However, there are still no reliable biomarkers for the prognosis of this disease. This study aims to construct a robust protein-based prognostic prediction model for GC patients. The protein expression data and clinical information of GC patients were downloaded from the TCPA and TCGA databases, and the expressions of 218 proteins in 352 GC patients were analyzed using bioinformatics methods. Additionally, Kaplan-Meier (KM) survival analysis and univariate and multivariate Cox regression analysis were applied to screen the prognosis-related proteins for establishing the prognostic prediction risk model. Finally, five proteins, including NDRG1_pT346, SYK, P90RSK, TIGAR, and XBP1, were related to the risk prognosis of gastric cancer and were selected for model construction. Furthermore, a significant trend toward worse survival was found in the high-risk group (p = 1.495 × 10-7). The time-dependent ROC analysis indicated that the model had better specificity and sensitivity compared to the clinical features at 1, 2, and 3 years (AUC = 0.685, 0.673, and 0.665, respectively). Notably, the independent prognostic analysis results revealed that the model was an independent prognostic factor for GC patients. In conclusion, the robust protein-based model based on five proteins was established, and its potential benefits in the prognostic prediction of GC patients were demonstrated.

A molecular classification of gastric cancer associated with distinct clinical outcomes and validated by an XGBoost-based prediction model

Gastric cancer (GC) is a heterogeneous disease and a leading cause of cancer-related deaths. Discovering robust, clinically relevant molecular classifications is critical for guiding personalized therapies for GC. Here, we propose a refined molecular classification scheme for GC using integrated optimal algorithms and multi-omics data. Based on the important features of mRNA, microRNA, and DNA methylation data selected by the multivariate Cox regression model, three subtypes linked to distinct clinical outcomes were identified by combining similarity network fusion and consensus clustering methods. Three subtypes were validated by an extreme gradient boosting machine learning prediction model with 125 differentially expressed genes in multiple independent cohorts. The molecular characteristics of mutation signatures, characteristic gene sets, driver genes, and chemotherapy sensitivity for each subtype were also identified: subtype 1 was associated with favorable prognosis and characterized by high ARID1A and PIK3CA mutations, subtype 2 was associated with a poor prognosis and harbored high recurrent TP53 mutations, and subtype 3 was associated with high CHD1, APOA1 mutations, and a poor prognosis. The proposed three-subtype scheme achieved a better clinical prediction performance (area under the curve value = 0.71) than The Cancer Genome Atlas classification, which may provide a practical subtyping framework to improve the treatment of GC.

Gastric Cancer Subtypes in Tumour and Nontumour Tissues by Immunologic and Hallmark Gene Sets

A previous research study on differentiating gastric cancer (GC) into distinct subtypes or prognostic models was mostly based on GC tissues, which neglected the role of nontumour tissues in GC subtypes. The purpose of the research was to identify GC subtypes on the basis of tumour and adjacent nontumour tissues to assess the prognosis of GC patients. We characterized three GC subtypes on the basis of the immunologic and hallmark gene sets in GC and adjacent nontumour tissues: among them, the GC patients with subtype I had the longest survival time compared to patients with other subtypes. The classification was closely associated with T stage and pathological stage of GC patients. A prognostic model containing two gene sets was constructed by LASSO analysis. Kaplan–Meier analysis showed that patients in the high-risk group survived longer than those in the low-risk group and the two prognostic genes sets in the model were strongly correlated with survival status. Then, GO and KEGG analyses and PPI network show that nontumour and tumour tissues are influencing the prognosis of GC patients in separate manners. In summary, we emphasized the prognostic value of nontumour tissue in GC patients and proposed a novel insight that both changes in tumour and nontumour tissues should be taken into account when selecting a treatment strategy for GC.

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.