Novel Prognostic Biomarkers in Gastric Cancer: CGB5, MKNK2, and PAPPA2

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.

Cancer-associated MDM2 W329G mutant attenuates ribosomal stress-mediated p53 responses to promote cell survival and glycolysis

Although amplification/overexpression is the predominant mechanism for the oncogenic properties of MDM2, an increasing number of MDM2 somatic missense mutations were identified in cancer patients with the recent advances in sequencing technology. Here, we characterized an MDM2 cancer-associated mutant variant W329G identified from a patient sample that contains a wild-type p53 gene. Trp329 is one of residues that were reported to be critical to MDM2's binding to ribosomal protein L11 (RPL11). We found that the MDM2 W329G mutant was resistant to the inhibitory effect of RPL11 on MDM2-mediated p53 ubiquitination and degradation, in line with its defect on RPL11 binding. Using isogenic U2OS cells with or without endogenous MDM2 W329G mutation, we demonstrated that the expression of classic p53 targets induced by ribosomal stress signals was reduced in mutant cells. RNA-seq analysis revealed that upon 5-FU treatment, the p53 response was significantly impaired. Also, the 5-FU-mediated repression of genes in cell cycle progression and DNA replication was diminished in W329G mutant-containing cells. Physiologically, U2OS W329G cells were more resistant to cell growth inhibition induced by ribosomal stress and exhibited higher glycolytic rates upon 5-FU treatment. Together, our data indicated that cancer-associated MDM2 W329G mutant attenuates ribosomal stress-mediated p53 responses to promote cell survival and glycolysis.

Development and validation of a tobacco smoking-related index for predicting overall survival and immunotherapy response in bladder cancer

Bladder cancer is one of the top five most prevalent cancers in the United States and a major cause of cancer-related mortality worldwide. Meanwhile, tobacco smoking is a well-established modifiable risk factor for bladder cancer, with a population-attributable risk of approximately 50%. But the relationship between the prognosis of bladder cancer and tobacco smoking remains unclear. To further explore the potential relationship between tobacco smoking and bladder cancer prognosis, the bladder cancer dataset from The Cancer Genome Atlas Program was used to build a tobacco smoking-related signature known as the "smoker index" for prognosis prediction. Additionally, we validated the efficacy of the signature with some external datasets. Finally, we preliminarily verified the role of CGB5, the hub gene in the smoker index, through pan-cancer analysis and in vitro assays. The study digs into the underlying connection between tobacco smoking and the prognosis of bladder cancer from a multi-omics perspective.

Unveiling DNA damage repair-based molecular subtypes, tumor microenvironment and pharmacogenomic landscape in gastric cancer

Objective: The current molecular classification system for gastric cancer covers genomic, molecular, and morphological characteristics. Non-etheless, classification of gastric cancer based upon DNA damage repair is still lacking. Here, we defined DNA damage repair-based subtypes across gastric cancer and identified clinicopathological, tumor microenvironment and pharmacogenomic features. Methods: Unsupervised clustering analysis was executed in the TCGA-STAD cohort based upon the transcriptional expression profiling of DNA damage repair genes. LASSO computational approach was adopted for generating a DNA damage repair-relevant gene signature. The identified subtypes or signature were externally verified in the GSE84426 or GSE84433 cohort. The transcriptional levels of immunomodulators, abundance of immune cells and somatic mutations were measured, respectively. Immunotherapeutic response, and drug sensitivity were investigated. The DNA damage repair-relevant genes were further experimentally verified. Results: Two DNA damage repair-based subtypes were identified, with the notable heterogeneity in prognostic stratification, tumor microenvironment and somatic mutations. The gene signature was generated for risk stratification and prognostic prediction, which was in relation to immunomodulators and immune cells. High-risk cases were more likely to respond to immunotherapy, with distinct pharmacogenomic landscapes between low- and high-risk groups. Higher levels of PAPPA2, MPO, MAGEA11, DEPP1, CPZ, and COLEC12 and lower level of CYTL1 were proven in gastric cancer cells versus controls. Silencing CYTL1 facilitated intracellular ROS accumulation and suppressed migration in gastric cancer cells. Conclusion: Collectively, the DNA damage repair-based classification is a suitable complement to existing molecular classification system, and the quantitative gene signature provides a robust tool in selecting specific therapeutic options.

Identification and characterization of a novel molecular classification incorporating oxidative stress and metabolism-related genes for stomach adenocarcinoma in the framework of predictive, preventive, and personalized medicine

BACKGROUND

Stomach adenocarcinoma (STAD) is one of the primary contributors to deaths that are due to cancer globally. At the moment, STAD does not have any universally acknowledged biological markers, and its predictive, preventive, and personalized medicine (PPPM) remains sufficient. Oxidative stress can promote cancer by increasing mutagenicity, genomic instability, cell survival, proliferation, and stress resistance pathways. As a direct and indirect result of oncogenic mutations, cancer depends on cellular metabolic reprogramming. However, their roles in STAD remain unclear.

METHOD

743 STAD samples from GEO and TCGA platforms were selected. Oxidative stress and metabolism-related genes (OMRGs) were acquired from the GeneCard Database. A pan-cancer analysis of 22 OMRGs was first performed. We categorized STAD samples by OMRG mRNA levels. Additionally, we explored the link between oxidative metabolism scores and prognosis, immune checkpoints, immune cell infiltration, and sensitivity to targeted drugs. A series of bioinformatics technologies were employed to further construct the OMRG-based prognostic model and clinical-associated nomogram.

RESULTS

We identified 22 OMRGs that could evaluate the prognoses of patients with STAD. Pan-cancer analysis concluded and highlighted the crucial part of OMRGs in the appearance and development of STAD. Subsequently, 743 STAD samples were categorized into three clusters with the enrichment scores being C2 (upregulated) > C3 (normal) > C1 (downregulated). Patients in C2 had the lowest OS rate, while C1 had the opposite. Oxidative metabolic score significantly correlates with immune cells and immune checkpoints. Drug sensitivity results reveal that a more tailored treatment can be designed based on OMRG. The OMRG-based molecular signature and clinical nomogram have good accuracy for predicting the adverse events of patients with STAD. Both transcriptional and translational levels of ANXA5, APOD, and SLC25A15 exhibited significantly higher in STAD samples.

CONCLUSION

The OMRG clusters and risk model accurately predicted prognosis and personalized medicine. Based on this model, high-risk patients might be identified in the early stage so that they can receive specialized care and preventative measures, and choose targeted drug beneficiaries to deliver individualized medical services. Our results showed oxidative metabolism in STAD and led to a new route for improving PPPM for STAD.

A novel necroptosis-related gene index for predicting prognosis and a cold tumor immune microenvironment in stomach adenocarcinoma

Background

Gastric cancer (GC) represents a major global clinical problem with very limited therapeutic options and poor prognosis. Necroptosis, a recently discovered inflammatory form of cell death, has been implicated in carcinogenesis and inducing necroptosis has also been considered as a therapeutic strategy.

Objective

We aim to evaluate the role of this pathway in gastric cancer development, prognosis and immune aspects of its tumor microenvironment.

Methods and results

In this study, we evaluated the gene expression of 55 necroptosis-related genes (NRGs) that were identified via carrying out a comprehensive review of the medical literature. Necroptosis pathway was deregulated in gastric cancer samples (n=375) as compared to adjacent normal tissues (n=32) obtained from the “The Cancer Genome Atlas (TCGA)”. Based on the expression of these NRGs, two molecular subtypes were obtained through consensus clustering that also showed significant prognostic difference. Differentially expressed genes between these two clusters were retrieved and subjected to prognostic evaluation via univariate cox regression analysis and LASSO cox regression analysis. A 13-gene risk signature, termed as necroptosis-related genes prognostic index (NRGPI), was constructed that comprehensively differentiated the gastric cancer patients into high- and low-risk subgroups. The prognostic significance of NRGPI was validated in the GEO cohort (GSE84437: n=408). The NRGPI-high subgroup was characterized by upregulation of 10 genes (CYTL1, PLCL1, CGB5, CNTN1, GRP, APOD, CST6, GPX3, FCN1, SERPINE1) and downregulation of 3 genes (EFNA3, E2F2, SOX14). Further dissection of these two risk groups by differential gene expression analysis indicated involvement of signaling pathways associated with cancer cell progression and immune suppression such as WNT and TGF-β signaling pathway. Para-inflammation and type-II interferon pathways were activated in NRGPI-high patients with an increased infiltration of Tregs and M2 macrophage indicating an exhausted immune phenotype of the tumor microenvironment. These molecular characteristics were mainly driven by the eight NRGPI oncogenes (CYTL1, PLCL1, CNTN1, GRP, APOD, GPX3, FCN1, SERPINE1) as validated in the gastric cancer cell lines and clinical samples. NRGPI-high patients showed sensitivity to a number of targeted agents, in particular, the tyrosine kinase inhibitors.

Conclusions

Necroptosis appears to play a critical role in the development of gastric cancer, prognosis and shaping of its tumor immune microenvironment. NRGPI can be used as a promising prognostic biomarker to identify gastric cancer patients with a cold tumor immune microenvironment and poor prognosis who may response to selected molecular targeted therapy.

A novel prognostic index of stomach adenocarcinoma based on immunogenomic landscape analysis and immunotherapy options

Stomach adenocarcinoma (STAD) is one of the most common malignant tumors worldwide. In this study, we attempted to construct a valid immune-associated gene prognostic index risk model that can predict the survival of patients with STAD and the efficacy of immune checkpoint inhibitors (ICIs) treatment. Transcriptome, clinical, and gene mutational data were obtained from the TCGA database. Immune-related genes were downloaded from the ImmPort and InnateDB databases. A total of 493 immune-related genes were identified to be enriched in functions associated with immune response, as well as in immune and tumor-related pathways. Further, 36 candidate genes related to the overall survival (OS) of STAD were obtained by weighted gene co-expression network analysis (WGCNA). Next, based on a Cox regression analysis, we constructed an immune-associated gene prognostic index (IAGPI) risk model based on eight genes, which was verified using the GEO STAD cohort. The patients were divided into two subsets according to their risk score. Patients in the low-risk group had better OS than those in the high-risk group. In the low-risk group, there were more CD8, activated memory CD4, and follicular helper T cells, and M1 macrophages, whereas monocytes, M2 macrophages, eosinophils, and neutrophils were more abundant in the high-risk group. The patients in the low-risk group were more sensitive to ICIs therapy. The IAGPI risk model can precisely predict the prognosis, reflect the tumor immune microenvironment, and predict the efficacy of ICIs therapy in patients with STAD.

ACAN, MDFI, and CHST1 as Candidate Genes in Gastric Cancer: A Comprehensive Insilco Analysis

BACKGROUND

Gastric cancer (GC) is a complex disorder with an inadequate response to treatment. Although many efforts have been made to clarify the development of GC, the exact etiology and molecular mechanisms of this malignancy remain unclear. This study was designed to identify and characterize essential associated genes with GC to construct a prognostic model.

METHODS

In this Insilco study, the gene expression microarray dataset GSE122401 was downloaded from the Gene Expression Omnibus (GEO). The raw data were processed and quantile-normalized with the edgeR package of R ver.3.5.3. The module-trait relationship and hub-genes associated with GC were analyzed with Weighted Gene Co-expression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Cluepedia and Enrichr Database. Finally, hub-genes were screened and validated by GEPIA online database.

RESULTS

According to the WGCNA results, the blue module was found to be strongly correlated with the GC (r=0.91, p-value=1e-57). DEGs analysis was performed by edgeR package of R and indicated a total of 47 genes as hub-genes. Verifying the hub-genes expression using GEPIA online database showed a significantly increased level of ACAN gene expression in primary cancer cell line compared to metastatic cell line. On the other hand, the expression of MDFI and CHST1 genes in primary cell lines were lower compared to metastatic cancer cell lines.

CONCLUSIONS

This study provides a framework of the co-expression gene modules ACAN, MDFI, and CHST1 as hub-genes. These hub-genes might offer candidate biomarkers to targeted therapy against GC. Further experiment validation and animal models are needed to reveal the exact mechanism of the above-mentioned genes in the pathogenesis and prognoses of GC.