Identification and validation of a novel angiogenesis-related gene signature for predicting prognosis in gastric adenocarcinoma

The Methylation and Expression of LINC00511, an Important Angiogenesis-Related lncRNA in Stomach Adenocarcinoma

Stomach adenocarcinoma (STAD) has high incidence and mortality rates. Long non-coding RNAs (lncRNAs) and angiogenesis are closely related to the pathogenesis and metastasis of STAD. Recently, emerging evidence demonstrated that DNA methylation plays crucial roles in the development of STAD. This study explored the relationship between DNA methylation and the abnormal expression of angiogenesis-related lncRNAs (ARlncRNAs) in stomach adenocarcinoma, aiming to identify prognostic biomarkers. Moreover, a Cox analysis and Lasso regression were used to establish an ARlncRNA feature set related to angiogenesis. The prognostic model was evaluated by using a Kaplan-Meier (KM) analysis, ROC curves, and nomograms. Based on the identified 18 key ARlncRNAs, a prognostic predictive model was constructed. In addition, a specific ARlncRNA with abnormal methylation in the model, LINC00511, showed significant differences in expression and methylation across different subgroups. The methylation and expression of LINC00511 were analyzed by a correlation and co-expression analysis. The correlation analysis indicated that promoter methylation may improve LINC00511 expression. Further analysis found 355 mRNAs co-expressed with LINC00511 which may interact with 6 miRNAs to regulate target gene expression. The abnormal methylation of LINC00511 could significantly contribute to the progression of stomach adenocarcinoma.

Identification the Cellular Senescence Associated lncRNA LINC01579 in Gastric Cancer

Cellular senescence is a key promoter of tumorigenesis and malignant progression. This study aimed to develop a predictive model for assessing cellular senescence in gastric cancer (GC) outcomes. We identified senescence-related genes and lncRNAs from 375 stomach adenocarcinoma (STAD) patients and established a prognostic senescence score using multivariate Cox regression, validated in testing, TCGA-STAD and the combined TCGA-COAD and READ cohorts. The model's predictive efficacy was evaluated across clinical subgroups, tumour microenvironments and immune cell infiltration. A total of 116 senescence-related lncRNAs were filtered, and patients were clustered into two senescent subtypes. The lncRNA signature identified LINC01579 as an independent prognostic factor for GC. Low-risk groups showed prolonged overall survival, increased immune cell infiltration and reduced mutation load. Downregulation of LINC01579 using antisense oligonucleotides (ASOs) on normal human fibroblasts decreased cellular proliferation and migration in GC. Collectively, this study established and validated a promising prognostic model connecting senescence-related lncRNAs and clinical outcome in GC and provided potential senescence-related biomarkers for GC prognosis prediction.

LINC01094 promotes gastric cancer through dual targeting of CDKN1A by directly binding RBMS2 and HDAC1

BACKGROUND

Accumulating studies have focused on long noncoding RNAs (lncRNAs) because of their regulatory effects on multiple cancers. However, the biological functions and molecular mechanisms of lncRNAs in gastric cancer (GC) remain to be elucidated in depth.

METHODS

Long intergenic nonprotein coding RNA 1094 (LINC01094), a differentially expressed lncRNA between GC tissues and adjacent normal tissues, was identified. Moreover, gain- and loss-of-function experiments in vitro and in vivo were carried out. To understand the mechanisms underlying the regulatory effects of LINC01094, we performed RNA pull-down assays, RNA immunoprecipitation assays, chromatin immunoprecipitation assays, luciferase reporter assays, etc. RESULTS: LINC01094 was markedly upregulated in GC tissues and cell lines, and LINC01094 upregulation was positively correlated with GC malignant behaviours in vitro and in vivo. Mechanistically, LINC01094 downregulated the expression of CDKN1A by interacting with RNA binding motif single stranded interacting protein 2 (RBMS2) and histone deacetylase 1 (HDAC1). Additionally, LINC01094 was confirmed to sponge miR-128-3p and participate in the LINC01094-miR-128-3p-RUNX family transcription factor 1 (RUNX1) feedback loop. Finally, Ro 5-3335, a validated RUNX1 inhibitor, was explored for anticancer drug development in GC.

CONCLUSIONS

The LINC01094-miR-128-3p-RUNX1 feedback loop downregulates CDKN1A and promotes GC cooperatively with RBMS2 and HDAC1. Furthermore, Ro 5-3335 may hold promising therapeutic potential in the treatment of GC. Hence, our study found an oncogenic lncRNA, LINC01094, which could be a promising target for cancer treatment and diagnosis.

TREMSUCS-TCGA - an integrated workflow for the identification of biomarkers for treatment success

Many publicly available databases provide disease related data, that makes it possible to link genomic data to medical and meta-data. The cancer genome atlas (TCGA), for example, compiles tens of thousand of datasets covering a wide array of cancer types. Here we introduce an interactive and highly automatized TCGA-based workflow that links and analyses epigenomic and transcriptomic data with treatment and survival data in order to identify possible biomarkers that indicate treatment success. TREMSUCS-TCGA is flexible with respect to type of cancer and treatment and provides standard methods for differential expression analysis or DMR detection. Furthermore, it makes it possible to examine several cancer types together in a pan-cancer type approach. Parallelisation and reproducibility of all steps is ensured with the workflowmanagement system Snakemake. TREMSUCS-TCGA produces a comprehensive single report file which holds all relevant results in descriptive and tabular form that can be explored in an interactive manner. As a showcase application we describe a comprehensive analysis of the available data for the combination of patients with squamous cell carcinomas of head and neck, cervix and lung treated with cisplatin, carboplatin and the combination of carboplatin and paclitaxel. The best ranked biomarker candidates are discussed in the light of the existing literature, indicating plausible causal relationships to the relevant cancer entities.

Machine learning based intratumor heterogeneity signature for predicting prognosis and immunotherapy benefit in stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is a prevalent malignancy that is highly aggressive and heterogeneous. Intratumor heterogeneity (ITH) showed strong link to tumor progression and metastasis. High ITH may promote tumor evolution. An ITH-related signature (IRS) was created using as integrative technique including 10 machine learning methods based on TCGA, GSE15459, GSE26253, GSE62254 and GSE84437 datasets. The relevance of IRS in predicting the advantages of immunotherapy was assessed using a number of prediction scores and three immunotherapy datasets (GSE78220, IMvigor210 and GSE91061). Vitro experiments were performed to verify the biological functions of AKR1B1. The RSF + Enet (alpha = 0.1) projected model was proposed as the ideal IRS because it had the highest average C-index. The IRS demonstrated a strong performance in serving as an independent risk factor for the clinical outcome of STAD patients. It performed exceptionally well in predicting the overall survival rate of STAD patients, as seen by the TCGA cohort's AUC of 1-, 3-, and 5-year ROC curves, which were 0.689, 0.683, and 0.669, respectively. A low IRS score demonstrated a superior response to immunotherapy, as seen by a lower TIDE score, lower immune escape score, greater TMB score, higher PD1&CTLA4 immunophenoscore, higher response rate, and improved prognosis. Common chemotherapeutic and targeted treatment regimens had lower IC50 values in the group with higher IRS scores. Vitro experiment showed that AKR1B1 was upregulated in STAD and knockdown of AKR1B1 obviously suppressed tumor cell proliferation and migration. The present investigation produced the best IRS for STAD, which may be applied to prognostication, risk stratification, and therapy planning for STAD patients.

LINC01094: A key long non-coding RNA in the regulation of cancer progression and therapeutic targets

LINC01094 is a long non-coding RNA that plays a crucial role in cancer progression by modulating key signaling pathways, such as PI3K/AKT, Wnt/β-catenin and TGF-β Signaling Pathway Feedback Loop. In this review we summarize the recent research on the functional mechanisms of LINC01094 in various cancers, including its impact on tumor growth, metastasis, and resistance to therapy. We also discuss the therapeutic potential of targeting LINC01094 and highlight the current strategies and challenges in this area. Perspectives on future development of LINC01094-based therapies are also provided.

Construction and assessment of an angiogenesis-related gene signature for prognosis of head and neck squamous cell carcinoma

OBJECTIVE

Angiogenesis-associated genes (AAGs) play a critical role in cancer patient survival. However, there are insufficient reports on the prognostic value of AAGs in head and neck squamous cell carcinoma (HNSC). Therefore, this study aimed to investigate the correlation between AAG expression levels and survival in HNSC patients, explore the predictive value of signature genes and lay the groundwork for future in-depth research.

METHODS

Relevant data for HNSC were obtained from the databases. AAGs-associated signature genes linked to prognosis were screened to construct a predictive model. Further analysis was conducted to determine the functional correlation of the signature genes.

RESULTS

The signature genes (STC1, SERPINA5, APP, OLR1, and PDGFA) were used to construct prognostic models. Patients were divided into high-risk and low-risk groups based on the calculated risk scores. Survival analysis showed that patients in the high-risk group had a significantly lower overall survival than those in the low-risk group (P < 0.05). Therefore, this prognostic model was an independent prognostic factor for predicting HNSC. In addition, patients in the low-risk group were more sensitive to multiple anti-cancer drugs. Functional correlation analysis showed a good correlation between the characteristic genes and HNSC metastasis, invasion, and angiogenesis.

CONCLUSION

This study established a new prognostic model for AAGs and may guide the selection of therapeutic agents for HNSC. These genes have important functions in the tumor microenvironment; it also provides a valuable resource for the future clinical trials investigating the relationship between HNSC and AAGs.

Identification of a fatty acid metabolism-related gene signature to predict prognosis in stomach adenocarcinoma

BACKGROUND

Fatty acid metabolism (FAM) contributes to tumorigenesis and tumor development, but the role of FAM in the progression of stomach adenocarcinoma (STAD) has not been comprehensively clarified.

METHODS

The expression data and clinical follow-up information were obtained from The Cancer Genome Atlas (TCGA). FAM pathway was analyzed by gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) methods. Univariate Cox regression analysis was conducted to select prognosis genes. Molecular subtypes were classified by consensus clustering analysis. Furthermore, least absolute shrinkage and selection operator (Lasso) analysis was employed to develop a risk model. ESTIMATE and tumour immune dysfunction and exclusion (TIDE) algorithm were used to assess immunity. pRRophetic package was conducted to predict drug sensitivity.

RESULTS

Based on 14 FAM related prognosis genes (FAMRG), 2 clusters were determined. Patients in C2 showed a worse overall survival (OS). Furthermore, a 7-FAMRG risk model was established as an independent predictor for STAD, with a higher riskscore indicating an unfavorable OS. High riskscore patients had higher TIDE score and these patients were more sensitive to anticancer drugs such as Bortezomib, Dasatinib and Pazopanib. A nomogram based on riskscore was an effective prediction tool applicable to clinical settings. The results from pan-cancer analysis supported a prominent application value of riskscore model in other cancer types.

CONCLUSION

The FAMRGs model established in this study could help predict STAD prognosis and offer new directions for future studies on dysfunctional FAM-induced damage and anti-tumor drugs in STAD disease.

Comprehensive analysis of T-cell regulatory factors and tumor immune microenvironment in stomach adenocarcinoma

BACKGROUND

Gastric cancer (GC) is one of the most prevalent malignant tumors worldwide and is associated with high morbidity and mortality rates. However, the specific biomarkers used to predict the postoperative prognosis of patients with gastric cancer remain unknown. Recent research has shown that the tumor microenvironment (TME) has an increasingly positive effect on anti-tumor activity. This study aims to build signatures to study the effect of certain genes on gastric cancer.

METHODS

Expression profiles of 37 T cell-related genes and their TME characteristics were comprehensively analyzed. A risk signature was constructed and validated based on the screened T cell-related genes, and the roles of hub genes in GC were experimentally validated.

RESULTS

A novel T cell-related gene signature was constructed based on CD5, ABCA8, SERPINE2, ESM1, SERPINA5, and NMU. The high-risk group indicated lower overall survival (OS), poorer immune efficacy, and higher drug resistance, with SERPINE2 promoting GC cell proliferation, according to experiments. SERPINE2 and CXCL12 were significantly correlated, indicating poor OS via the Youjiang cohort.

CONCLUSIONS

This study identified T cell-related genes in patients with stomach adenocarcinoma (STAD) for prognosis estimation and proposed potential immunotherapeutic targets for STAD.

Construction of a gene model related to the prognosis of patients with gastric cancer receiving immunotherapy and exploration of COX7A1 gene function

BACKGROUND

GC is a highly heterogeneous tumor with different responses to immunotherapy, and the positive response depends on the unique interaction between the tumor and the tumor microenvironment (TME). However, the currently available methods for prognostic prediction are not satisfactory. Therefore, this study aims to construct a novel model that integrates relevant gene sets to predict the clinical efficacy of immunotherapy and the prognosis of GC patients based on machine learning.

METHODS

Seven GC datasets were collected from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database and literature sources. Based on the immunotherapy cohort, we first obtained a list of immunotherapy related genes through differential expression analysis. Then, Cox regression analysis was applied to divide these genes with prognostic significancy into protective and risky types. Then, the Single Sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to score the two categories of gene sets separately, and the scores differences between the two gene sets were used as the basis for constructing the prognostic model. Subsequently, Weighted Correlation Network Analysis (WGCNA) and Cytoscape were applied to further screen the gene sets of the constructed model, and finally COX7A1 was selected for the exploration and prediction of the relationship between the clinical efficacy of immunotherapy for GC. The correlation between COX7A1 and immune cell infiltration, drug sensitivity scoring, and immunohistochemical staining were performed to initially understand the potential role of COX7A1 in the development and progression of GC. Finally, the differential expression of COX7A1 was verified in those GC patients receiving immunotherapy.

RESULTS

First, 47 protective genes and 408 risky genes were obtained, and the ssGSEA algorithm was applied for model construction, showing good prognostic discrimination ability. In addition, the patients with high model scores showed higher TMB and MSI levels, and lower tumor heterogeneity scores. Then, it is found that the COX7A1 expressions in GC tissues were significantly lower than those in their corresponding paracancerous tissues. Meanwhile, the patients with high COX7A1 expression showed higher probability of cancer invasion, worse clinical efficacy of immunotherapy, worse overall survival (OS) and worse disease-free survival (DFS).

CONCLUSIONS

The ssGSEA score we constructed can serve as a biomarker for GC patients and provide important guidance for individualized treatment. In addition, the COX7A1 gene can accurately distinguish the prognosis of GC patients and predict the clinical efficacy of immunotherapy for GC patients.

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.

AGAP2-AS1 promotes the assembly of m6A methyltransferases and activation of the IL6/STAT3 pathway by binding with WTAP in the carcinogenesis of gastric cancer

Owing to the lack of biomarkers for early diagnosis, gastric cancer (GC) is often associated with a poor prognosis. Thus, there is an urgent need to identify early molecular targets in GC. Dysregulated long noncoding RNAs (lncRNAs) have been evaluated by integrated bioinformatics analysis; and we investigate their specific role and potential mechanism via N6-methyladenosine (m6A) methylation modification in the carcinogenesis and progression of GC. In this study, we report upregulation of lncRNA AGAP2-AS1, activated by a gain of H3K4Me3, in GC tissues and cells. AGAP2-AS1 was linked to adverse prognosis in patients with GC. Functionally, AGAP2-AS1 knockdown inhibited cell proliferation and migration of GC cells. Mechanistically, AGAP2-AS1 bound WT1-associated protein (WTAP) to promote the formation of the WTAP/methyltransferase-like 3 (METTL3)/METTL14 m6A methyltransferase complex. AGAP2-AS1 stabilized signal transducer and activator of transcription 3 (STAT3) mRNA in an m6A-dependent manner and, thus, activated the interleukin 6 (IL6)/STAT3 pathway. Importantly, activation of the AGAP2-AS1/WTAP/STAT3 pathways promoted cell proliferation and migration in GC. Collectively, the present findings revealed a novel regulatory relationship between lncRNA and m6A modification. Furthermore, targeting the AGAP2-AS1/WTAP/STAT3 axis may be a promising strategy for the inhibition of inflammation-mediated carcinogenesis and progression in GC.

Identification of memory B-cell-associated miRNA signature to establish a prognostic model in gastric adenocarcinoma

BACKGROUND

Memory B cells and microRNAs (miRNAs) play important roles in the progression of gastric adenocarcinoma (GAC), also known as stomach adenocarcinoma (STAD). However, few studies have investigated the use of memory B-cell-associated miRNAs in predicting the prognosis of STAD.

METHODS

We identified the marker genes of memory B cells by single-cell RNA sequencing (scRNA-seq) and identified the miRNAs associated with memory B cells by constructing an mRNA‒miRNA coexpression network. Then, univariate Cox, random survival forest (RSF), and stepwise multiple Cox regression (StepCox) algorithms were used to identify memory B-cell-associated miRNAs that were significantly related to overall survival (OS). A prognostic risk model was constructed and validated using these miRNAs, and patients were divided into a low-risk group and a high-risk group. In addition, the differences in clinicopathological features, tumour microenvironment, immune blocking therapy, and sensitivity to anticancer drugs in the two groups were analysed.

RESULTS

Four memory B-cell-associated miRNAs (hsa-mir-145, hsa-mir-125b-2, hsa-mir-100, hsa-mir-221) with significant correlations to OS were identified and used to construct a prognostic model. Time-dependent receiver operating characteristic (ROC) curve analysis confirmed the feasibility of the model. Kaplan‒Meier (K‒M) survival curve analysis showed that the prognosis was poor in the high-risk group. Comprehensive analysis showed that patients in the high-risk group had higher immune scores, matrix scores, and immune cell infiltration and a poor immune response. In terms of drug screening, we predicted eight drugs with higher sensitivity in the high-risk group, of which CGP-60474 was associated with the greatest sensitivity.

CONCLUSIONS

In summary, we identified memory B-cell-associated miRNA prognostic features and constructed a novel risk model for STAD based on scRNA-seq data and bulk RNA-seq data. Among patients in the high-risk group, STAD showed the highest sensitivity to CGP-60474. This study provides prognostic insights into individualized and precise treatment for STAD patients.