Helicobacter pylori Infection-Related Long Non-Coding RNA Signatures Predict the Prognostic Status for Gastric Cancer Patients

Development and validation of a risk prognostic model based on the H. pylori infection phenotype for stomach adenocarcinoma

Background

Stomach adenocarcinoma (STAD) is one of the most common malignancies. Infection of helicobacter pylori (H. pylori) is a major risk factor that leads to the development of STAD. This study constructed a risk model based on the H. pylori-related macrophages for predicting STAD prognosis.

Methods

The single-cell RNA sequencing (scRNA-seq) dataset and the clinic information and RNA-seq datasets of STAD patients were collected for establishing a prognostic model and for validation. The "Seurat" and "harmony" packages were used to process the scRNA-seq data. Key gene modules were sectioned using the "limma" package and the "WGCNA" package. Kaplan-Meier (KM) and Receiver Operating Characteristic Curve (ROC) analyses were performed with "survminer" package. The "GSVA" package was employed for single sample gene set enrichment analysis (ssGSEA). Cell migration and invasion were measured by carrying out wound healing and trans-well assays.

Results

A total of 17397 were screened and classified into 8 cell type clusters, among which the macrophage cluster was closely associated with the H. pylori infection. Macrophages were further categorized into four subtypes (including C1, C2, C3, and C4), and highly variable genes of macrophage subtype C4 could serve as an indicator of the prognosis of STAD. Subsequently, we developed a RiskScore model based on six H. pylori -associated genes (TNFRSF1B, CTLA4, ABCA1, IKBIP, AKAP5, and NPC2) and observed that the high-risk patients exhibited poor prognosis, higher suppressive immune infiltration, and were closely associated with cancer activation-related pathways. Furthermore, a nomogram combining the RiskScore was developed to accurately predict the survival of STAD patients. AB CA 1 in the RiskScore model significantly affected the migration and invasion of tumor cells.

Conclusion

The gene expression profile served as an indicator of the survival for patients with STAD and addressed the clinical significance of using H. pylori-associated genes to treat STAD. The current findings provided novel understandings for the clinical evaluation and management of STAD.

Implications of lncRNAs in Helicobacter pylori-associated gastrointestinal cancers: underlying mechanisms and future perspectives

Helicobacter pylori (H. pylori) is a harmful bacterium that is difficult to conveniently diagnose and effectively eradicate. Chronic H. pylori infection increases the risk of gastrointestinal diseases, even cancers. Despite the known findings, more underlying mechanisms are to be deeply explored to facilitate the development of novel prevention and treatment strategies of H. pylori infection. Long noncoding RNAs (lncRNAs) are RNAs with more than 200 nucleotides. They may be implicated in cell proliferation, inflammation and many other signaling pathways of gastrointestinal cancer progression. The dynamic expression of lncRNAs indicates their potential to be diagnostic or prognostic biomarkers. In this paper, we comprehensively summarize the processes of H. pylori infection and the treatment methods, review the known findings of lncRNA classification and functional mechanisms, elucidate the roles of lncRNAs in H. pylori-related gastrointestinal cancer, and discuss the clinical perspectives of lncRNAs.

Emerging roles of circular RNAs in tumorigenesis, progression, and treatment of gastric cancer

With an estimated one million new cases reported annually, gastric cancer (GC) ranks as the fifth most diagnosed malignancy worldwide. The early detection of GC remains a major challenge, and the prognosis worsens either when patients develop resistance to chemotherapy or radiotherapy or when the cancer metastasizes. The precise pathogenesis underlying GC is not well understood, which further complicates its treatment. Circular RNAs (circRNAs), a recently discovered class of noncoding RNAs that originate from parental genes through "back-splicing", have been shown to play a key role in various biological processes in both eukaryotes and prokaryotes. CircRNAs have been linked to cardiovascular diseases, diabetes, hypertension, Alzheimer's disease, and the occurrence and progression of tumors. Prior studies have established that circRNAs play a crucial role in GC, impacting tumorigenesis, diagnosis, progression, and therapy resistance. This review aims to summarize how circRNAs contribute to GC tumorigenesis and progression, examine their roles in the development of drug resistance, discuss their potential as biotechnological drugs, and summarize their response to therapeutic drugs and microorganism in GC.

Long non-coding RNAs and gastric cancer: An update of potential biomarkers and therapeutic applications

The frequent metastasis of gastric cancer (GC) complicates the cure and therefore the development of effective diagnostic and therapeutic approaches is urgently necessary. In recent years, lncRNA has emerged as a drug target in the treatment of GC, particularly in the areas of cancer immunity, cancer metabolism, and cancer metastasis. This has led to the demonstration of the importance of these RNAs as prognostic, diagnostic and therapeutic agents. In this review, we provide an overview of the biological activities of lncRNAs in GC development and update the latest pathological activities, prognostic and diagnostic strategies, and therapeutic options for GC-related lncRNAs.

Machine learning-based identification of a novel prognosis-related long noncoding RNA signature for gastric cancer

Gastric cancer (GC) is one of the most common malignancies with a poor prognosis. Immunotherapy has attracted much attention as a treatment for a wide range of cancers, including GC. However, not all patients respond to immunotherapy. New models are urgently needed to accurately predict the prognosis and the efficacy of immunotherapy in patients with GC. Long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and progression of cancers. Recent studies have identified a variety of prognosis-related lncRNA signatures in multiple cancers. However, these studies have some limitations. In the present study, we developed an integrative analysis to screen risk prediction models using various feature selection methods, such as univariate and multivariate Cox regression, least absolute shrinkage and selection operator (LASSO), stepwise selection techniques, subset selection, and a combination of the aforementioned methods. We constructed a 9-lncRNA signature for predicting the prognosis of GC patients in The Cancer Genome Atlas (TCGA) cohort using a machine learning algorithm. After obtaining a risk model from the training cohort, we further validated the model for predicting the prognosis in the test cohort, the entire dataset and two external GEO datasets. Then we explored the roles of the risk model in predicting immune cell infiltration, immunotherapeutic responses and genomic mutations. The results revealed that this risk model held promise for predicting the prognostic outcomes and immunotherapeutic responses of GC patients. Our findings provide ideas for integrating multiple screening methods for risk modeling through machine learning algorithms.

An Immune Signature for Risk Stratification and Therapeutic Prediction in Helicobacter Pylori-Infected Gastric Cancer

Simple Summary

Helicobacter pylori (HP) infection is the greatest risk factor for gastric cancer (GC), and over half of the world’s population is colonized with HP. Up to now, many gene signatures are designed for predicting the prognosis of GC patients, while there are no signatures designed specifically for HP+ GC patients. Considering the tight association between HP infection and tumor immune microenvironment, we constructed an immune-related signature named IRSHG in this study, aiming to provide new insight into the treatment for HP+ GC patients. IRSHG was the first prognostic signature for HP+ GC patients, demonstrating high reliability and feasibility in predicting the prognosis. In addition, IRSHG could help investigate potential therapies and guide anti-PD-1 immunotherapy for HP+ GC patients, providing new insight for the personalized treatment of GC.

Abstract

Helicobacter pylori (HP) infection is the greatest risk factor for gastric cancer (GC). Increasing evidence has clarified that tumor immune microenvironment (TIME) is closely related to the prognosis and therapeutic efficacy of HP-positive (HP+) GC patients. In this study, we aimed to construct a novel immune-related signature for predicting the prognosis and immunotherapy efficacy of HP+ GC patients. A total of 153 HP+ GC from three different cohorts were included in this study. An Immune-Related prognostic Signature for HP+ GC patients (IRSHG) was established using Univariate Cox regression, the LASSO algorithm, and Multivariate Cox regression. Univariate and Multivariate analyses proved IRSHG was an independent prognostic predictor for HP+ GC patients, and an IRSHG-integrated nomogram was established to quantitatively assessthe prognostic risk. The low-IRSHG group exhibited higher copy number load and distinct mutation profiles compared with the high-IRSHG group. In addition, the difference of hallmark pathways and immune cells infiltration between the two groups was investigated. Notably, tumor immune dysfunction and exclusion (TIDE) analysis indicated that the low-IRSHG group had a higher sensitivity to anti-PD-1 immunotherapy, which was validated by an external pabolizumab treatment cohort. Moreover, 98 chemotherapeutic drugs and corresponding potential biomarkers were identified for two groups, and several drugs with potential ability to reverse IRSHG score were identified using CMap analysis. Collectively, IRSHG may serve as a promising biomarker for survival outcome as well as immunotherapy efficacy. Furthermore, it can also help to prioritize potential therapeutics for HP+ GC patients, providing new insight for the personalized treatment of HP-infected GC.

LncRNA BACE1-AS promotes the progression of osteosarcoma through miR-762/SOX7 axis

BACKGROUND

Osteosarcoma (OS) is a rare malignant primary tumor of mesenchymal origin affecting bone that occurs in adolescents and children. LncRNAs are important regulators of tumorigenesis and development. This study aimed to explore the role and molecular basis of LncRNA BACE1-AS (BACE1 antisense RNA) in OS.

METHODS AND RESULTS

Through the analysis of differential expressed lncRNAs in OS tissues by GEO database, LncRNA BACE1-AS display a remarkably lower expression. This found can also be observed in both OS tissues and cell lines by qRT-PCR. Furthermore, using Cell counting kit-8 (CCK-8), transwell, wound healing and westernblot assays, overexpression LncRNA BACE1-AS remarkably reduce cell proliferation, migration and invasion abilities in OS. In addition, LncRNA BACE1-AS is validated as a sponge of miR-762 through the prediction of lncRNASNP. Further, luciferase reporter and RIP assays are conducted to confirm the binding sites between LncRNA BACE1-AS and miR-762. SRY-box transcription factor 7 (SOX7) target to miR-762 and regulated by LncRNA BACE1-AS. Moreover, inhibition of miR-762 attenuate the role of sh-LncRNA BACE1-AS in OS cells, at meanwhile reduce the expression of SOX7.

CONCLUSION

In this study, LncRNA BACE1-AS regulates proliferation, migration and invasion of osteosarcoma cells by miR-762/SOX7 axis, implying that LncRNA BACE1-AS is a potential target for osteosarcoma therapy.

Identification and Comprehensive Prognostic Analysis of a Novel Chemokine-Related lncRNA Signature and Immune Landscape in Gastric Cancer

Gastric cancer (GC) is a malignant tumor with poor survival outcomes. Immunotherapy can improve the prognosis of many cancers, including GC. However, in clinical practice, not all cancer patients are sensitive to immunotherapy. Therefore, it is essential to identify effective biomarkers for predicting the prognosis and immunotherapy sensitivity of GC. In recent years, chemokines have been widely reported to regulate the tumor microenvironment, especially the immune landscape. However, whether chemokine-related lncRNAs are associated with the prognosis and immune landscape of GC remains unclear. In this study, we first constructed a novel chemokine-related lncRNA risk model to predict the prognosis and immune landscape of GC patients. By using various algorithms, we identified 10 chemokine-related lncRNAs to construct the risk model. Then, we determined the prognostic efficiency and accuracy of the risk model. The effectiveness and accuracy of the risk model were further validated in the testing set and the entire set. In addition, our risk model exerted a crucial role in predicting the infiltration of immune cells, immune checkpoint genes expression, immunotherapy scores and tumor mutation burden of GC patients. In conclusion, our risk model has preferable prognostic performance and may provide crucial clues to formulate immunotherapy strategies for GC.

The role of non-coding RNA in the diagnosis and treatment of Helicobacter pylori-related gastric cancer, with a focus on inflammation and immune response

Helicobacter pylori (H. pylori) is one of the globally recognized causative factors of gastric cancer (GC). Currently, no definite therapy and drugs for H. pylori-related GC have been widely acknowledged although H. pylori infection could be eradicated in early stage. Inflammation and immune response are spontaneous essential stages during H. pylori infection. H pylori may mediate immune escape by affecting inflammation and immune response, leading to gastric carcinogenesis. As an important component of transcriptome, non-coding RNAs (ncRNAs) have been proven to play crucial roles in the genesis and development of H. pylori-induced GC. This review briefly described the effects of ncRNAs on H. pylori-related GC from the perspective of inflammation and immune response, as well as their association with inflammatory reaction and immune microenvironment. We aim to explore the potential of ncRNAs as markers for the early diagnosis, prognosis, and treatment of H. pylori-related GC. The ncRNAs involved in H. pylori-related GC may all hold promise as novel therapeutic targets for immunotherapy.

Angiogenesis-related lncRNAs predict the prognosis signature of stomach adenocarcinoma

Background

Stomach adenocarcinoma (STAD), which accounts for approximately 95% of gastric cancer types, is a malignancy cancer with high morbidity and mortality. Tumor angiogenesis plays important roles in the progression and pathogenesis of STAD, in which long noncoding RNAs (lncRNAs) have been verified to be crucial for angiogenesis. Our study sought to construct a prognostic signature of angiogenesis-related lncRNAs (ARLncs) to accurately predict the survival time of STAD.

Methods

The RNA-sequencing dataset and corresponding clinical data of STAD were acquired from The Cancer Genome Atlas (TCGA). ARLnc sets were obtained from the Ensemble genome database and Molecular Signatures Database (MSigDB, Angiogenesis M14493, INTegrin pathway M160). A ARLnc-related prognostic signature was then constructed via univariate Cox and multivariate Cox regression analysis in the training cohort. Survival analysis and Cox regression were performed to assess the performance of the prognostic signature between low- and high-risk groups, which was validated in the validation cohort. Furthermore, a nomogram that combined the clinical pathological characteristics and risk score conducted to predict the overall survival (OS) of STAD. In addition, ARLnc-mRNA coexpression pairs were constructed with Pearson’s correlation analysis and visualized to infer the functional annotation of the ARLncs by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The expression of four ARLncs in STAD and their correlation with the angiogenesis markers, CD34 and CD105, were also validated by RT–qPCR in a clinical cohort.

Results

A prognostic prediction signature including four ARLncs (PVT1, LINC01315, AC245041.1, and AC037198.1) was identified and constructed. The OS of patients in the high-risk group was significantly lower than that of patients in the low-risk group (p < 0.001). The values of the time-dependent area under the curve (AUC) for the ARLnc signature for 1-, 3-, and 5- year OS were 0.683, 0.739, and 0.618 in the training cohort and 0.671, 0.646, and 0.680 in the validation cohort, respectively. Univariate and multivariate Cox regression analyses indicated that the ARLnc signature was an independent prognostic factor for STAD patients (p < 0.001). Furthermore, the nomogram and calibration curve showed accurate prediction of the survival time based on the risk score. In addition, 262 mRNAs were screened for coexpression with four ARLncs, and GO analysis showed that mRNAs were mainly involved in biological processes, including angiogenesis, cell adhesion, wound healing, and extracellular matrix organization. Furthermore, correlation analysis showed that there was a positive correlation between risk score and the expression of the angiogenesis markers, CD34 and CD105, in TCGA datasets and our clinical sample cohort.

Conclusion

Our study constructed a prognostic signature consisting of four ARLnc genes, which was closely related to the survival of STAD patients, showing high efficacy of the prognostic signature. Thus, the present study provided a novel biomarker and promising therapeutic strategy for patients with STAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08987-y.