Identification of N6-Methylandenosine-Related lncRNAs for Subtype Identification and Risk Stratification in Gastric Adenocarcinoma

WTAP-Involved the m6A Modification of lncRNA FAM83H-AS1 Accelerates the Development of Gastric Cancer

The long noncoding RNA FAM83H-antisense RNA 1 (FAM83H-AS1) is involved in gastric cancer (GC) development. This study determined whether FAM83H-AS1 was regulated by N6-methyladenosine (m6A) modifications in GC. Real-time quantitative polymerase chain reaction was performed to determine the expression levels of FAM83H-AS1 and Wilms' tumor 1 associated protein (WTAP). The protein content of WTAP was evaluated using western blotting. To assess the m6A alterations in FAM83H-AS1, methylated RNA immunoprecipitation was performed to identify interactions between WTAP and FAM83H-AS1. Functionally, the proliferation, migration, and invasion of GC cells were measured using a Cell Counting Kit-8 and transwell assays, respectively. High expression levels of FAM83H-AS1 and WTAP were detected in GC samples and there was a positive correlation between them. In addition, WTAP mediates FAM83H-AS1 expression in an m6A-dependent manner. Further investigations indicated that WTAP silencing reversed the cancer-promoting role of FAM83H-AS1 overexpression in GC cell migration, proliferation, and invasion. Our results suggest that WTAP-mediated FAM83H-AS1 promotes GC development via m6A modification. Our findings provide new biomarkers for GC diagnosis and targeted therapy.

The regulatory mechanism of m6A modification in gastric cancer

To the best of our knowledge, N6-Methyladenosine (m6A) exerts a significant role in the occurrence and development of various tumors. Gastric cancer (GC), originating from the mucosal epithelium in the digestive tract, is the fifth most common cancer and the third most common cause of cancer death around the world. Therefore, it is urgent to explore the specific mechanism of tumorigenesis of GC. As we all know, m6A modification as the most common RNA modification, is involved in the modification of mRNA and ncRNA at the post-transcriptional level, which played a regulatory role in various biological processes. As identified by numerous studies, the m6A modification are able to influence the proliferation, apoptosis, migration, and invasion of GC. What's more, m6A modification are associated with EMT, drug resistance, and aerobic glycolysis in GC. m6A related-ncRNAs may be a valuable biomarker used by the prediction of GC diagnosis in the future. This review summarizes the role of m6A modification in the mechanism of gastric cancer, with the aim of identifying biological progress.

Identification and validation of m5c-related lncRNA risk model for ovarian cancer

Ovarian cancer (OC) is one of the common malignant tumors that seriously threaten women's health, and there is a lack of clinical prognostic predictors, while m5c and lncRNA have been shown to be predictive of multiple cancers, including OC. Therefore, our goal was to construct a risk model for OC based on m5c-related lncRNA.340 m5c-related lncRNA were identified and a novel risk model of OC ground on nine m5C-related lncRNA was constructed using LASSO-COX regression analysis. Kaplan-Meier analysis showed there was a significant difference in prognosis between risk groups. We established a nomogram which was a good predictor of overall survival. In addition, GSEA was enriched in multiple pathways and immune function analysis suggested that immune infiltration varies depending on the risk group. In vitro experiments show that AC005562.1, a key lncRNA of the risk model, is highly expressed in OC cells and promotes OC cell proliferation. Finally, we further explored the potential biological markers of m5c-related lncRNA in OC with WGCNA analysis and established a ceRNA network. In conclusion,we have developed a reliable m5c-related prediction model and performed systematic validation and exploration of various aspects. These results can be used for the assessment of OC prognosis and the discovery of novel biomarkers.

Characteristic of molecular subtypes based on PANoptosis-related genes and experimental verification of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a type of liver cancer that originates from liver cells. It is one of the most common types of liver cancer and a leading cause of cancer-related death worldwide. Early detection and treatment can improve the HCC prognosis. Therefore, it is necessary to further improve HCC markers and risk stratification. PANoptosome is a cytoplasmic polymer protein complex that regulates a proinflammatory programmed cell death pathway called "PANoptosis". The role of PANoptosis in HCC remains unclear. In this study, the molecular changes of PANoptosis related genes (PAN-RGs) in HCC were systematically evaluated. We characterized the heterogeneity of HCC by using consensus clustering to identify two distinct subtypes. The two subtypes showed different survival rate, biological function, chemotherapy drug sensitivity and immune microenvironment. After identification of PAN-RG differential expression genes (DEGs), a prognostic model was established by Cox regression analysis using minimum absolute contraction and selection operator (LASSO), and its prognostic value was verified by Cox regression analysis, Kaplan-Meier curve and receiver operating characteristic (ROC) curve. Our own specimens were also used to further validate the prognostic significance and possible clinical value of the selected targets. Subsequently, we conducted a preliminary discussion on the reasons for the influence of the model on the prognosis through TME analysis, drug resistance analysis, TMB analysis and other studies. This study provides a new idea for individualized and precise treatment of HCC.

N6-methylandenosine-related immune genes correlate with prognosis and immune landscapes in gastric cancer

OBJECTIVES

This study aimed to probe into the significance of N6-methyladenosine (m6A)-related immune genes (m6AIGs) in predicting prognoses and immune landscapes of patients with gastric cancer (GC).

METHODS

The clinical data and transcriptomic matrix of GC patients were acquired from The Cancer Genome Atlas database. The clinically meaningful m6AIGs were acquired by univariate Cox regression analysis. GC patients were stratified into different clusters via consensus clustering analysis and different risk subgroups via m6AIGs prognostic signature. The clinicopathological features and tumor microenvironment (TME) in the different clusters and different risk subgroups were explored. The predictive performance was evaluated using the KM method, ROC curves, and univariate and multivariate regression analyses. Moreover, we fabricated a nomogram based on risk scores and clinical risk characteristics. Biological functional analysis was performed based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. The connectivity map was used to screen out potential small molecule drugs for GC patients.

RESULTS

A total of 14 prognostic m6AIGs and two clusters based on 14 prognostic m6AIGs were identified. A prognostic signature based on 4 m6AIGs and a nomogram based on independent prognostic factors was constructed and validated. Different clusters and different risk subgroups were significantly correlated with TME scores, the distribution of immune cells, and the expression of immune checkpoint genes. Some malignant and immune biological processes and pathways were correlated with the patients with poor prognosis. Ten small molecular drugs with potential therapeutic effect were screened out.

CONCLUSIONS

This study revealed the prognostic role and significant values of m6AIGs in GC, which enhanced the understanding of m6AIGs and paved the way for developing predictive biomarkers and therapeutic targets for GC.

Screening and validation of platelet activation-related lncRNAs as potential biomarkers for prognosis and immunotherapy in gastric cancer patients

Background: Platelets (PLT) have a significant effect in promoting cancer progression and hematogenous metastasis. However, the effect of platelet activation-related lncRNAs (PLT-related lncRNAs) in gastric cancer (GC) is still poorly understood. In this study, we screened and validated PLT-related lncRNAs as potential biomarkers for prognosis and immunotherapy in GC patients.

Methods: We obtained relevant datasets from the Cancer Genome Atlas (TCGA) and Gene Ontology (GO) Resource Database. Pearson correlation analysis was used to identify PLT-related lncRNAs. By using the univariate, least absolute shrinkage and selection operator (LASSO) Cox regression analyses, we constructed the PLT-related lncRNAs model. Kaplan-Meier survival analysis, univariate, multivariate Cox regression analysis, and nomogram were used to verify the model. The Gene Set Enrichment Analysis (GSEA), drug screening, tumor immune microenvironment analysis, epithelial-mesenchymal transition (EMT), and DNA methylation regulators correlation analysis were performed in the high- and low-risk groups. Patients were regrouped based on the risk model, and candidate compounds and immunotherapeutic responses aimed at GC subgroups were also identified. The expression of seven PLT-related lncRNAs was validated in clinical medical samples using quantitative reverse transcription-polymerase chain reaction (qRT-PCR).

Results: In this study, a risk prediction model was established using seven PLT-related lncRNAs -(AL355574.1, LINC01697, AC002401.4, AC129507.1, AL513123.1, LINC01094, and AL356417.2), whose expression were validated in GC patients. Kaplan-Meier survival analysis, the receiver operating characteristic (ROC) curve analysis, univariate, multivariate Cox regression analysis verified the accuracy of the model. We screened multiple targeted drugs for the high-risk patients. Patients in the high-risk group had a poorer prognosis since low infiltration of immune killer cells, activation of immunosuppressive pathways, and poor response to immunotherapy. In addition, we revealed a close relationship between risk scores and EMT and DNA methylation regulators. The nomogram based on risk score suggested a good ability to predict prognosis and high clinical benefits.

Conclusion: Our findings provide new insights into how PLT-related lncRNAs biomarkers affect prognosis and immunotherapy. Also, these lncRNAs may become potential biomarkers and therapeutic targets for GC patients.

Molecular characteristics of novel immune subtypes of HCC based on lncRNAs related to immune disorders

As one of the most malignant cancers and despite various treatment breakthroughs, the prognosis of hepatocellular carcinoma (HCC) remains unsatisfactory. The immune status of the tumor microenvironment (TME) relates closely to HCC progression; however, the mechanism of immune cell infiltration in the TME remains unclear. In this study, we performed a new combination algorithm on lncRNA expression profile data from the TCGA-LIHC cohort to identify lncRNAs related to immune disorders. We identified 20 immune disorder-related lncRNAs and clustered HCC samples based on these lncRNAs. We identified four clusters with differences in immune cell infiltration and immune checkpoint gene expression. We further analyzed differences between groups 1 and 3 and found that the poor prognosis of group 3 may be due to specific and non-specific immunosuppression of the TME, upregulation of immune checkpoint pathways, and activation of tumor proliferation and migration pathways in group 3. We also developed a prognostic model and verified that it has good stability, effectiveness, and prognostic power. This study provides a basis for further exploration of the immune cell infiltration mechanism in HCC, differential HCC prognosis, and improvement of the efficacy of ICIs for the treatment of HCC.

N6-methyladenosine (m6A) reader IGF2BP2 promotes gastric cancer progression via targeting SIRT1

N⁶-methyladenosine (m⁶A) modification acts as the most prevalent internal modification in eukaryotic mRNA. Emerging evidence shows the critical biological roles of m⁶A key enzymes in human cancers. However, the roles of m⁶A binding protein IGF2BP2 in gastric cancer (GC) progression are still unclear. In this study, we confirmed that IGF2BP2 was highly expressed in GC cell lines and tumor tissues. Knocking down of IGF2BP2 suppressed cell proliferation and migration, and repressed xenograft tumor growth in vivo, while IGF2BP2 overexpression promoted the proliferation and migration. Mechanistically, we identified that IGF2BP2 regulated GC the proliferation/migration through recognizing the m⁶A modification sites of SIRT1 mRNA. In general, our findings demonstrated a novel regulatory mechanism that IGF2BP2/SIRT1 axis modulated GC progression in an m⁶A-dependent manner, suggesting that m⁶A may be a therapeutic target for GC.

Identification of Five N6-Methylandenosine-Related ncRNA Signatures to Predict the Overall Survival of Patients with Gastric Cancer

Noncoding ribonucleic acids (ncRNAs) are involved in various functions in the formation and progression of different tumors. However, the association between N6-methyladenosine-related ncRNAs (m6A-related ncRNAs) and gastric cancer (GC) prognosis remains elusive. As such, this research was aimed at identifying m6A-related ncRNAs (lncRNAs and miRNAs) in GC and developing prognostic models of relevant m6A-related ncRNAs and identifying potential biomarkers regulated by m6A. In this study, the m6A2Target database, Starbase database, and The Cancer Genome Atlas (TCGA) were used to screen m6A-related ncRNAs. And then, we performed integrated bioinformatics analyses to determine prognosis-associated ncRNAs and to develop the m6A-related ncRNA prognostic signature (m6A-NPS) for GC patients. Finally, five m6A-related ncRNAs (including lnc-ARHGAP12, lnc-HYPM-1, lnc-WDR7-11, LINC02266, and lnc-PRIM2-7) were identified to establish m6A-NPS. The predictive power of m6A-NPS was better in the receiver operating characteristic (ROC) curve analysis of the training set (area under the curve (AUC), >0.6). The m6A-NPS could be utilized to classify patients into high- and low-risk cohorts, and the Kaplan-Meier analysis indicated that participants in the high-risk cohort had a poorer prognosis. The entire TCGA dataset substantiated the predictive value of m6A-NPS. Significant differences in TCGA molecular GC subtypes were observed between high- and low-risk cohorts. The ROC curve analysis indicated that m6A-NPS had better predictive power than other clinical characteristics of GC prognosis. Uni- and multivariate regression analyses indicated m6A-NPS as an independent prognostic factor. Furthermore, the m6A status between the low-risk cohort and high-risk cohort was significantly different. Differential genes between them were enriched in multiple tumor-associated signaling pathways. In summary, five m6A-related ncRNA signatures that could forecast the overall survival of patients with GC were identified.

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.