Identification and Validation of a Prognostic Prediction Model of m6A Regulator-Related LncRNAs in Hepatocellular Carcinoma

Crosstalk between m6A modification and non-coding RNAs in HCC

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide, with high morbidity and occurrence. Although various therapeutic approaches have been rapidly developed in recent years, the underlying molecular mechanisms in the pathogenesis of HCC remain enigmatic. The N6-methyladenosine (m6A) RNA modification is believed to regulate RNA metabolism and further gene expression. This process is intricately regulated by multiple regulators, such as methylases and demethylases. Non-coding RNAs (ncRNAs) are involved in the regulation of the epigenetic modification, mRNA transcription and other biological processes, exhibiting crucial roles in tumor occurrence and development. The m6A-ncRNA interaction has been implicated in the malignant phenotypes of HCC and plays an important role in drug resistance. This review summarizes the effect of m6A-ncRNA crosstalk on HCC progression and their clinical implications as prognostic markers and therapeutic targets in this disease.

Cancer stem cell-derived CHI3L1 activates the MAF/CTLA4 signaling pathway to promote immune escape in triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) development may be associated with tumor immune escape. This study explores whether the CHI3L1/MAF/CTLA4/S100A4 axis affects immune escape in TNBC through interplay with triple-negative breast cancer stem cells (TN-BCSCs).

OBJECTIVE

The aim of this study is to utilize single-cell transcriptome sequencing (scRNA-seq) to uncover the molecular mechanisms by which the CHI3L1/MAF/CTLA4 signaling pathway may mediate immune evasion in triple-negative breast cancer through the interaction between tumor stem cells (CSCs) and immune cells.

METHODS

Cell subsets in TNBC tissues were obtained through scRNA-seq, followed by screening differentially expressed genes in TN-BCSCs and B.C.s (CD44+ and CD24-) and predicting the transcription factor regulated by CHI3L1. Effect of CHI3L1 on the stemness phenotype of TNBC cells investigated. Effects of BCSCs-231-derived CHI3L1 on CTLA4 expression in T cells were explored after co-culture of BCSCs-231 cells obtained from microsphere culture of TN-BCSCs with T cells. BCSCs-231-treated T cells were co-cultured with CD8+ T cells to explore the resultant effect on T cell cytotoxicity. An orthotopic B.C. transplanted tumor model in mice with humanized immune systems was constructed, in which the Role of CHI3L1/MAF/CTLA4 in the immune escape of TNBC was explored.

RESULTS

Eight cell subsets were found in the TNBC tissues, and the existence of TN-BCSCs was observed in the epithelial cell subset. CHI3L1 was related to the stemness phenotype of TNBC cells. TN-BCSC-derived CHI3L1 increased CTLA4 expression in T cells through MAF, inhibiting CD8+ T cell cytotoxicity and inducing immunosuppression. Furthermore, the CTLA4+ T cells might secrete S100A4 to promote the stemness phenotype of TNBC cells.

CONCLUSIONS

TN-BCSC-derived CHI3L1 upregulates CTLA4 expression in T cells through MAF, suppressing the function of CD8+ T cells, which promotes the immune escape of TNBC.

Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma

BACKGROUND

Near 70% of hepatocellular carcinoma (HCC) recurrence is early recurrence within 2-year post surgery. Long non-coding RNAs (lncRNAs) are intensively involved in HCC progression and serve as biomarkers for HCC prognosis. The aim of this study is to construct a lncRNA-based signature for predicting HCC early recurrence.

METHODS

Data of RNA expression and associated clinical information were accessed from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Recurrence associated differentially expressed lncRNAs (DELncs) were determined by three DEG methods and two survival analyses methods. DELncs involved in the signature were selected by three machine learning methods and multivariate Cox analysis. Additionally, the signature was validated in a cohort of HCC patients from an external source. In order to gain insight into the biological functions of this signature, gene sets enrichment analyses, immune infiltration analyses, as well as immune and drug therapy prediction analyses were conducted.

RESULTS

A 4-lncRNA signature consisting of AC108463.1, AF131217.1, CMB9-22P13.1, TMCC1-AS1 was constructed. Patients in the high-risk group showed significantly higher early recurrence rate compared to those in the low-risk group. Combination of the signature, AFP and TNM further improved the early HCC recurrence predictive performance. Several molecular pathways and gene sets associated with HCC pathogenesis are enriched in the high-risk group. Antitumor immune cells, such as activated B cell, type 1 T helper cell, natural killer cell and effective memory CD8 T cell are enriched in patients with low-risk HCCs. HCC patients in the low- and high-risk group had differential sensitivities to various antitumor drugs. Finally, predictive performance of this signature was validated in an external cohort of patients with HCC.

CONCLUSION

Combined with TNM and AFP, the 4-lncRNA signature presents excellent predictability of HCC early recurrence.

A Novel Prognostic Chemokine-Related lncRNAs Signature Associated with Immune Landscape in Colon Adenocarcinoma

Chemokines have been reported to be involved in tumorigenesis and progression and can also modulate the tumor microenvironment. However, it is still unclear whether chemokine-related long noncoding RNAs (lncRNAs) can affect the prognosis of colon adenocarcinoma (COAD). We summarized chemokine-related genes and downloaded RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) database. A total of 52 prognostic chemokine-related lncRNAs were screened by univariate Cox regression analysis; patients were grouped according to cluster analysis results. Lasso regression analysis was applied to determine chemokine-related lncRNAs to construct a risk model for further research. This study first investigated the differences between the prognosis and immune status of two chemokine-related lncRNAs clusters by consensus clustering. Then, using various algorithms, we obtained ten chemokine-related lncRNAs to construct a new prognostic chemokine-related lncRNAs risk model. The risk model's predictive efficiency, validity, and accuracy were further validated and determined in the test and training cohorts. Furthermore, this risk model played a vital role in predicting immune cell infiltration, immune checkpoint gene expression, tumor mutational burden (TMB), immunotherapy score, and drug sensitivity in COAD patients. These findings elucidated the critical role of novel prognostic chemokine-related lncRNAs in prognosis, immune landscape, and drug therapy, thereby providing valuable insights for prognosis assessment and personalized treatment strategies for COAD patients.

Construction and validation of a necroptosis-related lncRNAs prognosis signature of hepatocellular carcinoma

Background: Immunotherapy has achieved remarkable success in treating advanced liver cancer. Current evidence shows that most of the available immune checkpoint inhibitor (ICB) treatments are suboptimal, and specific markers are needed for patients regarded as good candidates for immunotherapy. Necroptosis, a type of programmed cell death, plays an important role in hepatocellular carcinoma (HCC) progression and outcome. However, studies on the necroptosis-related lncRNA in HCC are scarce. In this view, the present study investigates the link among necroptosis-related lncRNA, prognosis, immune microenvironment, and immunotherapy response.

Methods: Gene transcriptome and clinical data were retrieved from The Cancer Genome Atlas database. Pearson correlation analysis of necroptosis-related genes was performed to identify necroptosis-related lncRNAs. The Wilcoxon method was used to detect differentially expressed genes, and prognostic relevant lncRNAs were obtained by univariate Cox regression analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were utilized to perform functional enrichment analysis. Lasso–Cox stepwise regression analysis was employed to calculate risk score, which was involved in analyzing immune cells infiltration, immune checkpoints expression, and predicting immunotherapeutic efficacy. Quantitative RT-PCR (qRT-PCR) was performed to detect the expression pattern of lncRNA in cell lines.

Results: The 10 lncRNAs generated in this study were used to create a prognostic risk model for HCC and group patients into groups based on risk. High-risk patients with HCC have a significantly lower OS rate than low-risk patients. Multivariate Cox regression analysis showed that risk score is an independent risk factor for HCC with high accuracy. Patients in the high-risk group exhibited a weaker immune surveillance and higher expression level of immune checkpoint molecules. In terms of drug resistance, patients in the low-risk group were more sensitive to sorafenib. The OS-related nomogram was constructed to verify the accuracy of our model. Finally, quantitative RT-PCR experiments were used to verify the expression patterns of candidate genes.

Conclusion: The lncRNA signature established herein, encompassing 10 necroptosis-related lncRNAs, is valuable for survival prediction and holds promise as prognostic markers for HCC.

Comprehensive analysis of nine m7G-related lncRNAs as prognosis factors in tumor immune microenvironment of hepatocellular carcinoma and experimental validation

Background: Hepatocellular carcinoma (HCC) remains the most prevalent gastrointestinal malignancy worldwide, with robust drug resistance to therapy. N7-methylguanosine (m7G) mRNA modification has been significantly related to massive human diseases. Considering the effect of m7G-modified long non-coding RNAs (lncRNAs) in HCC progression is unknown, the study aims at investigating a prognostic signature to improve clinical outcomes for patients with HCC.

Methods: Two independent databases (TCGA and ICGC) were used to analyze RNAseq data of HCC patients. First, co-expression analysis was applied to obtain the m7G-related lncRNAs. Moreover, consensus clustering analysis was employed to divide HCC patients into clusters. Then, using least absolute shrinkage and selection operator-Cox regression analysis, the m7G-related lncRNA prognostic signature (m7G-LPS) was first tested in the training set and then confirmed in both the testing and ICGC sets. The expression levels of the nine lncRNAs were further confirmed via real-time PCR in cell lines, principal component analysis, and receiver operating characteristic curve. The m7G-LPS could divide HCC patients into two different risk groups with the optimal risk score. Then, Kaplan–Meier curves, tumor mutation burden (TMB), therapeutic effects of chemotherapy agents, and expressions of immune checkpoints were performed to further enhance the availability of immunotherapeutic treatments for HCC patients.

Results: A total of 1465 lncRNAs associated with the m7G genes were finally selected from the TCGA database, and through the univariate Cox regression, the expression levels of 22 m7G-related lncRNAs were concerning HCC patients’ overall survival (OS). Then, the whole patients were grouped into two subgroups, and the OS in Cluster 1 was longer than that of patients in Cluster 2. Furthermore, nine prognostic m7G-related lncRNAs were identified to conduct the m7G-LPS, which were further verified. A prognostic nomogram combined age, gender, HCC grade, stage, and m7G-LPS showed strong reliability and accuracy in predicting OS in HCC patients. Finally, immune checkpoint expression, TMB, and several chemotherapy agents were remarkably associated with risk scores. More importantly, the OS of the TMB-high patients was the worst among the four groups.

Conclusion: The prognostic model we established was validated by abundant algorithms, which provided a new perspective on HCC tumorigenesis and thus improved individualized treatments for patients.

Research Progress on Long Noncoding RNAs and N6-Methyladenosine in Hepatocellular Carcinoma

N6-methyladenosine (m6A) is an epigenetic modification that widely exists in long noncoding RNAs (lncRNAs) and is involved in the regulation of oncogenes or tumor suppressor genes that form complex enzymes to affect the occurrence of tumors. The abnormal modification of m6A methylation can alter the overall m6A level and thus contribute to the malignant biological behaviors of hepatocellular carcinoma (HCC). LncRNAs related to m6A methylation are involved in lipogenesis, the proliferation, migration and invasion of HCC cells, the stemness of tumor cells and sorafenib resistance. In this review, we systematically elaborated the occurrence mechanism of lncRNA and m6A methylation modification in HCC and the effect of m6A methylation modification of lncRNA on the occurrence of HCC, suggesting that the combination of m6A methylation modification and lncRNA will be more meaningful as molecular markers or prognostic markers. It is helpful to provide further ideas for exploring the pathogenesis of HCC and identifying new targets for HCC treatment and diagnosis and achieve precise individual treatment of liver cancer.

Prognostic Signature and Tumor Immune Landscape of N7-Methylguanosine-Related lncRNAs in Hepatocellular Carcinoma

Despite great advances in the treatment of liver hepatocellular carcinoma (LIHC), such as immunotherapy, the prognosis remains extremely poor, and there is an urgent need to develop novel diagnostic and prognostic markers. Recently, RNA methylation-related long non-coding RNAs (lncRNAs) have been demonstrated to be novel potential biomarkers for tumor diagnosis and prognosis as well as immunotherapy response, such as N6-methyladenine (m6A) and 5-methylcytosine (m5C). N7-Methylguanosine (m7G) is a widespread RNA modification in eukaryotes, but the relationship between m7G-related lncRNAs and prognosis of LIHC patients as well as tumor immunotherapy response is still unknown. In this study, based on the LIHC patients’ clinical and transcriptomic data from TCGA database, a total of 992 m7G-related lncRNAs that co-expressed with 22 m7G regulatory genes were identified using Pearson correlation analysis. Univariate regression analysis was used to screen prognostic m7G-related lncRNAs, and the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were applied to construct a 9-m7G-related-lncRNA risk model. The m7G-related lncRNA risk model was validated to exhibit good prognostic performance through Kaplan–Meier analysis and ROC analysis. Together with the clinicopathological features, the m7G-related lncRNA risk score was found to be an independent prognostic factor for LIHC. Furthermore, the high-risk group of LIHC patients was unveiled to have a higher tumor mutation burden (TMB), and their tumor microenvironment was more prone to the immunosuppressive state and exhibited a lower response rate to immunotherapy. In addition, 47 anti-cancer drugs were identified to exhibit a difference in drug sensitivity between the high-risk and low-risk groups. Taken together, the m7G-related lncRNA risk model might display potential value in predicting prognosis, immunotherapy response, and drug sensitivity in LIHC patients.

N7-Methylguanosine Genes Related Prognostic Biomarker in Hepatocellular Carcinoma

Background: About 90% of liver cancer-related deaths are caused by hepatocellular carcinoma (HCC). N7-methylguanosine (m7G) modification is associated with the biological process and regulation of various diseases. To the best of our knowledge, its role in the pathogenesis and prognosis of HCC has not been thoroughly investigated.

Aim: To identify N7-methylguanosine (m7G) related prognostic biomarkers in HCC. Furthermore, we also studied the association of m7G–related prognostic gene signature with immune infiltration in HCC.

Methods: The TCGA datasets were used as a training and GEO dataset “GSE76427” for validation of the results. Statistical analyses were performed using the R statistical software version 4.1.2.

Results: Functional enrichment analysis identified some pathogenesis related to HCC. We identified 3 m7G-related genes (CDK1, ANO1, and PDGFRA) as prognostic biomarkers for HCC. A risk score was calculated from these 3 prognostic m7G-related genes which showed the high-risk group had a significantly poorer prognosis than the low-risk group in both training and validation datasets. The 3- and 5-years overall survival was predicted better with the risk score than the ideal model in the entire cohort in the predictive nomogram. Furthermore, immune checkpoint genes like CTLA4, HAVCR2, LAG3, and TIGT were expressed significantly higher in the high-risk group and the chemotherapy sensitivity analysis showed that the high-risk groups were responsive to sorafenib treatment.

Conclusion: These 3 m7G genes related signature model can be used as prognostic biomarkers in HCC and a guide for immunotherapy and chemotherapy response. Future clinical study on this biomarker model is required to verify its clinical implications.