Investigation of the Clinical Significance and Prognostic Value of the lncRNA ACVR2B-As1 in Liver Cancer

Machine learning-driven prognostic analysis of cuproptosis and disulfidptosis-related lncRNAs in clear cell renal cell carcinoma: a step towards precision oncology

Cuproptosis and disulfidptosis, recently discovered mechanisms of cell death, have demonstrated that differential expression of key genes and long non-coding RNAs (lncRNAs) profoundly influences tumor development and affects their drug sensitivity. Clear cell renal cell carcinoma (ccRCC), the most common subtype of kidney cancer, presently lacks research utilizing cuproptosis and disulfidptosis-related lncRNAs (CDRLRs) as prognostic markers. In this study, we analyzed RNA-seq data, clinical information, and mutation data from The Cancer Genome Atlas (TCGA) on ccRCC and cross-referenced it with known cuproptosis and disulfidptosis-related genes (CDRGs). Using the LASSO machine learning algorithm, we identified four CDRLRs-ACVR2B-AS1, AC095055.1, AL161782.1, and MANEA-DT-that are strongly associated with prognosis and used them to construct a prognostic risk model. To verify the model's reliability and validate these four CDRLRs as significant prognostic factors, we performed dataset grouping validation, followed by RT-qPCR and external database validation for differential expression and prognosis of CDRLRs in ccRCC. Gene function and pathway analysis were conducted using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) for high- and low-risk groups. Additionally, we have analyzed the tumor mutation burden (TMB) and the immune microenvironment (TME), employing the oncoPredict and Immunophenoscore (IPS) algorithms to assess the sensitivity of diverse risk categories to targeted therapeutics and immunosuppressants. Our predominant objective is to refine prognostic predictions for patients with ccRCC and inform treatment decisions by conducting an exhaustive study on cuproptosis and disulfidptosis.

The regulatory effect of lncRNA LINC00943 on the progression of hepatocellular carcinoma and its relationship with clinicopathological features

BACKGROUND

The risk factors for the pathogenesis of HCC are highly variable, and the prognosis of patients is very unsatisfactory. In this study, we investigated the regulatory effect of LINC00943 on HCC progression and its relationship with clinicopathological features.

METHODS

LINC00943 level in HCC tissues and cell specimens was verified by RT-qPCR. The pathologic significance of LINC00943 in the prognosis of HCC was analyzed by Kaplan-Meier and Cox regression analyses. The behavioral function of LINC00943 in HCC cells was evaluated via CCK-8 and Transwell assays. The specific targeting relationship between LINC00943 and miR-195-5p was investigated by luciferase activity assay.

RESULTS

LINC00943 was highly expressed in HCC tissues and cell specimens. Clinical data analysis showed that elevated LINC00943 indicated poor prognosis in patients with HCC and was related to TNM stage and lymph node metastasis. Cell experiments demonstrated that silencing LINC00943 sponge miR-195-5p suppressed the proliferation, migration and invasion of HCC cells. Mechanistically, miR-195-5p inhibitor remedied the suppressive effect of silencing LINC00943 on the biological functions of HCC cells.

CONCLUSION

LINC00943 may be an independent prognostic factor of HCC, which provides new thinking for the prognosis and treatment of HCC patients.

Establishment and verification of a prognostic risk score model based on immune genes for hepatocellular carcinoma in an Asian population

Background

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, with the highest incidence in East Asia, and hepatitis B virus (HBV) infection is the most common cause of HCC in Asian population. The immune system is closely related to the development of HCC and plays an important role in the treatment of this disease. In this study, we analyzed the data of HCC from The Cancer Genome Atlas (TCGA) database and constructed a risk-score prognostic model based on immune genes of an Asian HCC population, aiming to provide new perspectives for clinical treatment and management of HCC in Asian population.

Methods

Data concerning clinical attributes and transcriptomic profiles of individuals in the Asian population diagnosed with HCC were retrieved from the TCGA database. Concurrently, immune-related genes were sourced from the Immport database for incorporation into our analysis. A total of 265 immune-related genes displaying differential expression were identified through wilcoxTest analysis in R. Further refinement using univariate and multivariate Cox regression analysis led to the identification of 15 genes that exhibited strong associations with prognosis. MICB/PSMD14/TRAF3/SP1/NDRG1/HDAC1/HRAS/NRAS/SEMA5B/GMFB/ACVR2B/BRD8/MMP12/KITLG/DCK, and a prognostic risk score model was constructed based on the above genes.

Results

The findings demonstrated notable differences in survival rates between the low-risk and high-risk groups, as depicted by the Kaplan-Meier (K-M) survival curves (P<0.001). Furthermore, the model's predictive capability was evidenced by receiver operating characteristic (ROC) curves, with area under the curve (AUC) =0.901. Finally, the relationship of the model with each clinical trait and immune cells was assessed by correlation analysis.

Conclusions

The prognostic risk score model constructed by immune genes based on the Asian HCC population has certain predictive capacity.

Creation of a Prognostic Model Using Cuproptosis-Associated Long Noncoding RNAs in Hepatocellular Carcinoma

Cuproptosis is an unusual form of cell death caused by copper accumulation in mitochondria. Cuproptosis is associated with hepatocellular carcinoma (HCC). Long noncoding RNAs (LncRNAs) have been shown to be effective prognostic biomarkers, yet the link between lncRNAs and cuproptosis remains unclear. We aimed to build a prognostic model of lncRNA risk and explore potential biomarkers of cuproptosis in HCC. Pearson correlations were used to derive lncRNAs co-expressed in cuproptosis. The model was constructed using Cox, Lasso, and multivariate Cox regressions. Kaplan-Meier survival analysis, principal components analysis, receiver operating characteristic curve, and nomogram analyses were carried out for validation. Seven lncRNAs were identified as prognostic factors. A risk model was an independent prognostic predictor. Among these seven lncRNAs, prostate cancer associated transcript 6 (PCAT6) is highly expressed in different types of cancer, activating Wnt, PI3K/Akt/mTOR, and other pathways; therefore, we performed further functional validation of PCAT6 in HCC. Reverse transcription-polymerase chain reaction results showed that PCAT6 was aberrantly highly expressed in HCC cell lines (HepG2 and Hep3B) compared to LO2 (normal hepatocytes). When its expression was knocked down, cells proliferated and migrated less. PCAT6 might be a potential biomarker for predicting prognosis in HCC.

A novel pyroptosis gene expression-based risk score for survival in gastric cancer

BACKGROUND

Gastric cancer (GC) is a highly heterogeneous disease, which makes treatment and prognosis prediction difficult. Pyroptosis plays a vital role in the development of GC and influence the prognosis of GC. Long non-coding RNAs (lncRNAs), as regulators of gene expressions, are among putative biomarkers and therapeutic targets. However, the importance of pyroptosis-associated lncRNAs is still unclear in predicting prognosis in gastric cancer.

METHODS

In this study, the mRNA expression profiles and clinical data of GC patients were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. A pyroptosis-related lncRNA signature was constructed based on TCGA databases by using the Least Absolute Shrinkage and Selection Operator (LASSO) method Cox regression model. GC patients from the GSE62254 database cohort were used for validation. Univariate and multivariate Cox analyses were used to determine the independent predictors for OS. Gene set enrichment analyses were performed to explore the potential regulatory pathways. The immune cell infiltration level was analyzed via CIBERSORT.

RESULTS

A four-pyroptosis-related lncRNA (ACVR2B-AS1, PRSS30P, ATP2B1-AS1, RMRP) signature was constructed using LASSO Cox regression analysis. GC patients were stratified into high- and low-risk groups, and patients in the high-risk group showed significant worse prognosis in TNM stage, gender, and age. The risk score was an independent predictor for OS by multivariate Cox analysis. Functional analysis indicated that the immune cell infiltrate was different between high- and low-risk groups.

CONCLUSION

The pyroptosis-related lncRNA prognostic signature can be used for predicting prognosis in GC. Moreover, the novel signature might provide clinical therapeutic intervention for GC patients.

KLRK1 as a prognostic biomarker for lung adenocarcinoma cancer

Lung cancer is one of the most common malignancy worldwide and causes estimated 1.6 million deaths each year. Cancer immunosurveillance has been found to play an important role in lung cancer and may be related with its prognosis. KLRK1, encoding NKG2D, is a homodimeric lectin-like receptor. However, there has not been one research of KLRK1 as a biomarker in lung cancer. Data including patients` clinical characteristics and RNAseq information of KLRK1 from TCGA were downloaded. A total of 1019 patients with lung cancer were included in this study, among which 407 patients were female and 611 patients were male. Evaluations of mRNA expression, diagnostic value by ROC (receiver operating characteristic) curves and prognostic value by survival curve, Cox model and subgroup analysis were performed. The level of KLRK1 expression in lung adenocarcinoma cancer tissues and normal lung tissues was detected by qRT-PCR. The CCK-8 assay investigated the proliferation rate and the wound healing assay assessed the migratory ability in vitro. The expression of KLRK1 in tumor was lower than that in normal tissue. KLRK1 expression was associated with gender, histologic grade, stage, T classification and vital status. Patients with high KLRK1 expression presented an improved overall survival (P = 0.0036) and relapse free survival (P = 0.0031). KLRK1 was found to have significant prognostic value in lung adenocarcinoma (P = 0.015), stage I/II (P = 0.03), older patients (P = 0.0052), and male (P = 0.0047) by subgroup overall survival analysis, and in lung adenocarcinoma (P = 0.0094), stage I/II (P = 0.0076), older patients (P = 0.0072), and male (P = 0.0033) by subgroup relapse free survival analysis. Lung adenocarcinoma cancer patients with high KLRK1 expression presented an improved overall survival (P = 0.015) and relapse free survival (P = 0.0094). In vitro studies indicated that KLRK1 inhibited tumor cell proliferation and migration. KLRK1 was an independent prognostic factor and high KLRK1 expression indicated a better overall and relapse free survival. KLRK1 may be a prognostic biomarker for lung adenocarcinoma cancer.

The Association between Long Noncoding RNA over Expression and Poor Prognosis of Liver Cancer: A Meta-Analysis

BACKGROUND

Long noncoding RNA (lncRNA) is considered to be a mediator of carcinogenesis, which may be associated with liver cancer survival. However, the relationship remains inconclusive. Meta-analysis was conducted to analytically review the association between the lncRNA expression level and clinicopathological characteristics and prognostic value of hepatic carcinoma.

MATERIALS AND METHODS

Four databases including Embase, PubMed, Web of Science, and the Cochrane Library were searched to collect studies about the relation between lncRNA overexpression and prognosis of liver cancer, dating from the earliest records of these databases to March 2021. Two researchers independently screened the data and literature to perform a stringent evaluation of the quality of material involved in the study. Meta-analysis was performed by Stata 16.0 software on 42 case-control studies with 6293 samples.

RESULTS

The outcomes of meta-analysis are presented as follows: lncRNA overexpression patients had later TNM stage (OR = 0.36, 95% CI (0.31, 0.41), P < 0.001), lower histological grade (OR = 0.56, 95%CI (0.49, 0.65), P < 0.001), more vascular invasion (OR = 2.02, 95% CI (1.74, 2.35), P < 0.001), bigger tumor size (OR = 2.28, 95% CI (2.00, 2.60), P < 0.001), more severe liver cirrhosis (OR = 1.39, 95% CI(0.1.16, 1.66), P < 0.001), more likely to metastasize (OR = 1.80, 95%CI(1.49, 2.18), P < 0.001), and more tumor numbers (OR = 0.72, 95% CI (0.62, 0.84), P < 0.05). lncRNA over expression patients had shorter OS (HR = 2.32, 95 CI% (2.08, 2.59), P < 0.01, RFS (HR = 2.19, 95 CI% (1.72, 2.78), P < 0.01), and DFS (HR = 2.01, 95 CI% (1.57, 2.57), P < 0.01).

CONCLUSIONS

Overexposure of lncRNA is a poor prognostic feature for patients with hepatic carcinoma. The scope of our study was limited because of a lack of relevant research and the poor representativeness and varying quality of the studies involved in the current meta-analysis. Our conclusion still requires higher studies for further validation. This trial is clinically registered with CRD4201920620.

The Association between Long Noncoding RNA over Expression and Poor Prognosis of Liver Cancer: A Meta-Analysis

Background

Long noncoding RNA (lncRNA) is considered to be a mediator of carcinogenesis, which may be associated with liver cancer survival. However, the relationship remains inconclusive. Meta-analysis was conducted to analytically review the association between the lncRNA expression level and clinicopathological characteristics and prognostic value of hepatic carcinoma.

Materials and Methods

Four databases including Embase, PubMed, Web of Science, and the Cochrane Library were searched to collect studies about the relation between lncRNA overexpression and prognosis of liver cancer, dating from the earliest records of these databases to March 2021. Two researchers independently screened the data and literature to perform a stringent evaluation of the quality of material involved in the study. Meta-analysis was performed by Stata 16.0 software on 42 case-control studies with 6293 samples.

Results

The outcomes of meta-analysis are presented as follows: lncRNA overexpression patients had later TNM stage (OR = 0.36, 95% CI (0.31, 0.41), P < 0.001), lower histological grade (OR = 0.56, 95%CI (0.49, 0.65), P < 0.001), more vascular invasion (OR = 2.02, 95% CI (1.74, 2.35), P < 0.001), bigger tumor size (OR = 2.28, 95% CI (2.00, 2.60), P < 0.001), more severe liver cirrhosis (OR = 1.39, 95% CI(0.1.16, 1.66), P < 0.001), more likely to metastasize (OR = 1.80, 95%CI(1.49, 2.18), P < 0.001), and more tumor numbers (OR = 0.72, 95% CI (0.62, 0.84), P < 0.05). lncRNA over expression patients had shorter OS (HR = 2.32, 95 CI% (2.08, 2.59), P < 0.01, RFS (HR = 2.19, 95 CI% (1.72, 2.78), P < 0.01), and DFS (HR = 2.01, 95 CI% (1.57, 2.57), P < 0.01).

Conclusions

Overexposure of lncRNA is a poor prognostic feature for patients with hepatic carcinoma. The scope of our study was limited because of a lack of relevant research and the poor representativeness and varying quality of the studies involved in the current meta-analysis. Our conclusion still requires higher studies for further validation. This trial is clinically registered with CRD4201920620.

Identification and validation of tumor microenvironment-related lncRNA prognostic signature for uveal melanoma

AIM

To investigate the role of tumor microenvironment (TME)-related long non-coding RNA (lncRNA) in uveal melanoma (UM), probable prognostic signature and potential small molecule drugs using bioinformatics analysis.

METHODS

UM expression profile data were downloaded from the Cancer Genome Atlas (TCGA) and bioinformatics methods were used to find prognostic lncRNAs related to UM immune cell infiltration. The gene expression profile data of 80 TCGA specimens were analyzed using the single sample Gene Set Enrichment Analysis (ssGSEA) method, and the immune cell infiltration of a single specimen was evaluated. Finally, the specimens were divided into high and low infiltration groups. The differential expression between the two groups was analyzed using the R package 'edgeR'. Univariate, multivariate and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analyses were performed to explore the prognostic value of TME-related lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses were also performed. The Connectivity Map (CMap) data set was used to screen molecular drugs that may treat UM.

RESULTS

A total of 2393 differentially expressed genes were identified and met the criteria for the low and high immune cell infiltration groups. Univariate Cox analysis of lncRNA genes with differential expression identified 186 genes associated with prognosis. Eight prognostic markers of TME-included lncRNA genes were established as potentially independent prognostic elements. Among 269 differentially expressed lncRNAs, 69 were up-regulated and 200 were down-regulated. Univariate Cox regression analysis of the risk indicators and clinical characteristics of the 8 lncRNA gene constructs showed that age, TNM stage, tumor base diameter, and low and high risk indices had significant prognostic value. We screened the potential small-molecule drugs for UM, including W-13, AH-6809 and Imatinib.

CONCLUSION

The prognostic markers identified in this study are reliable biomarkers of UM. This study expands our current understanding of the role of TME-related lncRNAs in UM genesis, which may lay the foundations for future treatment of this disease.

HN1 as a diagnostic and prognostic biomarker for liver cancer

Background: The present study aimed to examine the diagnostic and prognostic value of HN1 in terms of overall survival (OS) and recurrence-free survival (RFS) in liver cancer and its potential regulatory signaling pathway.

Methods: We obtained clinical data and HN1 RNA-seq expression data of liver cancer patients from The Cancer Genome Atlas database, and analyzed the differences and clinical association of HN1 expression in different clinical features. We uesd receiver-operating characteristic curve to evaluate the diagnosis capability of HN1. We analyzed and evaluated the prognostic significance of HN1 by Kaplan–Meier curves and Cox analysis. Gene Set Enrichment Analysis (GSEA) was used to identify signaling pathways related to HN1 expression.

Results: HN1 mRNA was up-regulated in liver cancer, and was associated with age, histologic grade, stage, T classification, M classification, and vital status. HN1 mRNA had ideal specificity and sensitivity for the diagnosis (AUC = 0.855). Besides, the analysis of Kaplan–Meier curves and Cox model showed that HN1 mRNA was strongly associated with the overall survival and could be well-predicted liver cancer prognosis, as an independent prognostic variable. GSEA analysis identified three signaling pathways that were enriched in the presence of high HN1 expression.

Conclusion: HN1 serves as a biomarker of diagnosis and prognosis in liver cancer.

RNA sequencing: new technologies and applications in cancer research

Over the past few decades, RNA sequencing has significantly progressed, becoming a paramount approach for transcriptome profiling. The revolution from bulk RNA sequencing to single-molecular, single-cell and spatial transcriptome approaches has enabled increasingly accurate, individual cell resolution incorporated with spatial information. Cancer, a major malignant and heterogeneous lethal disease, remains an enormous challenge in medical research and clinical treatment. As a vital tool, RNA sequencing has been utilized in many aspects of cancer research and therapy, including biomarker discovery and characterization of cancer heterogeneity and evolution, drug resistance, cancer immune microenvironment and immunotherapy, cancer neoantigens and so on. In this review, the latest studies on RNA sequencing technology and their applications in cancer are summarized, and future challenges and opportunities for RNA sequencing technology in cancer applications are discussed.

LncRNA NCK1-AS1 promotes non-small cell lung cancer progression via regulating miR-512-5p/p21 axis

OBJECTIVE

This study aimed at probing into the effect of lncRNA NCK1-AS1 on proliferation, migration and invasion of non-small cell lung cancer (NSCLC) cells and its regulatory function on miR-512-5p/p21 molecular axis.

METHODS

Quantitative real-time polymerase chain reaction (qRT-PCR) was used to assess the expressions of NCK1-AS1 and miR-512-5p in NSCLC tissues and cell lines. The alterations of cell proliferation, migration, invasion and cell cycle were examined by cell counting kit-8 (CCK-8) assay, BrdU experiment, Transwell experiment and flow cytometry, respectively. The dual-luciferase reporter assay and RNA immunoprecipitation experiment were performed to validate the binding relationships between miR-512-5p and NCK1-AS1, and miR-512-5p the 3'UTR of p21 mRNA. Western blot was used to determine the effects of NCK1-AS1 and miR-512-5p on p21 protein expression.

RESULTS

NCK1-AS1 expression was up-regulated in NSCLC tissues and cells, and its high expression was correlated with shorter overall survival time and faster progression of patients. Overexpression of NCK1-AS1 promoted NSCLC cell proliferation, migration and invasion, and accelerated the cell cycle, whereas NCK1-AS1 siRNA inhibited these malignant biological behaviors, and arrested cell cycle. NCK1-AS1 could bind to miR-512-5p, p21 was verified as a target gene of miR-512-5p, and NCK1-AS1 could up-regulate the expression of p21 in NSCLC cells via repressing miR-512-5p expression.

CONCLUSION

NCK1-AS1 promotes NSCLC progression by regulating miR-512-5p/p21 molecular axis.

SLC25A11 serves as a novel prognostic biomarker in liver cancer

Liver cancer is a disease with high mortality; it is often diagnosed at intermediate and advanced stages and has a high recurrence rate. ROS restriction and adequate energy supply play significant roles in liver cancer. SLC25A11, a member of the malate-aspartate shuttle (MAS), regulates electroneutral exchange between 2-oxoglutarate and other dicarboxylates. It transports glutathione (GSH) from the cytoplasm into mitochondria to maintain GSH levels to limit ROS production. Moreover, SLC25A11 is essential for ATP generation in cancers as it regulates NADH transportation from the cytoplasm to mitochondria. The purpose of this research was to investigate the prognostic value of SLC25A11 in liver cancer. The Cancer Genome Atlas database was used to analyze the levels of SLC25A11 in liver cancer. Fisher's exact and chi-square tests were used to evaluate the relationship between SLC25A11 expression and clinical characteristics. Finally, we explored the value of SLC25A11 in prognosis by Cox analysis and Kaplan-Meier curves. Our results revealed that SLC25A11 was downregulated in liver cancer compared to normal controls. Low expression of SLC25A11 was associated with clinical stage, vital status, histologic grade, overall survival (OS) and relapse-free survival (RFS). Liver cancer patients with low SLC25A11 expression had shorter OS and RFS than patients with high SLC25A11 expression. Multivariate analysis showed that the expression of SLC25A11 was an independent predictor of RFS and OS. In conclusion, this study identified that SLC25A11 serves as a new prognostic marker for liver cancer.

The prognostic value of lncRNA SNHG4 and its potential mechanism in liver cancer

BACKGROUND AND OBJECT

Emerging evidence shows that non-coding RNA functions as new gene regulators and prognostic markers in several cancers, including liver cancer. Here, we focused on the small nucleolar RNA host gene 4 (SNHG4) in liver cancer prognosis based on The Cancer Genome Atlas (TCGA) data.

METHODS

The expression data and clinical information were downloaded from TCGA. Chi-square tests evaluated the correlation between SNHG4 expression and clinical parameters. Differences in survival between high and low expression groups (optic cutoff value determined by ROC) from Cox regression analysis were compared, and P-value was calculated by a log-rank test. Kaplan-Meier curves were compared with the log-rank test. GSEA and ceRNA network were conducted to explore the potential mechanism.

RESULTS

Data mining of lncRNA expression data for 371 patients with primary tumor revealed overexpression of SNHG4 in liver cancer. High SNHG4 expression was correlated with histological type (P = 0.01), histologic grade (P = 0.001), stage (P = 0.01), T classification (P = 0.004) and survival status (P = 0.013). Patients with high SNHG4 expression had poor overall survival and relapse-free survival compared with those with low SNHG4 expression. Multivariate analysis identified SNHG4 as an independent prognostic factor of poor survival in liver cancer. GSEA revealed related signaling pathway and ceRNA network explored the further mechanism.

CONCLUSION

High SNHG4 expression is an independent predictor of poor prognosis in liver cancer.