Identification of a Novel Eight-lncRNA Prognostic Signature for HBV-HCC and Analysis of Their Functions Based on Coexpression and ceRNA Networks

Identifying potential drug targets for varicose veins through integration of GWAS and eQTL summary data

Background

Varicose veins (VV) are a common chronic venous disease that is influenced by multiple factors. It affects the quality of life of patients and imposes a huge economic burden on the healthcare system. This study aimed to use integrated analysis methods, including Mendelian randomization analysis, to identify potential pathogenic genes and drug targets for VV treatment.

Methods

This study conducted Summary-data-based Mendelian Randomization (SMR) analysis and colocalization analysis on data collected from genome-wide association studies and cis-expression quantitative trait loci databases. Only genes with PP.H4 > 0.7 in colocalization were chosen from the significant SMR results. After the above analysis, we screened 12 genes and performed Mendelian Randomization (MR) analysis on them. After sensitivity analysis, we identified four genes with potential causal relationships with VV. Finally, we used transcriptome-wide association studies and The Drug-Gene Interaction Database data to identify and screen the remaining genes and identified four drug targets for the treatment of VV.

Results

We identified four genes significantly associated with VV, namely, KRTAP5-AS1 [Odds ratio (OR) = 1.08, 95% Confidence interval (CI): 1.05-1.11, p = 1.42e-10] and PLEKHA5 (OR = 1.13, 95% CI: 1.06-1.20, p = 6.90e-5), CBWD1 (OR = 1.05, 95% CI: 1.01-1.11, p = 1.42e-2) and CRIM1 (OR = 0.87, 95% CI: 0.81-0.95, p = 3.67e-3). Increased expression of three genes, namely, KRTAP5-AS1, PLEKHA5, and CBWD1, was associated with increased risk of the disease, and increased expression of CRIM1 was associated with decreased risk of the disease. These four genes could be targeted for VV therapy.

Conclusion

We identified four potential causal proteins for varicose veins with MR. A comprehensive analysis indicated that KRTAP5-AS1, PLEKHA5, CBWD1, and CRIM1 might be potential drug targets for varicose veins.

Comprehensive analysis reveals TSPEAR as a prognostic biomarker in colorectal cancer

Background: Colorectal cancer (CRC) is one of the most common malignant tumors and has high morbidity and mortality rates. Previous studies have shown that TSPEAR mutations are involved in the development and progression of gastric cancer and liver cancer. However, the role of TSPEAR in CRC is still unclear. Methods: In The Cancer Genome Atlas (TCGA) database, 590 CRC patients with complete survival information were analyzed. We assessed TSPEAR expression in a pan-cancer dataset from the TCGA database. Cox regression analysis was performed to evaluate factors associated with prognosis. Enrichment analysis via the R package "clusterProfiler" was used to explore the potential function of TSPEAR. The single-sample GSEA (ssGSEA) method from the R package "GSVA" and the TIMER database were used to investigate the association between the immune infiltration level and TSPEAR expression in CRC. The R package "maftools" was used to explore the association between tumour mutation burden (TMB) and TSPEAR expression in CRC. CCK-8 assays and cell invasion assays were used to detect the effect of TSPEAR and TGIF2 on the biological behavior of CRC cells. Results: Pan-cancer analysis revealed that TSPEAR was upregulated in CRC tissues compared to normal tissues and that high TSPEAR expression was associated with poorer overall survival (OS) (p=0.0053). The expression of TSPEAR increased with increasing TNM stage, T stage, N stage, and M stage. The nomogram constructed with TSPEAR, age, and TNM stage showed better predictive value than TSPEAR, age, or TNM stage alone. Immune cell infiltration analysis revealed that high expression of TSPEAR was associated with lower immune cell infiltration. Tumor mutation burden (TMB) analysis indicated that high expression of TSPEAR was associated with lower TMB (p=0.005), and high TMB was associated with shorter OS (p=0.02). CCK-8 assays and cell invasion assays indicated that in vitro knockdown of TSPEAR inhibited the proliferation, migration, and invasion of CRC cells. In addition, TSPEAR expression may be regulated by the upstream transcription factor TGIF2. Conclusion: TSPEAR expression was higher in CRC tissues than in normal tissues. Its upregulation was significantly associated with a poor prognosis. Additionally, TSPEAR plays a significant role in tumor immunity and the biological behavior of CRC cells. Thus, TSPEAR may become a promising prognostic biomarker and therapeutic target for CRC patients.

Identification of the potential regulatory interactions in rheumatoid arthritis through a comprehensive analysis of lncRNA-related ceRNA networks

OBJECTIVE

This study aimed at constructing a network of competing endogenous RNA (ceRNA) in the synovial tissues of rheumatoid arthritis (RA). It seeks to discern potential biomarkers and explore the long non-coding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) axes that are intricately linked to the pathophysiological mechanisms underpinning RA, and providing a scientific basis for the pathogenesis and treatment of RA.

METHODS

Microarray data pertaining to RA synovial tissue, GSE103578, GSE128813, and GSE83147, were acquired from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ). Conducted to discern both differentially expressed lncRNAs (DELncRNAs) and differentially expressed genes (DEGs). A ceRNA network was obtained through key lncRNAs, key miRNAs, and key genes. Further investigations involved co-expression analyses to uncover the lncRNA-miRNA-mRNA axes contributing to the pathogenesis of RA. To delineate the immune-relevant facets of this axis, we conducted an assessment of key genes, emphasizing those with the most substantial immunological correlations, employing the GeneCards database. Finally, gene set enrichment analysis (GSEA) was executed on the identified key lncRNAs to elucidate their functional implications in RA.

RESULTS

The 2 key lncRNAs, 7 key miRNAs and 6 key genes related to the pathogenesis of RA were obtained, as well as 2 key lncRNA-miRNA-mRNA axes (KRTAP5-AS1-hsa-miR-30b-5p-PNN, XIST-hsa-miR-511-3p/hsa-miR-1277-5p-F2RL1). GSEA of two key lncRNAs obtained biological processes and signaling pathways related to RA synovial lesions.

CONCLUSION

The findings of this investigation hold promise in furnishing a foundational framework and guiding future research endeavors aimed at comprehending the etiology and therapeutic interventions for RA.

Prognostic 7-SLC-Gene Signature Identified via Weighted Gene Co-Expression Network Analysis for Patients with Hepatocellular Carcinoma

Background

Solute carrier (SLC) proteins play an important role in tumor metabolism. But SLC-associated genes' prognostic significance in hepatocellular carcinoma (HCC) remained elusive. We identified SLC-related factors and developed an SLC-related classifier to predict and improve HCC prognosis and treatment.

Methods

From the TCGA database, corresponding clinical data and mRNA expression profiles of 371 HCC patients were acquired, and those of 231 tumor samples were derived from the ICGC database. Genes associated with clinical features were filtered using weighted gene correlation network analysis (WGCNA). Next, univariate LASSO Cox regression studies developed SLC risk profiles, with the ICGC cohort data being used in validation.

Result

Univariate Cox regression analysis revealed that 31 SLC genes (P < 0.05) were related to HCC prognosis. 7 (SLC22A25, SLC2A2, SLC41A3, SLC44A1, SLC48A1, SLC4A2, and SLC9A3R1) of these genes were applied in developing a SLC gene prognosis model. Samples were classified into the low-andhigh-risk groups by the prognostic signature, with those in the high-risk group showing a significantly worse prognosis (P < 0.001 in the TCGA cohort and P=0.0068 in the ICGC cohort). ROC analysis validated the signature's prediction power. In addition, functional analyses showed enrichment of immune-related pathways and different immune status between the two risk groups.

Conclusion

The 7-SLC-gene prognostic signature established in this study helped predict the prognosis, and was also correlated with the tumor immune status and infiltration of different immune cells in the tumor microenvironment. The current findings may provide important clinical indications for proposing a novel combination therapy consists of targeted anti-SLC therapy and immunotherapy for HCC patients.

A novel senescence-related lncRNA signature that predicts prognosis and the tumor microenvironment in patients with lung adenocarcinoma

Background: Cellular senescence has recently been considered a new cancer hallmark. However, the factors regulating cellular senescence have not been well characterized. The aim of this study is to identify long non-coding RNAs (lncRNAs) associated with senescence and prognosis in patients with lung adenocarcinoma (LUAD). Methods: Using RNA sequence data from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) and senescence genes from the CellAge database, a subset of senescence-related lncRNAs was first identified. Then, using univariate and multivariate Cox regression analyses, a senescence lncRNA signature (LUADSenLncSig) associated with LUAD prognosis was developed. Based on the median LUADSenLncSig risk score, LUAD patients were divided into high-risk and low-risk groups. Kaplan-Meier analysis was used to compare the overall survival (OS) in the high- and low-risk score subgroups. Differences in Gene Set Enrichment Analysis (GSEA), immune infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) module score, chemotherapy, and targeted therapy selection were also compared between the high-risk and low-risk groups. Results: A prognostic risk model was obtained consisting of the following nine senescence-related lncRNAs: LINC01116, AC005838.2, SH3PXD2A-AS1, VIMS-AS1, SH3BP5-AS1, AC092279.1, AC026355.1, AC027020.2, and LINC00996. The LUADSenLncSig high-risk group was associated with poor OS (hazard ratio = 1.17, 95% confidence interval = 1.102-1.242; p < 0.001). The accuracy of the model was further supported based on receiver operating characteristic (ROC), principal component analysis (PCA), and internal validation cohorts. In addition, a nomogram was developed consisting of LUADSenLncSig for LUAD prognosis, which is consistent with the actual probability of OS. Furthermore, immune infiltration analysis showed the low-risk group had a stronger anti-tumor immune response in the tumor microenvironment. Notably, the levels of immune checkpoint genes such as CTLA-4, PDCD-1, and CD274, and the TIDE scores were significantly higher in the low-risk subgroups than in high-risk subgroups (p < 0.001). This finding indicates the LUADSenLncSig can potentially predict immunotherapy efficacy. Conclusion: In this study, a lncRNA signature, LUADSenLncSig, that has dual functions of senescence phenotype identification and prognostic prediction as well as the potential to predict the LUAD response to immunotherapy was developed.

THUMPD3-AS1 facilitates cell growth and aggressiveness by the miR-218-5p/SKAP1 axis in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a malignant cancer with a high mortality. Accumulating studies have revealed that mRNAs involved in ceRNA (competing endogenous RNA) network are implicated in the tumorigenesis and development of CRC. Here, we aimed to elucidate the ceRNA network involving Src kinase associated phosphoprotein 1 (SKAP1) in the biological characteristics of CRC.

METHODS

Expression levels of genes in colon adenocarcinoma (COAD) samples and prognosis of COAD patients were predicted using publicly available online tool. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), clony formation and Transwell assays were conducted to test the biological functions of SKAP1 and THUMPD3 antisense RNA 1 (THUMPD3-AS1) in CRC cells. Western blot was used to measure the protein levels of SKAP1. Gene expression in CRC cells was detected by reverse transcription quantitative polymerase chain reaction (RT-qPCR). The interaction between miR-218-5p and THUMPD3-AS1 (or SKAP1) was verified by RNA pulldown and luciferase reporter assays.

RESULTS

SKAP1 was upregulated in COAD tissues and CRC cells and it reflected a poor prognosis in patients with COAD. SKAP1 knockdown inhibited CRC (HT-29 and HCT-116) cell proliferation, migration and invasion. Mechanistically, THUMPD3-AS1 acted as a ceRNA to sponge miR-218-5p and subsequently upregulated SKAP1 expression in CRC cells. SKAP1 overexpression reversed the suppressive effect of THUMPD3-AS1 knockdown on proliferation, migration and invision of CRC cells.

CONCLUSIONS

THUMPD3-AS1 promotes CRC cell growth and aggressiveness by regulating the miR-218-5p/SKAP1 axis.

Regulatory Roles of Noncoding RNAs in the Progression of Gastrointestinal Cancers and Health Disparities

Annually, more than a million individuals are diagnosed with gastrointestinal (GI) cancers worldwide. With the advancements in radio- and chemotherapy and surgery, the survival rates for GI cancer patients have improved in recent years. However, the prognosis for advanced-stage GI cancers remains poor. Site-specific GI cancers share a few common risk factors; however, they are largely distinct in their etiologies and descriptive epidemiologic profiles. A large number of mutations or copy number changes associated with carcinogenesis are commonly found in noncoding DNA regions, which transcribe several noncoding RNAs (ncRNAs) that are implicated to regulate cancer initiation, metastasis, and drug resistance. In this review, we summarize the regulatory functions of ncRNAs in GI cancer development, progression, chemoresistance, and health disparities. We also highlight the potential roles of ncRNAs as therapeutic targets and biomarkers, mainly focusing on their ethnicity-/race-specific prognostic value, and discuss the prospects of genome-wide association studies (GWAS) to investigate the contribution of ncRNAs in GI tumorigenesis.

Long noncoding RNAs in hepatitis B virus replication and oncogenesis

Several diverse long noncoding RNAs (lncRNAs) have been identified to be involved in hepatitis B virus (HBV) replication and oncogenesis, especially those dysregulated in HBV-related hepatocellular carcinoma (HCC). Most of these dysregulated lncRNAs are modulated by the HBV X protein. The regulatory mechanisms of some lncRNAs in HBV replication and oncogenesis have been characterized. Genetic polymorphisms of several lncRNAs affecting HBV replication or oncogenesis have also been studied. The prognosis of HCC remains poor. It is important to identify novel tumor markers for early diagnosis and find more therapeutic targets for effective treatments of HCC. Some dysregulated lncRNAs in HBV-related HCC may become biomarkers for early diagnosis and/or the therapeutic targets of HCC. This mini-review summarizes these findings briefly, focusing on recent developments.

The development and validation of a novel senescence-related long-chain non-coding RNA (lncRNA) signature that predicts prognosis and the tumor microenvironment of patients with hepatocellular carcinoma

Background

The epigenetic regulators of cellular senescence, especially long non-coding RNAs (lncRNAs), remain unclear. The expression levels of lncRNA were previously known to be prognostic indicators for tumors. We hypothesized that lncRNAs regulating cellular senescence could also predict prognosis in patients with hepatocellular carcinoma (HCC) and developed a novel lncRNA predictive signature.

Methods

Using RNA sequencing data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database, a co-expression network of senescence-related messenger RNAs (mRNAs) and lncRNAs was constructed. Using univariate Cox regression analysis and a stepwise multiple Cox regression analysis, we constructed a prognostic HCC senescence-related lncRNA signature (HCCSenLncSig). Kaplan-Meier analysis was used to compare the overall survival (OS) of high- and low-risk groups stratified by the HCCSenLncSig. Furthermore, the HCCSenLncSig risk score and other clinical characteristics were included to develop an HCC prognostic nomogram. The accuracy of the model was evaluated by the time dependent receiver operating characteristic (ROC) and calibration curves, respectively.

Results

We obtained a prognostic risk model consisting of 8 senescence-related lncRNAs: AL117336.3, AC103760.1, FOXD2-AS1, AC009283.1, AC026401.3, AC021491.4, AC124067.4, and RHPN1-AS1. The HCCSenLncSig high-risk group was associated with poor OS [hazard ratio (HR) =1.125, 95% confidence interval (CI): 1.082-1.169; P<0.001]. The accuracy of the model was further supported by ROC curves (the area under the curve is 0.783, sensitivity of 0.600, and specificity of 0.896 at the cut-off value of 1.447). The HCCSenLncSig was found to be an independent prognostic factor from other clinical factors in both univariate and multivariate Cox regression analyses. The prognostic nomogram shows HCCSenLncSig has a good prognostic effect for survival risk stratification. Finally, we found that a higher number of immunosuppressed Treg cells infiltrate in high-risk patients (P<0.001 compared to low-risk patients), possibly explaining why these patients have a poor prognosis. On the other hand, the expression of immunotherapy markers, such as CD276, PDCD1, and CTLA4, was also up-regulated in the high-risk patients, indicating potential immunotherapy response in these patients.

Conclusions

The development of HCCSenLncSig allows us to better predict HCC patients' survival outcomes and disease risk, as well as contribute to the development of novel HCC anti-cancer therapeutic strategies.

Diversity of Dysregulated Long Non-Coding RNAs in HBV-Related Hepatocellular Carcinoma

Infection with the hepatitis B virus (HBV) continues to pose a major threat to public health as approximately 292 million people worldwide are currently living with the chronic form of the disease, for which treatment is non-curative. Chronic HBV infections often progress to hepatocellular carcinoma (HCC) which is one of the world’s leading causes of cancer-related deaths. Although the process of hepatocarcinogenesis is multifaceted and has yet to be fully elucidated, several studies have implicated numerous long non-coding RNAs (lncRNAs) as contributors to the development of HCC. These host-derived lncRNAs, which are often dysregulated as a consequence of viral infection, have been shown to function as signals, decoys, guides, or scaffolds, to modulate gene expression at epigenetic, transcriptional, post-transcriptional and even post-translational levels. These lncRNAs mainly function to promote HBV replication and oncogene expression or downregulate tumor suppressors. Very few lncRNAs are known to suppress tumorigenesis and these are often downregulated in HCC. In this review, we describe the mechanisms by which lncRNA dysregulation in HBV-related HCC promotes tumorigenesis and cancer progression.

High Expression of Long Non-Coding RNA TMCO1-AS1 is Associated With Poor Prognosis of Hepatocellular Carcinoma

Background: The molecular pathways along with the clinical significance of long non-coding RNAs (lncRNAs) in hepatocellular carcinoma (HCC) remain uncertain. Our study sought to identify and characterize lncRNAs associated with HCC.

Methods: LncRNA TMCO1-AS1 was identified by differential expression analysis, receiver operating characteristic (ROC) analysis, and univariate analysis using RNA sequencing and clinical information of HCC from the public database. Then clinical correlations and survival analysis were conducted to further appraise the prognostic significance of lncRNA TMCO1-AS1 in HCC. Hepatoma and adjoining normal tissues from 66 patients who received surgical operation at our center were used to verify the results of the bioinformatics analysis. A survival prognostic model was established combining TMCO1-AS1 expression and other clinical characteristics.

Results: Bioinformatics analysis showed the aberrant high expression of TMCO1-AS1 in HCC tissue. TMCO1-AS1 expression was positively correlated with alpha-fetoprotein (AFP) level, vascular invasion, tumor stage, as well as tumor differentiation. Moreover, survival analysis found a significant inverse association between the expression of TMCO1-AS1 and the survival of patients with HCC. Cox analysis indicated that TMCO1-AS1 was an independent factor for HCC prognosis. Analysis of the HCC tissues from patients at our center provided results similar to those of the bioinformatics analysis. Risk models for overall survival (OS) and recurrence-free survival (RFS) incorporating TMCO1-AS1 exhibited better sensitivity and specificity than using clinical characteristics alone.

Conclusion: High TMCO1-AS1 expression is significantly correlated with the unfavorable poor prognosis of HCC, indicating its potential of being a novel prognostic marker for HCC.

Down-regulation of ZNF252P-AS1 alleviates ovarian cancer progression by binding miR-324-3p to downregulate LY6K

Background

Ovarian cancer is a common gynecological malignant disease in women. Our work aimed to study the specific functions of ZNF252P antisense RNA 1 (ZNF252P-AS1) in ovarian cancer.

Methods

ZNF252P-AS1, miR-324-3p, and lymphocyte antigen 6 family member K (LY6K) expression were analyzed by bioinformatics tools in ovarian cancer tissues and was quantified by qRT-PCR in ovarian cancer cells. The effect of ZNF252P-AS1 knockdown, miR-324-3p suppression, and LY6K over-expression on apoptosis, cell viability, invasion, migration, and epithelial to mesenchymal transition (EMT) was determined in vitro by using colony formation and EdU assays, flow cytometry, transwell assay, and Western blot. The interactions between ZNF252P-AS1 and miR-324-3p and between miR-324-3p and LY6K were validated by luciferase assays. The effects of restraining ZNF252P-AS1 in vivo were studied using BALB/c male nude mice.

Results

ZNF252P-AS1 and LY6K levels were up-regulated, while miR-324-3p was declined in ovarian cancer tissues and cells. ZNF252P-AS1 knockdown reduced ovarian cancer cell proliferation, invasion, migration, and EMT, whereas promoted its apoptosis. Besides, ZNF252P-AS1 interacted with miR-324-3p and reversely regulated its level, and miR-324-3p was directly bound to LY6K and negatively regulated its expression. Moreover, ZNF252P-AS1 knockdown reversed the effect of miR-324-3p on cancer cell apoptosis, growth, migration, invasion, and EMT. Similar results were discovered in the rescue experiments between miR-324-3p and LY6K. Additionally, mouse models in vivo experiments further validated that ZNF252P-AS1 knockdown distinctly inhibited tumor growth.

Conclusion

ZNF252P-AS1 mediated miR-324-3p/LY6K signaling to facilitate progression of ovarian cancer.

ETS Proto-Oncogene 1-activated muskelin 1 antisense RNA drives the malignant progression of hepatocellular carcinoma by targeting miR-22-3p to upregulate ETS Proto-Oncogene 1

Long noncoding RNA muskelin 1 antisense RNA (MKLN1-AS) acted as an oncogenic regulator in hepatocellular carcinoma (HCC). This study was performed to investigate the functional mechanism of MKLN1-AS. MKLN1-AS, microRNA-22-3p (miR-22-3p) and ETS Proto-Oncogene 1 (ETS1) levels were examined using reverse transcription-quantitative polymerase-chain reaction. Protein expression was detected by Western blot. The target relation was analyzed by dual-luciferase reporter assay, RNA immunoprecipitation assay and RNA pull-down assay. Cell proliferation ability was determined through cell counting kit-8 assay, colony formation assay and ethylenediurea assay. Angiogenesis was examined by tube formation assay. Cell migration and invasion were assessed via transwell assay. In vivo research was conducted by xenograft tumor model in nude mice. MKLN1-AS was upregulated in HCC tissues and cells. ETS1 promoted the ETS1 expression by binding to the 582–596 sites. Silence of MKLN1-AS suppressed cell growth, angiogenesis, migration, and invasion. MKLN1-AS interacted with miR-22-3p in HCC cells. The function of MKLN1-AS downregulation was relieved by miR-22-3p inhibition in HCC cells. ETS1 was validated as a target of miR-22-3p, and MKLN1-AS upregulated the ETS1 expression by sponging miR-22-3p. Overexpression of miR-22-3p retarded HCC progression by downregulating the level of ETS1. Tumor growth in vivo was also enhanced by MKLN1-AS through the regulation of miR-22-3p/ETS1 axis. These data demonstrated that ETS1-mediated MKLN1-AS contributed to the malignant phenotypes of HCC cells via depending on the miR-22-3p/ETS1 regulatory axis.

Overexpression of lncRNA A2M-AS1 inhibits cell growth and aggressiveness via regulating the miR-587/bone morphogenetic protein 3 axis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a malignant tumor that occurs in the lungs. Numerous reports have substantiated the participation of long non-coding RNAs (lncRNAs) in the tumorigenesis of LUAD. Previously, lncRNA alpha-2-macroglobulin antisense RNA 1 (A2M-AS1) was confirmed to be an important regulator in the biological processes of LUAD and dysregulation of A2M-AS1 was associated with non-small cell lung cancer (NSCLC) progression. However, the precise mechanism of A2M-AS1 in LUAD has not been elucidated. Therefore, our study was designed to investigate the detailed molecular mechanism of A2M-AS1 in LUAD. Herein, the expression of lncRNA A2M-AS1, microRNA (miRNA) miR-587, and bone morphogenetic protein 3 (BMP3) in LUAD cell lines and tissues were detected by real-time quantitative polymerase chain reaction (RT-qPCR) and western blotting. The viability, proliferation, migration and invasion of LUAD cells were tested by cell counting kit-8 (CCK-8), colony formation and Transwell assays. In vivo tumor growth was investigated by xenograft animal experiment. Interactions among A2M-AS1, miR-587 and BMP3 were measured by RNA pulldown and luciferase reporter assays. In this study, A2M-AS1 was downregulated in LUAD tissues and cells and related to poor prognosis in LUAD patients. A2M-AS1 overexpression suppressed LUAD cell proliferation, migration and invasion in vitro and inhibited tumor growth in vivo. Mechanistically, A2M-AS1 directly bound with miR-587 to promote BMP3 expression in LUAD cells. Low expression of BMP3 was found in LUAD tissues and cells and was closely correlated with poor prognosis in LUAD patients. BMP3 deficiency reserved the inhibitory influence of A2M-AS1 overexpression on LUAD cell behaviors. Overall, A2M-AS1 inhibits cell growth and aggressiveness via regulating the miR-587/BMP3 axis in LUAD.

Construction of a novel immune-related lncRNA signature and its potential to predict the immune status of patients with hepatocellular carcinoma

Background

The accuracy of existing biomarkers for predicting the prognosis of hepatocellular carcinoma (HCC) is not satisfactory. It is necessary to explore biomarkers that can accurately predict the prognosis of HCC.

Methods

In this study, original transcriptome data were downloaded from The Cancer Genome Atlas (TCGA) database. Immune-related long noncoding ribonucleic acids (irlncRNAs) were identified by coexpression analysis, and differentially expressed irlncRNA (DEirlncRNA) pairs were distinguished by univariate analysis. In addition, the least absolute shrinkage and selection operator (LASSO) penalized regression was modified. Next, the cutoff point was determined based on the area under the curve (AUC) and Akaike information criterion (AIC) values of the 5-year receiver operating characteristic (ROC) curve to establish an optimal model for identifying high-risk and low-risk groups of HCC patients. The model was then reassessed in terms of clinicopathological features, survival rate, tumor-infiltrating immune cells, immunosuppressive markers, and chemotherapy efficacy.

Results

A total of 1009 pairs of DEirlncRNAs were recognized in this study, 30 of these pairs were included in the Cox regression model for subsequent analysis. After regrouping according to the cutoff point, we could more effectively identify factors such as aggressive clinicopathological features, poor survival outcomes, specific immune cell infiltration status of tumors, high expression level of immunosuppressive biomarkers, and low sensitivity to chemotherapy drugs in HCC patients.

Conclusions

The nonspecific expression level signature involved with irlncRNAs shows promising clinical value in predicting the prognosis of HCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09059-x.

Genome-Wide Screening Identifies Prognostic Long Noncoding RNAs in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignancy with a poor prognosis. Therefore, there is an urgent call for the investigation of novel biomarkers in HCC. In the present study, we identified 6 upregulated lncRNAs in HCC, including LINC01134, RHPN1-AS1, NRAV, CMB9-22P13.1, MKLN1-AS, and MAPKAPK5-AS1. Higher expression of these lncRNAs was correlated to a more advanced cancer stage and a poorer prognosis in HCC patients. Enrichment analysis revealed that these lncRNAs played a crucial role in HCC progression, possibly through a series of cancer-related biological processes, such as cell cycle, DNA replication, histone acetyltransferase complex, fatty acid oxidation, and lipid modification. Moreover, competing endogenous RNA (ceRNA) network analysis revealed that these lncRNAs could bind to certain miRNAs to promote HCC progression. Loss-of-function assays indicated that silencing of RHPN1-AS1 significantly suppressed HCC proliferation and migration. Though further validations are still needed, these identified lncRNAs could serve as valuable potential biomarkers for HCC prognosis.

Integrated Analysis of lncRNA-Mediated ceRNA Network Reveals a Prognostic Signature for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common types of malignancy and is associated with high mortality. Prior research suggests that long non-coding RNAs (lncRNAs) play a crucial role in the development of HCC. Therefore, it is necessary to identify lncRNA-associated therapeutic biomarkers to improve the accuracy of HCC prognosis. Transcriptomic data of HCC obtained from The Cancer Genome Atlas (TCGA) database were used in the present study. Differentially expressed RNAs (DERNAs), including 74 lncRNAs, 16 miRNAs, and 35 mRNAs, were identified using bioinformatics analysis. The DERNAs were subsequently used to reconstruct a competing endogenous RNA (ceRNA) network. A lncRNA signature was revealed using Cox regression analysis, including LINC00200, MIR137HG, LINC00462, AP002478.1, and HTR2A-AS1. Kaplan-Meier plot demonstrated that the lncRNA signature is highly accurate in discriminating high- and low-risk patients (P < 0.05). The area under curve (AUC) value exceeded 0.7 in both training and validation cohort, suggesting a high prognostic potential of the signature. Furthermore, multivariate Cox regression analysis indicated that both the TNM stage and the lncRNA signature could serve as independent prognostic factors for HCC (P < 0.05). Then, a nomogram comprising the TNM stage and the lncRNA signature was determined to raise the accuracy in predicting the survival of HCC patients. In the present study, we have introduced a ceRNA network that could contribute to provide a new insight into the identification of potential regulation mechanisms for the development of HCC. The five-lncRNA signature could serve as a reliable biosignature for HCC prognosis, while the nomogram possesses strong potential in clinical applications.

Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.