Development and validation of 9-long Non-coding RNA signature to predicting survival in hepatocellular carcinoma

Based on disulfidptosis, unveiling the prognostic and immunological signatures of Asian hepatocellular carcinoma and identifying the potential therapeutic target ZNF337-AS1

BACKGROUND

Disulfidptosis is a newly discovered programmed cell death pathway that may be connected to tumorigenesis and development, showing promise as a novel treatment strategy for cancer. This study aims to construct a prognostic model of disulfidptosis-related Long non-coding RNAs (DRLRs) within the Asian HCC population and to investigate the impact of DRLRs on HCC.

METHODS

Utilising a combination of univariate Cox, Lasso-Cox, and multivariate Cox analyses, five pivotal DRLRs (AC099850.3, ZNF337-AS1, LINC01138, AL031985.3, AC131009.1) were identified, forming a robust prognostic signature. Subsequent validations included Receiver Operating Characteristic (ROC) and Concordance Index analyses, alongside Principal Component Analysis. Comprehensive bioinformatics analysis was performed on the hub DRLRs, followed by experimental validation using quantitative real-time polymerase chain reaction and cellular functional assays.

RESULTS

The risk score independently predicted prognosis, outperforming traditional clinical-pathological factors across varying ages, tumour stages, and pathological classifications in the cohort. A nomogram integrating these variables demonstrated capability in forecasting survival. Multivariate analysis confirmed that the risk score and AJCC TNM staging are independent prognostic factors for predicting overall survival (OS) in Asian HCC patients (both P < 0.001). The prognostic model's ROC area under the ROC values for 1-, 3-, and 5-year predictions were 0.837, 0.794, and 0.783, respectively, indicating its strong diagnostic and prognostic value. Pathway and immune landscape analyses elucidated the biological underpinnings and immune modulations associated with the high-risk group. Immune landscape analysis indicated that both immunescore (P < 0.001) and estimatescore (P < 0.05) were significantly decreased in the high-risk group, with both specific and non-specific immune responses being significantly suppressed, while the tumour immune dysfunction and exclusion score was notably increased (P < 0.001). Tumour mutational burden (TMB) analysis revealed a significantly higher TMB in the high-risk group (P = 0.033) and shorter OS for HCC patients in the high TMB subgroup (P = 0.002). Notably, Potential chemotherapeutic agents (PFI3, 5-Fluorouracil, BPD-00008900, GDC0810, and AZ6102) were identified for high-risk group. Experimental validations through quantitative PCR and in vitro assays confirmed the deregulation of these DRLRs in HCC, with functional studies highlighting the potential of ZNF337-AS1 silencing in curtailing tumour invasiveness.

CONCLUSION

Our investigations validate a DRLR-based risk scoring model as an effective prognostic tool for Asian HCC. This model not only enhances understanding of disulfidptosis's role in HCC but also facilitates personalised treatment strategies, potentially improving patient outcomes.

Construction of a prognostic model for disulfidptosis-related long noncoding RNAs in R0 resected hepatocellular carcinoma and analysis of their impact on malignant behavior

BACKGROUND

Disulfidptosis is an emerging form of cellular death resulting from the binding of intracellular disulfide bonds to actin cytoskeleton proteins. This study aimed to investigate the expression and prognostic significance of hub disulfidptosis-related lncRNAs (DRLRs) in R0 resected hepatocellular carcinoma (HCC) as well as their impact on the malignant behaviour of HCC cells.

METHODS

A robust signature for R0 resected HCC was constructed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression and was validated in an independent internal validation cohort to predict the prognosis of R0 HCC patients. Comprehensive bioinformatics analysis was performed on the hub DRLRs (KDM4A-AS1, MKLN1-AS, and TMCC1-AS1), followed by experimental validation using quantitative real-time polymerase chain reaction (qRT‒PCR) and cellular functional assays.

RESULTS

The signature served as an independent prognostic factor applicable to R0 HCC patients across different age groups, tumour stages, and pathological characteristics. Gene Ontology (GO) and gene set enrichment analysis (GSEA) revealed hub pathways associated with this signature. The high-risk group presented an increased abundance of M0 macrophages and activated memory CD4 T cells as well as elevated macrophage and major histocompatibility complex (MHC) class I expression. High-risk R0 HCC patients also presented increased tumour immune dysfunction and exclusion scores (TIDEs), mutation frequencies, and tumour mutational burdens (TMBs). Drug sensitivity analysis revealed that high-risk patients were more responsive to drugs, including GDC0810 and osimertinib. High expression levels of the three hub DRLRs were detected in R0 HCC tissues and HCC cell lines. Functional assays revealed that the three hub DRLRs enhanced HCC cell proliferation, migration, and invasion.

CONCLUSIONS

A signature was constructed on the basis of three DRLRs, providing novel insights for personalized precision therapy in R0 HCC patients.

Noncoding RNAs and programmed cell death in hepatocellular carcinoma: Significant role of epigenetic modifications in prognosis, chemoresistance, and tumor recurrence rate

Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high death rate in the world. The molecular mechanisms related to the pathogenesis of HCC have not been precisely defined so far. Hence, this review aimed to address the potential cross-talk between noncoding RNAs (ncRNAs) and programmed cell death in HCC. All related papers in the English language up to June 2023 were collected and screened. The searched keywords in scientific databases, including Scopus, PubMed, and Google Scholar, were HCC, ncRNAs, Epigenetic, Programmed cell death, Autophagy, Apoptosis, Ferroptosis, Chemoresistance, Tumor recurrence, Prognosis, and Prediction. According to the reports, ncRNAs, comprising long ncRNAs, microRNAs, circular RNAs, and small nucleolar RNAs can affect cell proliferation, migration, invasion, and metastasis, as well as cell death-related processes, such as autophagy, ferroptosis, necroptosis, and apoptosis in HCC by regulating cancer-associated genes and signaling pathways, for example, phosphoinositide 3-kinase/Akt, extracellular signal-regulated kinase/MAPK, and Wnt/β-catenin signaling pathways. It seems that ncRNAs, as epigenetic regulators, can be utilized as biomarkers in diagnosis, prognosis, survival and recurrence rates prediction, chemoresistance, and evaluation of therapeutic response in HCC patients. However, more scientific evidence is suggested to be accomplished to confirm these results.

KDM4A-AS1 Promotes Cell Proliferation, Migration, and Invasion via the miR-4306/STX6 Axis in Hepatocellular Carcinoma

As a primary liver malignancy, hepatocellular carcinoma (HCC) is commonly induced by chronic liver disease and cirrhosis. Bioinformatics analysis reveals that long noncoding RNA KDM4A antisense RNA 1 (KDM4A-AS1) may be aberrantly expressed in HCC and its abnormal expression might influence prognosis in patients. We conducted this study to illustrate the functions and mechanism of KDM4A-AS1 in regulating HCC malignant cell behavior. KD-M4A-AS1, microRNA (miR)-4306 and messenger RNA syntaxin 6 (STX6) expression was examined by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). HCC cell proliferation, apoptosis, migration, and invasion were measured by colony forming assays, flow cytometry, wound healing and Transwell assays. The interaction between genes was verified by RNA immunoprecipitation and luciferase reporter assays. Western blotting was performed to quantify protein expression of STX6 or apoptotic markers. KDM4A-AS1 was highly expressed in HCC cells and tissues. KDM4A-AS1 knockdown led to enhanced HCC cell apoptosis and suppressed HCC cell proliferation, migration, and invasion. MiR-4306 bound to and negatively regulated STX6. KDM4A-AS1 directly bound to miR-4306 and thus up-regulated STX6. STX6 overexpression reversed the inhibitory influence of KDM4A-AS1 depletion on HCC malignant behavior. KDM4A-AS1 promotes HCC cell migration, invasion, and growth by upregulating STX6 via miR-4306.

Cuproptosis-related prognostic signatures predict the prognosis and immunotherapy in HCC patients

Cuproptosis, an unusual type of programmed cell death mechanism of cell death, involved the disruption of specific mitochondrial metabolic enzymes in the occurrence and development of tumors. However, it was still unclear how the relationship between cuproptosis-related genes (CRGs) may contribute to hepatocellular carcinoma (HCC) potential the prognosis of HCC remained limited. Here, the landscape of 14 CRGs in HCC was evaluated using the Cancer Genome Atlas and International Cancer Genome Consortium datasets. And then, 4 CRGs (ATP7A, MTF1, GLS, and CDKN2A) were screened for the construction of risk signatures for prognosis and drug therapy. The HCC patients with CRGs high-risk showed poor prognosis than those with low risk. Moreover, the CRGs risk signature was shown to be an independent prognostic factor and associated with the immune microenvironment in HCC. Meanwhile, we constructed and verified a prognostic model based on cuproptosis-related lncRNAs (Cr-lncRNAs). We obtained 291 Cr-lncRNAs and constructed Cr-lncRNA prognosis signature based on 3 key Cr-lncRNAs (AC026356.1, NRAV, AL031985.3). The Cr-lncRNA prognosis signature was also an independent prognostic factor and associated with the immune microenvironment in HCC. Finally, the drug sensitivity database showed that 8 candidate drugs related to CRGs signature and Cr-lncRNAs signature. In summary, we evaluated and validated the CRGs and Cr-lncRNAs as potential predictive markers for prognosis, immunotherapy, and drug candidate with the personalized diagnosis and treatment of HCC.

A new marker constructed from immune-related lncRNA pairs can be used to predict clinical treatment effects and prognosis: in-depth exploration of underlying mechanisms in HNSCC

BACKGROUND

Long non-coding RNA (lncRNA) plays a vital role in tumor proliferation, migration, and treatment. Since it is challenging to standardize the gene expression levels detected by different platforms, the signatures composed of many immune-related single lncRNAs are still inaccurate. Utilizing a gene pair formed of two immune-related lncRNAs and strategically assigning values can effectively meet the demand for a higher-accuracy dual biomarker combination.

METHODS

Co-expression and differential expression analyses were performed on immune genes and lncRNAs data from The Cancer Genome Atlas and the ImmPort database to obtain differentially expressed immune-related lncRNAs for pairwise pairing. The prognostic-related differentially expressed immune-related lncRNAs (PR-DE-irlncRNAs) pairs were then identified by univariate Cox regression and used for lasso regression to construct a prognostic model. Various methods were used to validate the predictive prognostic performance of the model. Additionally, we explored the potential guiding value of the model in immunotherapy and chemotherapy and constructed a nomogram suitable for efficient prognosis prediction. Mechanistic exploration of anti-tumor immunity and mutational perspectives are also included. We also analyzed the correlation between the model and immune checkpoint inhibitors (ICIs)-related, N6-methyadenosine (m6A)-related, and multidrug resistance genes.

RESULTS

We used a total of 20 pairs of PR-DE-irlncRNAs to create a prognosis model. Quantitative real-time polymerase chain reaction experiments further verified the abnormal expression of 11 lncRNAs in HNSCC cells. Various methods have confirmed the excellent performance of the model in predicting patient prognosis. We reasoned that lncRNAs/TP53 mutation might play a positive/negative anti-tumor role through the immune system by multi-perspective analyses. Finally, it was found that the prognostic model was closely related to immunotherapy and chemotherapy as well as the expression of ICIs/m6A/multidrug resistance-related genes.

CONCLUSION

The prognostic model performs excellently in predicting the prognosis of patients and provides the potential value of practical guidance for treatment.

E2F1-mediated KDM4A-AS1 up-regulation promotes EMT of hepatocellular carcinoma cells by recruiting ILF3 to stabilize AURKA mRNA

Hepatocellular carcinoma (HCC) is a gastrointestinal tumor with high clinical incidence. Long non-coding RNAs (lncRNAs) play vital roles in modulating the growth and epithelial-mesenchymal transition (EMT) of HCC. However, the underlying mechanism of lncRNA KDM4A antisense RNA 1 (KDM4A-AS1) in HCC remains elusive. In our study, the role of KDM4A-AS1 in HCC was systematically investigated. The levels of KDM4A-AS1, interleukin enhancer-binding factor 3 (ILF3), Aurora kinase A (AURKA), and E2F transcription factor 1 (E2F1) were determined by RT-qPCR or western blot. ChIP and dual luciferase reporter experiments were performed to detect the binding relationship between E2F1 and KDM4A-AS1 promoter sequence. RIP and RNA-pull down confirmed the interaction of ILF3 with KDM4A-AS1/AURKA. Cellular functions were analyzed by MTT, flow cytometry, wound healing and transwell assays. IHC was performed to detect Ki67 in vivo. We found that KDM4A-AS1 was increased in HCC tissues and cells. Elevated KDM4A-AS1 level was correlated to poor prognosis of HCC. Knockdown of KDM4A-AS1 inhibited the proliferation, migration, invasion and EMT of HCC cells. ILF3 bound to KDM4A-AS1 and AURKA. KDM4A-AS1 maintained the stability of AURKA mRNA by recruiting ILF3. E2F1 transcriptionally activated KDM4A-AS1. Overexpressed KDM4A-AS1 reversed the contribution of E2F1 depletion to AURKA expression and EMT in HCC cells. KDM4A-AS1 promoted tumor formation in vivo through the PI3K/AKT pathway. These results revealed that E2F1 transcriptionally activated KDM4A-AS1 to regulate HCC progression via the PI3K/AKT pathway. E2F1 and KDM4A-AS1 may serve as good prognostic targets for HCC treatment.

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.

Identification of an Oxidative Stress-Related LncRNA Signature for Predicting Prognosis and Chemotherapy in Patients With Hepatocellular Carcinoma

Background: Oxidative stress plays a critical role in oncogenesis and tumor progression. However, the prognostic role of oxidative stress-related lncRNA in hepatocellular carcinomas (HCC) has not been fully explored. Methods: We used the gene expression data and clinical data from The Cancer Genome Atlas (TCGA) database to identify oxidative stress-related differentially expressed lncRNAs (DElncRNAs) by pearson correlation analysis. A four-oxidative stress-related DElncRNA signature was constructed by LASSO regression and Cox regression analyses. The predictive signature was further validated by Kaplan-Meier (K-M) survival analysis, receiver operating characteristic (ROC) curves, nomogram and calibration plots, and principal component analysis (PCA). Single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between the signature and immune status. Finally, the correlation between the signature and chemotherapeutic response of HCC patients was analyzed. Results: In our study, the four-DElncRNA signature was not only proved to be a robust independent prognostic factor for overall survival (OS) prediction, but also played a crucial role in the regulation of progression and chemotherapeutic response of HCC. ssGSEA showed that the signature was correlated with the infiltration level of immune cells. HCC patients in high-risk group were more sensitive to the conventional chemotherapeutic drugs including Sorafenib, lapatinib, Nilotinib, Gefitinib, Erlotinib and Dasatinib, which pave the way for targeting DElncRNA-associated treatments for HCC patients. Conclusion: Our study has originated a prognostic signature for HCC based on oxidative stress-related DElncRNAs, deepened the understanding of the biological role of four key DElncRNAs in HCC and laid a theoretical foundation for the choice of chemotherapy.

Analysis of the role of m6A and lncRNAs in prognosis and immunotherapy of hepatocellular carcinoma

Objective

To study the role of m6A and lncRNAs in the prognosis and immunotherapy of hepatocellular carcinoma, construct the risk score of overall survival of hepatocellular carcinoma, and search for new therapeutic targets and drugs.

Methods

The data used in this study are obtained from The Cancer Genome Atlas (TCGA) database. A total of 424 HCC samples were included. The co-expression of lncRNAs and M6A-related genes in HCC was analyzed, COX regression analysis was conducted to construct the risk score for HCC prognosis, and the model's validity was further verified in different clinical trials subtypes and principal component analysis. GO enrichment analysis and immune function analysis were performed for the differential genes in the high-risk group and the low-risk group divided by risk score and analyzed the prognostic effect of TMB on the two groups. Based on the results, potential therapeutic agents for HCC were screened.

Results

The risk score can better predict the prognosis of HCC, the area under the ROC curve is 0.727. Differential genes were mainly located in the extracellular matrix and chromosomal regions and may play regulatory roles in binding sites and catalytic enzymes, thereby affecting the chromosome division and cell proliferation of cells. Type Ⅱ IFN response, type Ⅰ IFN response and MHC class Ⅰ were the three most different functions in terms of immune function between the high-risk group and the low-risk group. Type II IFN response, type I IFN response was significantly down-regulated in the high-risk group, while MHC class I was up-regulated. 14 potential therapeutic drugs were screened out.

Conclusions

The risk score constructed with NRAV and AL031985.3 had a good predictive effect on the prognosis of HCC. Differences in genes and immune function between high-risk and low-risk groups promoted the occurrence and progression of HCC.

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.

Bioinformatics analysis and experimental verification of five metabolism-related lncRNAs as prognostic models for hepatocellular carcinoma

BACKGROUND

The number of patients with hepatocellular carcinoma (HCC) is showing a growing trend all over the world. The metabolic microenvironment has been shown to play a key role in the pathogenesis of HCC in recent studies. The expression of metabolites and metabolic processes in tumor cells can be regulated by gene regulation mediated by long non-coding RNAs (lncRNAs), the abnormal expression of which is closely related to tumor occurrence and metastasis. However, the fundamental mechanism of applying metabolism-related lncRNAs to predicting HCC is still unclear.

METHODS

With the complete RNA sequence data and clinical data obtained from The Cancer Genome Atlas database and metabolism-related genes downloaded from the Kyoto Encyclopedia of Genes and Genomes database, with false discovery rate < 0.001, log fold change > 1.5 selected as the screening criteria for lncRNA, the relationship between the expression level of metabolism-related LncRNAs (MRLs) and the overall survival rate was determined by the Univariate Cox regression analyses with the establishment of the metabolic prognosis model by the application of Multivariate Cox regression analyses, revealing the different biological processes and signaling pathways in both high-risk groups and low-risk groups by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and gene set enrichment analysis, leading the expression of lncRNA to be assessed by the reverse transcription-polymerase chain reaction results.

RESULTS

The overall survival rate of HCC patients is significantly correlated with signature of 5-MRLs. The prognosis characteristics of lncRNA reveal the relatively high death rate of patients in the high-risk groups, with the predicted signals by functional and pathway enrichment analysis related to biosynthesis, metabolic process, and metabolic pathway, with the prognostic characteristics of 5-MRLs by the combined analysis showing that it is an independent factor of HCC superior to the traditional clinical indicators in predicting the prognosis. A trend of high-expression was shown in MRLs in tumors by reverse transcription-polymerase chain reaction.

CONCLUSION

The new 5-MRLs as potential biomarkers provide more powerful prognostic information for HCC patients. In the future clinical treatment of HCC, it will provide doctors with more methods.

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.

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.

Use of 6 m6A-relevant lncRNA genes as prognostic markers of primary liver hepatocellular carcinoma based on TCGA database

BACKGROUND

Hepatocellular carcinoma (HCC) is diagnosed at the middle and advanced stages, negating radical treatment. Identifying specific and effective prognostic HCC biomarkers is important and can facilitate the discovery of potential therapeutic targets. N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) are associated with the development of multiple tumors. The role of m6A-relevant lncRNAs in the initiation and progression of HCC is unclear. The aim of the present study was to investigate the expression of m6A-relevant lncRNAs in HCC and to identify new prognostic markers of the disease.

METHODS

Gene expression and clinical data were retrieved from The Cancer Genome Atlas database. m6A-relevant lncRNAs were identified by co-expression analysis and were screened by univariate Cox regression analysis. Different HCC patient clusters were established via consensus clustering. Gene set enrichment analysis (GSEA) was used to determine the cluster enrichment pathways. A risk score model was constructed, and Kaplan-Meier analysis of the overall survival (OS) between cluster 1 (high risk) and cluster 2 (low risk) was performed. Relationships between the clusters, risk scores, and clinicopathological characteristics were clarified.

RESULTS

Of the 1,852 m6A-relevant lncRNAs identified, 68 had prognostic relevance. The pathological grade, American Joint Committee on Cancer stage, and T stage of cluster 1 were significantly more advanced than those of cluster 2. Based on GSEA, mitotic spindle, G2M_CHECKPOINT, glycolysis, the phosphoinositide 3-kinase (PI3K) protein kinase B (AKT) mammalian target of rapamycin (mTOR) pathway, and DNA repair were more enriched in cluster 1. Six key m6A-relevant lncRNAs were selected to build a risk score model predicting the prognosis of HCC. The OS of patients in the high-risk group was shorter than that of patients in the low-risk group. Risk score was an independent prognostic factor of HCC patients.

CONCLUSIONS

The findings indicated that m6A-relevant lncRNAs may be important in the progression of HCC. The risk score model based on the 6 key m6A-relevant lncRNAs can accurately predict the prognosis of patients with HCC.

Long non-coding RNA TMCC1-AS1 predicts poor prognosis and accelerates epithelial-mesenchymal transition in liver cancer

Long non-coding RNA transmembrane and coiled-coil domain family 1 antisense RNA 1 (TMCC1-AS1) has been frequently reported to be associated with prognosis in patients with liver cancer (LC). However, the biological role of TMCC1-AS1 in LC in vitro remains unclear. The expression levels of TMCC1-AS1 in primary tumor tissues and LC cell lines were determined using reverse transcription-quantitative PCR. The associations between TMCC1-AS1 expression and the clinicopathological factors of patients with LC were statistically analyzed using the χ2 test. The role of TMCC1-AS1 in LC prognosis was assessed using Kaplan-Meier curves and proportional hazards model (Cox) analysis. Cell proliferation was determined by Cell Counting Kit-8 and colony formation assays. Transwell assays were performed to determine migration and invasion. TMCC1-AS1 expression was found to be significantly upregulated in LC tissues and cell lines compared with the corresponding controls. High TMCC1-AS1 expression was associated with advanced TNM stage and lymph node metastasis. Furthermore, high TMCC1-AS1 expression predicted poor survival in patients with LC. Knockdown of TMCC1-AS1 significantly inhibited the proliferation, migration and invasion of HepG2 and SNU-182 cells, while overexpression of TMCC1-AS1 had the opposite effect in HepG2 and SNU-182 cells. At the molecular level, downregulation of TMCC1-AS1 expression resulted in increased E-cadherin expression and decreased proliferating cell nuclear antigen, Ki67, N-cadherin and Vimentin expression in HepG2 cells. Overexpression of TMCC1-AS1 had the opposite effects on these factors in SNU-182 cells. In conclusion, the present findings indicated that TMCC1-AS1 might be considered as a novel oncogene, which promotes cell proliferation and migration, and may be a potential therapeutic target for LC.

Identification of a Ferroptosis-Related Signature Model Including mRNAs and lncRNAs for Predicting Prognosis and Immune Activity in Hepatocellular Carcinoma

Background

Ferroptosis is a novel form of regulated cell death involved in tumor progression. The role of ferroptosis-related lncRNAs in hepatocellular carcinoma (HCC) remains unclear.

Methods

RNA-seq and clinical data for HCC patients were downloaded from The Cancer Genome Atlas (TCGA) Genomic Data Commons (GDC) portal. Bioinformatics methods, including weighted gene coexpression network analysis (WGCNA), Cox regression, and least absolute shrinkage and selection operator (LASSO) analysis, were used to identify signature markers for diagnosis/prognosis. The tumor microenvironment, immune infiltration and functional enrichment were compared between the low-risk and high-risk groups. Subsequently, small molecule drugs targeting ferroptosis-related signature components were predicted via the L1000FWD and PubChem databases.

Results

The prognostic model consisted of 2 ferroptosis-related mRNAs (SLC1A5 and SLC7A11) and 8 ferroptosis-related lncRNAs (AC245297.3, MYLK-AS1, NRAV, SREBF2-AS1, AL031985.3, ZFPM2-AS1, AC015908.3, MSC-AS1). The areas under the curves (AUCs) were 0.830 and 0.806 in the training and test groups, respectively. Decision curve analysis (DCA) revealed that the ferroptosis-related signature performed better than all pathological characteristics. Multivariate Cox regression analysis showed that the risk score was an independent prognostic factor. The survival probability of low- and high-risk patients could be clearly distinguished by the principal component analysis (PCA) plot. The risk score divided HCC patients into two distinct groups in terms of immune status, especially checkpoint gene expression, which was further supported by the Gene Ontology (GO) biological process, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, several small molecule drugs (SIB-1893, geldanamycin and PD-184352, etc) targeting ferroptosis-related signature components were identified for future reference.

Conclusion

We constructed a new ferroptosis-related mRNA/lncRNA signature for HCC patients. The model can be used for prognostic prediction and immune evaluation, providing a reference for immunotherapies and targeted therapies.

Prognostic Signature of Hepatocellular Carcinoma and Analysis of Immune Infiltration Based on m6A-Related lncRNAs

The relationship between m6A-related lncRNAs and prognosis in hepatocellular carcinoma (HCC) is not yet clear. We used Lasso regression to establish a prognostic signature based on m6A-related lncRNAs using a training set from TCGA, and then verified the signature efficacy in a test set. Fluorescence quantitative real-time PCR (qPCR), Survival analysis, clinical risk difference analysis, immune-related analysis, and drug-sensitivity analysis were conducted. The results revealed that 1,651 lncRNAs were differentially expressed in HCC tissues, among which, 163 were m6A-related. Univariate analysis showed that 87 lncRNAs were associated with the overall survival. Six differential m6A-related lncRNAs were validated and selected via Lasso regression to construct a prognostic signature which demonstrated a satisfactory predictive efficacy. In the clinically relevant pathologic stage, histologic grade, and T stage, the risk scores obtained based on this signature showed a statistically significant difference. The high- and low-risk groups exhibited a difference in the tumor immune infiltrating cells, immune checkpoint gene expression, and sensitivity to chemotherapy. In summary, the prognostic signature based on the m6A-related lncRNAs can effectively predict the prognosis of patients and might provide a new vista for the chemotherapy and immunotherapy of HCC.

Immune-Related lncRNA to Construct Novel Signature and Predict the Immune Landscape of Human Hepatocellular Carcinoma

The signature composed of immune-related long noncoding ribonucleic acids (irlncRNAs) with no requirement of specific expression level seems to be valuable in predicting the survival of patients with hepatocellular carcinoma (HCC). Here, we retrieved raw transcriptome data from The Cancer Genome Atlas (TCGA), identified irlncRNAs by co-expression analysis, and recognized differently expressed irlncRNA (DEirlncRNA) pairs using univariate analysis. In addition, we modified Lasso penalized regression. Then, we compared the areas under curve, counted the Akaike information criterion (AIC) values of 5-year receiver operating characteristic curve, and identified the cut-off point to set up an optimal model for distinguishing the high- or low-disease-risk groups among patients with HCC. We then reevaluated them from the viewpoints of survival, clinic-pathological characteristics, tumor-infiltrating immune cells, chemotherapeutics efficacy, and immunosuppressed biomarkers. 36 DEirlncRNA pairs were identified, 12 of which were included in a Cox regression model. After regrouping the patients by the cut-off point, we could more effectively differentiate between them based on unfavorable survival outcome, aggressive clinic-pathological characteristics, specific tumor immune infiltration status, low chemotherapeutics sensitivity, and highly expressed immunosuppressed biomarkers. The signature established by paring irlncRNA regardless of expression levels showed a promising clinical prediction value.

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.