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

Unveiling the functions of five recently characterized lncRNAs in cancer progression

Numerous studies over the past few decades have shown that RNAs are multifaceted, multifunctional regulators of most cellular processes, contrary to the initial belief that they only act as mediators for translating DNA into proteins. LncRNAs, which refer to transcripts longer than 200nt and lack the ability to code for proteins, have recently been identified as central regulators of a variety of biochemical and cellular processes, particularly cancer. When they are abnormally expressed, they are closely associated with tumor occurrence, metastasis, and tumor staging. Therefore, through searches on Google Scholar, PubMed, and CNKI, we identified five five recently characterized lncRNAs-Lnc-SLC2A12-10:1, LncRNA BCRT1, lncRNA IGFBP4-1, LncRNA PCNAP1, and LncRNA CDC6-that have been linked to the promotion of cancer cell proliferation, invasion, and metastasis. Consequently, this review encapsulates the existing research and molecular underpinnings of these five newly identified lncRNAs across various types of cancer. It suggests that these novel lncRNAs hold potential as independent biomarkers for clinical diagnosis and prognosis, as well as candidates for therapeutic intervention. In parallel, we discuss the challenges inherent in the research on these five newly discovered lncRNAs and look forward to the avenues for future exploration in this field.

Anoikis-related lncRNA signature predicts prognosis and is associated with immune infiltration in hepatocellular carcinoma

Anoikis is a programmed cell death process triggered when cells are dislodged from the extracellular matrix. Numerous long noncoding RNAs (lncRNAs) have been identified as significant factors associated with anoikis resistance in various tumor types, including glioma, breast cancer, and bladder cancer. However, the relationship between lncRNAs and the prognosis of hepatocellular carcinoma (HCC) has received limited research attention. Further research is needed to investigate this potential link and understand the role of lncRNAs in the progression of HCC. We developed a prognostic signature based on the differential expression of lncRNAs implicated in anoikis in HCC. A co-expression network of anoikis-related mRNAs and lncRNAs was established using data obtained from The Cancer Genome Atlas (TCGA) for HCC. Cox regression analyses were conducted to formulate an anoikis-related lncRNA signature (ARlncSig) in a training cohort, which was subsequently validated in both a testing cohort and a combined dataset comprising the two cohorts. Receiver operating characteristic curves, nomograms, and decision curve analyses based on the ARlncSig score and clinical characteristics demonstrated robust predictive ability. Moreover, gene set enrichment analysis revealed significant enrichment of several immune processes in the high-risk group compared to the low-risk group. Furthermore, significant differences were observed in immune cell subpopulations, expression of immune checkpoint genes, and response to chemotherapy and immunotherapy between the high- and low-risk groups. Lastly, we validated the expression levels of the five lncRNAs included in the signature using quantitative real-time PCR. In conclusion, our ARlncSig model holds substantial predictive value regarding the prognosis of HCC patients and has the potential to provide clinical guidance for individualized immunotherapy. In this study, we obtained 36 genes associated with anoikis from the Gene Ontology and Gene Set Enrichment Analysis databases. We also identified 22 differentially expressed lncRNAs that were correlated with these genes using data from TCGA. Using Cox regression analyses, we developed an ARlncSig in a training cohort, which was then validated in both a testing cohort and a combined cohort comprising data from both cohorts. Additionally, we collected eight pairs of liver cancer tissues and adjacent tissues from the Affiliated Tumor Hospital of Nantong University for further analysis. The aim of this study was to investigate the potential of ARlncSig as a biomarker for liver cancer prognosis. The study developed a risk stratification system called ARlncSig, which uses five lncRNAs to categorize liver cancer patients into low- and high-risk groups. Patients in the high-risk group exhibited significantly lower overall survival rates compared to those in the low-risk group. The model's predictive performance was supported by various analyses including the receiver operating characteristic curve, nomogram calibration, clinical correlation analysis, and clinical decision curve. Additionally, differential analysis of immune function, immune checkpoint, response to chemotherapy, and immune cell subpopulations revealed significant differences between the high- and low-risk groups. Finally, quantitative real-time PCR validated the expression levels of the five lncRNAs. In conclusion, the ARlncSig model demonstrates critical predictive value in the prognosis of HCC patients and may provide clinical guidance for personalized immunotherapy.

Chlamydia trachomatis upregulates lncRNA CYTOR to mediate autophagy through miR-206/MAPK1 axis

Chlamydia trachomatis infection can be regulated by autophagy-related genes. LncRNA CYTOR has been proven to be involved in autophagy. In this research, we investigated the role of CYTOR in autophagy induced by C. trachomatis and the potential mechanisms. After C. trachomatis infection, CYTOR and MAPK1 were up-regulated and miR-206 was down-regulated, meanwhile, the autophagy-related protein Beclin1 and LC3-Ⅱ/LC3-Ⅰ ratio were increased. Interference with CYTOR or overexpression with miR-206 downregulated the autophagy-related protein Beclin1 and the number of autophagic spots LC3, decreased the protein ratio of LC3-II/LC3-I, and upregulated the expression of P62 protein. The luciferase reporter assay confirmed that CYTOR acted as a sponge for miR-206 to target MAPK1. In addition, CYTOR promoted autophagy induced by C. trachomatis infection through the MAPK1/ERK signaling pathway activation. Taken together, we have identified a novel molecular mechanism that the CYTOR/miR-206/MAPK1 axis was involved in the regulation of autophagy in C. trachomatis infection. This work provides an experimental basis for elucidating the pathogenesis of C. trachomatis for the treatment, prevention and control of related infectious diseases.

A disulfidptosis-related lncRNAs signature in hepatocellular carcinoma: prognostic prediction, tumor immune microenvironment and drug susceptibility

Disulfidptosis, a novel type of programmed cell death, has attracted researchers' attention worldwide. However, the role of disulfidptosis-related lncRNAs (DRLs) in liver hepatocellular carcinoma (LIHC) not yet been studied. We aimed to establish and validate a prognostic signature of DRLs and analyze tumor microenvironment (TME) and drug susceptibility in LIHC patients. RNA sequencing data, mutation data, and clinical data were obtained from the Cancer Genome Atlas Database (TCGA). Lasso algorithm and cox regression analysis were performed to identify a prognostic DRLs signature. Kaplan-Meier curves, principal component analysis (PCA), nomogram and calibration curve, function enrichment, TME, immune dysfunction and exclusion (TIDE), tumor mutation burden (TMB), and drug sensitivity analyses were analyzed. External datasets were used to validate the predictive value of DRLs. qRT-PCR was also used to validate the differential expression of the target lncRNAs in tissue samples and cell lines. We established a prognostic signature for the DRLs (MKLN1-AS and TMCC1-AS1) in LIHC. The signature could divide the LIHC patients into low- and high-risk groups, with the high-risk subgroup associated with a worse prognosis. We observed discrepancies in tumor-infiltrating immune cells, immune function, function enrichment, and TIDE between two risk groups. LIHC patients in the high-risk group were more sensitive to several chemotherapeutic drugs. External datasets, clinical tissue, and cell lines confirmed the expression of MKLN1-AS and TMCC1-AS1 were upregulated in LIHC and associated with a worse prognosis. The novel signature based on the two DRLs provide new insight into LIHC prognostic prediction, TME, and potential therapeutic strategies.

Constructed Risk Prognosis Model Associated with Disulfidptosis lncRNAs in HCC

Disulfidptosis is a novel cell death mode in which the accumulation of disulfide bonds in tumor cells leads to cell disintegration and death. Long-stranded noncoding RNAs (LncRNAs) are aberrantly expressed in hepatocellular carcinoma (HCC) and have been reported to carry significant potential as a biomarker for HCC prognosis. However, lncRNA studies with disulfidptosis in hepatocellular carcinoma have rarely been reported. Therefore, this study aimed to construct a risk prognostic model based on the disulfidptosis-related lncRNA and investigate the mechanisms associated with disulfidptosis in hepatocellular carcinoma. The clinical and transcriptional information of 424 HCC patients was downloaded from The Cancer Genome Atlas (TCGA) and divided into test and validation sets. Furthermore, 1668 lncRNAs associated with disulfidptosis were identified using Pearson correlation. Six lncRNA constructs were finally identified for the risk prognostic model using one-way Cox proportional hazards (COX), multifactorial COX, and lasso regression. Kaplan-Meier (KM) analysis, principal component analysis, receiver operating characteristic curve (ROC), C-index, and column-line plot results confirmed that the constructed model was an independent prognostic factor. Based on the disulfidptosis risk score, risk groups were identified as potential predictors of immune cell infiltration, drug sensitivity, and immunotherapy responsiveness. Finally, we confirmed that phospholipase B domain containing 1 antisense RNA 1 (PLBD1-AS1) and muskelin 1 antisense RNA (MKLN1-AS) were highly expressed in hepatocellular carcinoma and might be potential biomarkers in HCC by KM analysis and quantitative real-time PCR (RT-qPCR). This study demonstrated that lncRNA related to disulfidptosis could serve as a biomarker to predict prognosis and treatment targets for HCC.

Cuproptosis-related lncRNAs predict the prognosis and immune response in hepatocellular carcinoma

Cuproptosis has been recently used to indicate unique biological processes triggered by Cu action as a new term. This study aimed to explore the relationship between cuproptosis-related lncRNA and hepatocellular carcinoma (HCC) with regard to immunity and prognosis. RNA sequencing and the clinical data were downloaded from the TCGA database. The cuproptosis-related genes were sorted out through literature study. The cuproptosis-related IncRNA signature was identified by Cox regression analysis and the least absolute shrinkage and selection operator analysis. The K-M survival analysis, receiver operating characteristic analysis, and C-index analysis were adopted to evaluate the prognostic prediction performance of the signature. The functional enrichment, immune infiltration and tumor mutation analysis were further analyzed. Subsequently, we predicted the differences in chemosensitivity from tumor gene expression levels for some chemotherapy drugs. The prognostic signature consisting of 5 overall survival-related CUPlncRNAs. It showed an extraordinary ability to predict the prognoses of patients with HCC. The signature can predict the abundance of immune cell infiltration, immune functions, expression of immune checkpoint inhibitors, m6A genes, which was supported by the GO biological process and KEGG analysis. And it may also have a guiding effect in the sensitivity of different chemotherapeutic drugs and tumor mutation burden. We constructed a new cuproptosis-related lncRNA signature for HCC patients. The model can be used for prognostic prediction and immune evaluation, providing a reference for immunotherapies and targeted therapies.

Assessment of prognostic role of a novel 7-lncRNA signature in HCC patients

Background

Hepatocellular carcinoma (HCC) is characterized by extensive risk factors, high morbidity and mortality. Clinical prognostic evaluation assay assumes a nonspecific quality. Better HCC prognostics are urgently needed. Long noncoding RNAs (lncRNAs) exerts a crucial role in tumorigenesis and development. Excavating specific lncRNAs signature to ameliorate the high-risk survival prediction in HCC patients is worthwhile.

Methods

Differentially expressed lncRNAs (DElncRNAs) profile was acquired from The Cancer Genome Atlas database (TCGA). Then, the lncRNAs high-risk survival prognostic model was established using the least absolute shrinkage and selection operator (LASSO)-Cox regression algorithm. The lncRNAs were evaluated in clinical specimen by PCR. The receiver operating characteristic curve (ROC) analysis was further conducted to assess the potential prognostic value of the model. Moreover, a visible nomogram containing clinicopathological features and prognostic model was developed for prediction of survival property. Potential molecular mechanism was assessed by GO, KEGG, GSEA enrichment analysis and CIBERSORT immune infiltration analysis.

Results

A novel 7-lncRNA risk model (AL161937.2, LINC01063, AC145207.5, POLH-AS1, LNCSRLR, MKLN1-AS, AC105345.1) was constructed and validated for HCC prognosis prediction. Kaplan-Meier analysis revealed that patients in the high-risk group suffered a poor prognosis (p = 1.813 × 10^-8). These genes were detected by PCR, and the expression trend was in accordance with TCGA database. Interestingly, the risk score served as an independent risk factor for HCC patients (HR: 1.166, 95% CI:1.119-1.214, p < 0.001). The nomogram was established, and the predictive accuracy in the nomogram was prior to the TNM stage according to the ROC curve analysis. Cell proliferation related pathway, decreased CD4⁺ T cell, CD8⁺ T cell, NK cell and elevated Neutrophil, Macrophage M0 were observed in high-risk group. Besides, suppression of MKLN1-AS expression inhibited cell proliferation of HCC cells by CCK8 assay in vitro.

Conclusion

The 7-lncRNA signature may exert a particular prognostic prediction role in HCC and provide new insight in HCC carcinogenesis.

Cuproptosis-related lncRNAs predict prognosis and immune response of thyroid carcinoma

Objective: To estimate the survival and prognosis of patients with thyroid carcinoma (THCA) based on the Long non-coding RNA (lncRNA) traits linked to cuproptosis and to investigate the connection between the immunological spectrum of THCA and medication sensitivity. Methods: RNA-Seq data and clinical information for THCA were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We built a risk prognosis model by identifying and excluding lncRNAs associated with cuproptosis using Cox regression and LASSO methods. Both possible biological and immune infiltration functions were investigated using Principal Component Analysis (PCA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and immunoassays. The sensitivity of the immune response to possible THCA medicines was assessed using ratings for tumor immune dysfunction and exclusion (TIDE) and tumor mutational burden (TMB). Results: Seven cuproptosis-related lncRNAs were used to construct our prognostic prediction model: AC108704.1, DIO3OS, AL157388.1, AL138767.3, STARD13-AS, AC008532.1, and PLBD1-AS1. Using data from TCGA's training, testing, and all groups, Kaplan-Meier and ROC curves demonstrated this feature's adequate predictive validity. Different clinical characteristics have varying effects on cuproptosis-related lncRNA risk models. Further analysis of immune cell infiltration and single sample Gene Set Enrichment Analysis (ssGSEA) supported the possibility that cuproptosis-associated lncRNAs and THCA tumor immunity were closely connected. Significantly, individuals with THCA showed a considerable decline in survival owing to the superposition effect of patients in the high-risk category and high TMB. Additionally, the low-risk group had a higher TIDE score compared with the high-risk group, indicating that these patients had suboptimal immune checkpoint blocking responses. To ensure the accuracy and reliability of our results, we further verified them using several GEO databases. Conclusion: The clinical and risk aspects of cuproptosis-related lncRNAs may aid in determining the prognosis of patients with THCA and improving therapeutic choices.

Characterization of the circulating transcriptome expression profile and identification of novel miRNA biomarkers in hypertrophic cardiomyopathy

BACKGROUND

Hypertrophic cardiomyopathy (HCM), one of the most common genetic cardiovascular diseases, but cannot be explained by single genetic factors. Circulating microRNAs (miRNAs) are stable and highly conserved. Inflammation and immune response participate in HCM pathophysiology, but whether the miRNA profile changes correspondingly in human peripheral blood mononuclear cells (PBMCs) with HCM is unclear. Herein, we aimed to investigate the circulating non-coding RNA (ncRNA) expression profile in PBMCs and identify potential miRNAs for HCM biomarkers.

METHODS

A Custom CeRNA Human Gene Expression Microarray was used to identify differentially expressed (DE) mRNAs, miRNAs, and ncRNAs (including circRNA and lncRNA) in HCM PBMCs. Weighted correlation network analysis (WGCNA) was used to identify HCM-related miRNA and mRNA modules. The mRNAs and miRNAs from the key modules were used to construct a co-expression network. Three separate machine learning algorithms (random forest, support vector machine, and logistic regression) were applied to identify potential biomarkers based on miRNAs from the HCM co-expression network. Gene Expression Omnibus (GEO) database (GSE188324) and experimental samples were used for further verification. Gene set enrichment analysis (GSEA) and competing endogenous RNA (ceRNA) network was used to determine the potential functions of the selected miRNAs in HCM.

RESULTS

We identified 1194 DE-mRNAs, 232 DE-miRNAs and 7696 DE-ncRNAs in HCM samples compared with normal controls from the microarray data sets. WGCNA identified key miRNA modules and mRNA modules evidently associated with HCM. We constructed a miRNA‒mRNA co-expression network based on these modules. A total of three hub miRNAs (miR-924, miR-98 and miR-1) were identified by random forest, and the areas under the receiver operator characteristic curves of miR-924, miR-98 and miR-1 were 0.829, 0.866, and 0.866, respectively.

CONCLUSIONS

We elucidated the transcriptome expression profile in PBMCs and identified three hub miRNAs (miR-924, miR-98 and miR-1) as potential biomarkers for HCM detection.

Identification of hub genes and potential ceRNA networks of diabetic cardiomyopathy

Diabetic cardiomyopathy (DCM), a common complication of diabetes, is defined as ventricular dysfunction in the absence of underlying heart disease. Noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), play a crucial role in the development of DCM. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify key modules in DCM-related pathways. DCM-related miRNA-mRNA network and DCM-related ceRNA network were constructed by miRNA-seq to identify hub genes in these modules. We identified five hub genes that are associated with the onset of DCM, including Troponin C1 (Tnnc1), Phospholamban (Pln), Fatty acid binding proteins 3 (Fabp3), Popeye domain containing 2 (Popdc2), and Tripartite Motif-containing Protein 63 (Trim63). miRNAs that target the hub genes were mainly involved in TGF-β and Wnt signaling pathways. GO BP enrichment analysis found these miRNAs were involved in the signaling of TGF-β and glucose homeostasis. Q-PCR results found the gene expressions of Pln, Fabp3, Trim63, Tnnc1, and Popdc2 were significantly increased in DCM. Our study identified five hub genes (Tnnc1, Pln, Fabp3, Popdc2, Trim63) whose associated ceRNA networks are responsible for the onset of DCM.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Anesthetic‑specific lncRNA and mRNA profile changes in blood during colorectal cancer resection: A prospective, matched‑case pilot study

Prometastatic and antitumor effects of different anesthetics have been previously analyzed in several studies with conflicting results. Thus, the underlying perioperative molecular mechanisms mediated by anesthetics potentially affecting tumor phenotype and metastasis remain unclear. It was hypothesized that anesthetic‑specific long non‑coding RNA (lncRNA) expression changes are induced in the blood circulation and play a crucial role in tumor outcome. In the present study, high‑throughput sequencing and quantitative PCR were performed in order to identify lncRNA and mRNA expression changes affected by two therapeutic regimes, total intravenous anesthesia (TIVA) and volatile anesthetic gas (VAG) in patients undergoing colorectal cancer (CRC) resection. Total blood RNA was isolated prior to and following resection and characterized using RNA sequencing. mRNA‑lncRNA interactions and their roles in cancer‑related signaling of differentially expressed lncRNAs were identified using bioinformatics analyses. The comparison of these two time points revealed 35 differentially expressed lncRNAs in the TIVA‑group, and 25 in the VAG‑group, whereas eight were shared by both groups. Two lncRNAs in the TIVA‑group, and 23 in the VAG‑group of in silico identified target‑mRNAs were confirmed as differentially regulated in the NGS dataset of the present study. Pathway analysis was performed and cancer relevant canonical pathways for TIVA were identified. Target‑mRNA analysis of VAG revealed a markedly worsened immunological response against cancer. In this proof‑of‑concept study, anesthesic‑specific expression changes in lncRNA and mRNA profiles in blood were successfully identified. Moreover, the data of the present study provide the first evidence that anesthesia‑induced lncRNA pattern changes may contribute further in the observed differences in CRC outcome following tumor resection.

Buffy Coat Transcriptomic Analysis Reveals Alterations in Host Cell Protein Synthesis and Cell Cycle in Severe COVID-19 Patients

Transcriptome studies have reported the dysregulation of cell cycle-related genes and the global inhibition of host mRNA translation in COVID-19 cases. However, the key genes and cellular mechanisms that are most affected by the severe outcome of this disease remain unclear. For this work, the RNA-seq approach was used to study the differential expression in buffy coat cells of two groups of people infected with SARS-CoV-2: (a) Mild, with mild symptoms; and (b) SARS (Severe Acute Respiratory Syndrome), who were admitted to the intensive care unit with the severe COVID-19 outcome. Transcriptomic analysis revealed 1009 up-regulated and 501 down-regulated genes in the SARS group, with 10% of both being composed of long non-coding RNA. Ribosome and cell cycle pathways were enriched among down-regulated genes. The most connected proteins among the differentially expressed genes involved transport dysregulation, proteasome degradation, interferon response, cytokinesis failure, and host translation inhibition. Furthermore, interactome analysis showed Fibrillarin to be one of the key genes affected by SARS-CoV-2. This protein interacts directly with the N protein and long non-coding RNAs affecting transcription, translation, and ribosomal processes. This work reveals a group of dysregulated processes, including translation and cell cycle, as key pathways altered in severe COVID-19 outcomes.

A new acidic microenvironment related lncRNA signature predicts the prognosis of liver cancer patients

BACKGROUND

The acidic microenvironment (AME), like hypoxia, inflammation, or immunoreaction, is a hallmark of the tumor microenvironment (TME). This work aimed to develop a prediction signature dependent on AME-associated lncRNAs in order to predict the prognosis of LC individuals.

METHODS

We downloaded RNA-seq information and the corresponding clinical and predictive data from The Cancer Genome Atlas (TCGA) dataset and conducted univariate and multivariate Cox regression analyses to identify AME-associated lncRNAs for the construction of a prediction signature The Kaplan-Meier technique was utilized to determine the overall survival (OS) rate of the high (H)-risk and low (L)-risk groups. Using gene set enrichment analysis (GSEA) the functional variations between the H- and L-risk groups were investigated. The association between the prediction signature and immunological state was investigated using single-sample GSEA (ssGSEA). Additionally, the association between the predicted signature and the therapeutic response of LC individuals was evaluated. Lastly, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to verify the risk model.

RESULTS

We generated a signature comprised of seven AME-associated lncRNAs (LINC01116, AC002511.2, LINC00426, ARHGAP31-AS1, LINC01060, TMCC1-AS1, AC012065.1). The H-risk group had a worse prognosis than the L- risk group. The AME-associated lncRNA signature might determine the prognosis of individuals with LC independently. The AME-related lncRNA signature shows a greater predictive effectiveness than clinic-pathological factors, with an area under the receiver operating characteristic (ROC) curve of 0.806%. When participants were categorized based on several clinico-pathological characteristics, the OS of high-risk individuals was shorter compared to low-risk patients. GSEA demonstrated that the metabolism of different acids and the PPAR signaling pathway are closely associated with low-risk individuals. The prognostic signature was substantially associated with the immunological status of LC individuals, as determined by ssGSEA. High risk individuals were more sensitive to some immunotherapies (including anti-TNFSF4 anti-SIRPA, anti-CD276 and anti-TNFSF15) and some conventional chemotherapy drugs (including lapatinib and paclitaxel). Finally, the expression levels of the seven lncRNAs comprising the signature were tested by qRT-PCR.

CONCLUSIONS

A basis for the mechanism of AME-associated lncRNAs in LC is provided by the prediction signature, which also offers clinical therapeutic recommendations for LC individuals.

A novel necroptosis-related lncRNA signature predicts the prognosis and immune microenvironment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the malignant tumors with high mortality and a worse prognosis globally. Necroptosis is a programmed death mediated by receptor-interacting Protein 1 (RIP1), receptor-interacting Protein 1 (RIP3), and Mixed Lineage Kinase Domain-Like (MLKL). Our study aimed to create a new Necroptosis-related lncRNAs (NRlncRNAs) risk model that can predict survival and tumor immunity in HCC patients. The RNA expression and clinical data originated from the TCGA database. Pearson correlation analysis was applied to identify the NRlncRNAs. The LASSO-Cox regression analysis was employed to build the risk model. Next, the ROC curve and the area under the Kaplan-Meier curve were utilized to evaluate the accuracy of the risk model. In addition, based on the two groups of risk model, we performed the following analysis: clinical correlation, differential expression, PCA, TMB, GSEA analysis, immune cells infiltration, and clinical drug prediction analysis. Plus, qRT-PCR was applied to test the expression of genes in the risk model. Finally, a prognosis model covering six necroptosis-related lncRNAs was constructed to predict the survival of HCC patients. The ROC curve results showed that the risk model possesses better accuracy. The 1, 3, and 5-years AUC values were 0.746, 0.712, and 0.670, respectively. Of course, we also observed that significant differences exist in the following analysis, such as functional signaling pathways, immunological state, mutation profiles, and medication sensitivity between high-risk and low-risk groups of HCC patients. The result of qRT-PCR confirmed that three NRlncRNAs were more highly expressed in HCC cell lines than in the normal cell line. In conclusion, based on the bioinformatics analysis, we constructed an NRlncRNAs associated risk model, which predicts the prognosis of HCC patients. Although our study has some limitations, it may greatly contribute to the treatment of HCC and medical progression.

Prognostic prediction of head and neck squamous cell carcinoma: Construction of cuproptosis-related long non-coding RNA signature

BACKGROUND

Recently, a new type of programmed cell death, cuproptosis, has been identified to play important role in the progression of tumors. We constructed a cuproptosis-related long non-coding RNA (lncRNA) signature to predict the prognostic significance for head and neck squamous cell carcinoma (HNSCC).

METHODS

The risk model was developed based on differentially expressed lncRNAs associated with cuproptosis. Principal component analysis was used to assess the validity. The Kaplan-Meier curves were analyzed to compare the overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) values. The multivariate and univariate Cox regression analyses were used to evaluate the prognostic efficiency. Furthermore, the functional enrichment, immune cell infiltration, tumor mutation burden (TMB), and sensitivity toward chemotherapy were also explored.

RESULTS

Six cuproptosis-related lncRNAs (AL109936.2, CDKN2A-DT, AC090587.1, KLF3-AS1, AL133395.1, and LINC01063) were identified to construct the independent prognostic predictor for HNSCC. The area under the curve and C-index values obtained using the risk model were higher than the values corresponding to the clinical factors. Analysis of Kaplan-Meier curves indicated that the OS, PFS, and DSS time recorded for the patients in the low-risk group were higher than the corresponding values recorded for the patients belonging to the high-risk group. By functional enrichment analysis, we observed that differentially expressed genes were enriched in the immune response and tumor-associated pathways. The patients characterized by a low-risk score exhibited better immune cell infiltration than the patients belonging to the other group. We also observed that the sensitivity of the individuals belonging to the low-risk group to chemotherapeutic agents (cisplatin, docetaxel, and paclitaxel) was higher than the sensitivity of those in the other group.

CONCLUSIONS

A cuproptosis-related lncRNA-based signature that functioned as an independent prognosis predictor for HNSCC patients was constructed. The chemosensitivity of individual patients can be potentially predicted using this signature.

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

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

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

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

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

A prognostic exosome-related LncRNA risk model correlates with the immune microenvironment in liver cancer

Background: Emerging studies have shown the important roles of long noncoding RNAs (lncRNAs) in the occurrence and development of liver cancer. However, the exosome-related lncRNA signature in liver cancer remains to be clarified.

Methods: We obtained 371 tumor specimens and 50 normal tissues from the TCGA database. These samples were randomly divided into the training queue and verification queue. The exosome-related lncRNA risk model was verified by correlation analysis, Lasso regression analysis, and Cox regression analysis. The differences in the immune microenvironment in the two risk groups were obtained by analyzing the infiltration of different immune cells.

Results: Five exosome-related lncRNAs associated (MKLN1-AS, TMCC1-AS1, AL031985.3, LINC01138, AC099850.3) with a poor prognosis were identified and used to construct the signature. Receiver operating curve (ROC) and survival curves were used to confirm the predictive ability of this signature. Based on multivariate regression analysis in the training cohort (HR: 3.033, 95% CI: 1.762–5.220) and validation cohort (HR: 1.998, 95% CI: 1.065–3.751), the risk score was found to be an independent risk factor for patient prognosis. Subsequently, a nomogram was constructed to predict the 1-, 3-, 5-years survival rates of liver cancer patients. Moreover, this signature was also related to overexpressed immune checkpoints (PD-1, B7-H3, VSIR, PD-L1, LAG3, TIGIT and CTLA4).

Conclusion: Our study showed that exosome-related lncRNAs and the corresponding nomogram could be used as a better index to predict the outcome and immune regulation of liver cancer patients. This signature might provide a new idea for the immunotherapy of liver cancer in the future.

Long non-coding RNA LINC00152 in cancer: Roles, mechanisms, and chemotherapy and radiotherapy resistance

Long non-coding RNA LINC00152 (cytoskeleton regulator, or LINC00152) is an 828-bp lncRNA located on chromosome 2p11.2. LINC00152 was originally discovered during research on hepatocarcinogenesis and has since been regarded as a crucial oncogene that regulates gene expression in many cancer types. LINC00152 is aberrantly expressed in various cancers, including gastric, breast, ovarian, colorectal, hepatocellular, and lung cancer, and glioma. Several studies have indicated that LINC00152 is correlated with cell proliferation, apoptosis, migration, invasion, cell cycle, epithelial-mesenchymal transition (EMT), chemotherapy and radiotherapy resistance, and tumor growth and metastasis. High LINC00152 expression in most tumors is significantly associated with poor patient prognosis. Mechanistic analysis has demonstrated that LINC00152 can serve as a competing endogenous RNA (ceRNA) by sponging miRNA, regulating the abundance of the protein encoded by a particular gene, or modulating gene expression at the epigenetic level. LINC00152 can serve as a diagnostic or prognostic biomarker, as well as a therapeutic target for most cancer types. In the present review, we discuss the roles and mechanisms of LINC00152 in human cancer, focusing on its functions in chemotherapy and radiotherapy resistance.

Construction of a Necroptosis-Associated Long Non-Coding RNA Signature to Predict Prognosis and Immune Response in Hepatocellular Carcinoma

Background: Necroptosis is a form of programmed cell death, and studies have shown that long non-coding RNA molecules (lncRNAs) can regulate the process of necroptosis in various cancers. We sought to screen lncRNAs associated with necroptosis to predict prognosis and tumor immune infiltration status in patients with hepatocellular carcinoma (HCC).

Methods: Transcriptomic data from HCC tumor samples and normal tissues were extracted from The Cancer Genome Atlas database. Necroptosis-associated lncRNAs were obtained by co-expression analysis. Necroptosis-associated lncRNAs were then screened by Cox regression and least absolute shrinkage and selection operator methods to construct a risk model for HCC. The models were also validated and evaluated by Kaplan-Meier analysis, univariate and multivariate Cox regression, and time-dependent receiver operating characteristic (ROC) curves. In addition, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment, gene set enrichment, principal component, immune correlation, and drug sensitivity analyses were applied to assess model risk groups. To further differentiate the immune microenvironment of different HCC subtypes, the entire dataset was divided into three clusters, based on necroptosis-associated lncRNAs, and a series of analyses performed.

Results: We constructed a model comprising four necroptosis-associated lncRNAs: POLH-AS1, DUXAP8, AC131009.1, and TMCC1-AS1. Overall survival (OS) duration was significantly longer in patients classified as low-risk than those who were high-risk, according to our model. Univariate and multivariate Cox regression analyses further confirmed risk score stability. The analyzed models had area under the ROC curve values of 0.786, 0.713, and 0.639 for prediction of 1-, 3-, and 5-year OS, respectively, and risk score was significantly associated with immune cell infiltration and ESTIMATE score. In addition, differences between high and low-risk groups in predicted half-maximal inhibitory concentration values for some targeted and chemical drugs, providing a potential basis for selection of treatment approach. Finally, cluster analysis facilitated more refined differentiation of the immune microenvironment in patients with HCC and may allow prediction of the effectiveness of immune checkpoint inhibitors.

Conclusions: This study contributes to understanding of the function of necroptosis-related lncRNAs in predicting the prognosis and immune infiltration status of HCC. The risk model constructed and cluster analysis provide a basis for predicting the prognosis of patients with HCC and to inform the selection of immunotherapeutic strategies.

Long non-coding RNA muskelin 1 antisense RNA as a potential therapeutic target in hepatocellular carcinoma treatment

Long non-coding RNAs are essential to hepatocellular carcinoma (HCC) development, progression, and incidence of drug resistance. However, the biological significance of long non-coding RNA muskelin 1 antisense RNA (MKLN1-AS) remains poorly characterized. In this study, we observed noticeable increased levels of MKLN1-AS in HCC tissues. This upregulation of MKLN1-AS was clinically associated with vascular invasion and decreased disease-free survival and overall survival of patients with HCC. Functionally, MKLN1-AS-knockdown dramatically suppressed the metastasis and growth of HCC cells in vitro and in vivo. Additionally, the knockdown of MKLN1-AS augmented the pro-apoptosis effect of lenvatinib. Taken together, our findings indicate that MKLN1-AS may be exploited as a potential prognostic predictor and therapeutic target for HCC treatment.

Pyroptosis-Related LncRNA Signature Predicts Prognosis and Is Associated With Immune Infiltration in Hepatocellular Carcinoma

Pyroptosis is an inflammatory form of programmed cell death that is involved in various cancers, including hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) were recently verified as crucial mediators in the regulation of pyroptosis. However, the role of pyroptosis-related lncRNAs in HCC and their associations with prognosis have not been reported. In this study, we constructed a prognostic signature based on pyroptosis-related differentially expressed lncRNAs in HCC. A co-expression network of pyroptosis-related mRNAs-lncRNAs was constructed based on HCC data from The Cancer Genome Atlas. Cox regression analyses were performed to construct a pyroptosis-related lncRNA signature (PRlncSig) in a training cohort, which was subsequently validated in a testing cohort and a combination of the two cohorts. Kaplan-Meier analyses revealed that patients in the high-risk group had poorer survival times. Receiver operating characteristic curve and principal component analyses further verified the accuracy of the PRlncSig model. Besides, the external cohort validation confirmed the robustness of PRlncSig. Furthermore, a nomogram based on the PRlncSig score and clinical characteristics was established and shown to have robust prediction ability. In addition, gene set enrichment analysis revealed that the RNA degradation, the cell cycle, the WNT signaling pathway, and numerous immune processes were significantly enriched in the high-risk group compared to the low-risk group. Moreover, the immune cell subpopulations, the expression of immune checkpoint genes, and response to chemotherapy and immunotherapy differed significantly between the high- and low-risk groups. Finally, the expression levels of the five lncRNAs in the signature were validated by quantitative real-time PCR. In summary, our PRlncSig model shows significant predictive value with respect to prognosis of HCC patients and could provide clinical guidance for individualized immunotherapy.

Construction and Validation of a Ferroptosis-Related lncRNA Signature as a Novel Biomarker for Prognosis, Immunotherapy and Targeted Therapy in Hepatocellular Carcinoma

Recently, immunotherapy combined with targeted therapy has significantly prolonged the survival time and improved the quality of life of patients with hepatocellular carcinoma (HCC). However, HCC treatment remains challenging due to the high heterogeneity of this malignancy. Sorafenib, the first-line drug for the treatment of HCC, can inhibit the progression of HCC by inducing ferroptosis. Ferroptosis is associated with the formation of an immunosuppressive microenvironment in tumours. Moreover, long non-coding RNAs (lncRNAs) are strongly associated with ferroptosis and the progression of HCC. Discovery of ferroptosis-related lncRNAs (FR-lncRNAs) is critical for predicting prognosis and the effectiveness of immunotherapy and targeted therapies to improve the quality and duration of survival of HCC patients. Herein, all cases from The Cancer Genome Atlas (TCGA) database were divided into training and testing groups at a 6:4 ratio to construct and validate the lncRNA signatures. Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression analyses were used to screen the six FR-lncRNAs (including MKLN1-AS, LINC01224, LNCSRLR, LINC01063, PRRT3-AS1, and POLH-AS1). Kaplan–Meier (K–M) and receiver operating characteristic (ROC) curve analyses demonstrated the optimal predictive prognostic ability of the signature. Furthermore, a nomogram indicated favourable discrimination and consistency. For further validation, we used real-time quantitative polymerase chain reaction (qRT-PCR) to analyse the expression of LNCSRLR, LINC01063, PRRT3-AS1, and POLH-AS1 in HCC tissues. Moreover, we determined the ability of the signature to predict the effects of immunotherapy and targeted therapy in patients with HCC. Gene set enrichment analysis (GSEA) and somatic mutation analysis showed that ferroptosis-related pathways, immune-related pathways, and TP53 mutations may be strongly associated with the overall survival (OS) outcomes of HCC patients. Overall, our study suggests that a new risk model of six FR-lncRNAs has a significant prognostic value for HCC and that it could contribute to precise and individualised HCC treatment.

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.

Immunoautophagy-Related Long Noncoding RNA (IAR-lncRNA) Signature Predicts Survival in Hepatocellular Carcinoma

BACKGROUND

The dysregulation of autophagy and immunological processes has been linked to various pathophysiological conditions, including cancer. Most notably, their particular involvement in hepatocellular carcinoma (HCC) is becoming increasingly evident. This has led to the possibility of developing a prognostic signature based on immuno-autophagy-related (IAR) genes. Given that long non-coding RNAs (lncRNAs) also play a special role in HCC, a combined signature utilizing IAR genes and HCC-associated long noncoding RNAs (as IARlncRNA) may potentially help in the clinical scenario.

METHOD

We used Pearson correlation analysis, Kaplan-Meier survival curves, univariate and multivariate Cox regression, and ROC curves to generate and validate a prognostic immuno-autophagy-related long non-coding RNA (IARlncRNA) signature. The Chi-squared test was utilized to investigate the correlation between the obtained signature and the clinical characteristics. CIBERSORT algorithms and the Wilcoxon rank sum test were applied to investigate the correlation between signature and infiltrating immune cells. GO and KEGG analyses were performed to derived signature-dependent pathways.

RESULTS

Herein, we build an IAR-lncRNA signature (as first in the literature) and demonstrate its prognostic ability in hepatocellular carcinoma. Primarily, we identified three IARlncRNAs (MIR210HG, AC099850.3 and CYTOR) as unfavorable prognostic determinants. The obtained signature predicted the high-risk HCC group with shorter overall survival, and was further associated with clinical characteristics such as tumor grade (t = 10.918, p = 0.001). Additionally, several infiltrating immune cells showed varied fractions between the low-risk group and the high-risk HCC groups in association with the obtained signature. In addition, pathways analysis described by the signature clearly distinguishes both risk groups in HCC.

CONCLUSIONS

The immuno-autophagy-related long non-coding RNA (IARlncRNA) signature we established exhibits a prognostic ability in hepatocellular carcinoma. To our knowledge, this is the first attempt in the literature to combine three determinants (immune, autophagy and LnRNAs), thus requiring molecular validation of this obtained signature in clinical samples.

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.

A Novel Autophagy-Related Long Non-Coding RNA Signature to Predict Prognosis and Therapeutic Response in Esophageal Squamous Cell Carcinoma

Background

Considering the significance of autophagy and long non-coding RNAs (lncRNAs) in the biology of esophageal squamous cell carcinoma (ESCC), the present study aimed to identify a new autophagy-related lncRNA signature to forecast the clinical outcomes of ESCC patients and to guide individualized treatment.

Methods

The expression profiles were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. We extracted autophagy-related genes from the Human Autophagy Database and identified autophagy-related lncRNAs through Spearman correlation analysis. Univariate, least absolute shrinkage and selection operator and multivariate Cox regression analyses were performed on GSE53625 to construct an autophagy-related lncRNAs prognostic signature. The model was subjected to bootstrap internal validation, and the expression levels of lncRNAs were verified by TCGA database. The potential molecular mechanism of the model was explored by gene set enrichment analysis (GSEA). Spearman correlation coefficient examined the correlation between risk score and ferroptosis-associated genes as well as the response to immunotherapy and chemotherapy.

Results

We identified and validated an autophagy-related lncRNAs prognostic signature in 179 patients with ESCC. The prognosis of patients in the low-risk group was significantly better than that in the high-risk group (p-value <0.001). The reliability of the model was verified by Brier score and ROC. GSEA results showed significant enrichment of cancer- and autophagy-related signaling pathways in the high-risk group and metabolism-related pathways in the low-risk group. Correlation analysis indicated that the model can effectively forecast the effect of immunotherapy and chemotherapy. About 35.41% (74/209) ferroptosis-related genes were significantly correlated with risk scores.

Conclusion

In brief, we constructed a novel autophagy-related lncRNAs signature (LINC02024, LINC01711, LINC01419, LCAL1, FENDRR, ADAMTS9-AS1, AC025244.1, AC015908.6 and AC011997.1), which could improve the prediction of clinical outcomes and guide individualized treatment of ESCC patients.

Identification of Mutator-Derived lncRNA Signatures of Genomic Instability for Promoting the Clinical Outcome in Hepatocellular Carcinoma

BACKGROUND

Accumulating evidence proves that long noncoding RNA (lncRNA) plays a crucial role in maintaining genomic instability. However, it is significantly absent from exploring genomic instability-associated lncRNAs and discovering their clinical significance.

OBJECTIVE

To identify crucial mutator-derived lncRNAs and construct a predictive model for prognosis and genomic instability in hepatocellular carcinoma.

METHODS

First, we constructed a mutator hypothesis-derived calculative framework through uniting the lncRNA expression level and somatic mutation number to screen for genomic instability-associated lncRNA in hepatocellular carcinoma. We then selected mutator-derived lncRNA from the genome instability-associated lncRNA by univariate Cox analysis and Lasso regression analysis. Next, we created a prognosis model with the mutator-derived lncRNA signature. Furthermore, we verified the vital role of the model in the prognosis and genomic instability of hepatocellular carcinoma patients. Finally, we examined the potential relationship between the model and the mutation status of TP53.

RESULTS

In this study, we screened 88 genome instability-associated lncRNAs and built a prognosis model with four mutator-derived lncRNAs. Moreover, the model was an independent predictor of prognosis and an accurate indicator of genomic instability in hepatocellular carcinoma. Finally, the model could catch the TP53 mutation status, and the model was a more effective indicator than the mutation status of TP53 for hepatocellular carcinoma patients.

CONCLUSION

This research adopted a reliable method to analyze the role of lncRNA in genomic instability. Besides, the prognostic model with four mutator-derived lncRNAs is an excellent new indicator of prognosis and genomic instability in hepatocellular carcinoma. In addition, this finding may help clinicians develop therapeutic systems.

Macrophages M1-Related Prognostic Signature in Hepatocellular Carcinoma

A large number of studies have found that macrophages M1 play an important role in the occurrence and development of tumors. The aim of our study is to explore the causes of differential infiltration of macrophages M1 in hepatocellular carcinoma from the perspective of transcriptome and establish a prognostic model of hepatocellular carcinoma. We downloaded gene expression and clinical data from the public database, estimated the content of macrophages M1 in different samples with R software, and found the different genes between high- and low-infiltration groups. Using differentially expressed genes, we constructed a model composed of 7 genes. The risk score of the model has a good ability to predict the prognosis, has a positive correlation with immune checkpoints, and is closely related to other immune cells and immune function. Our model shows good prognostic function and has wide application value.

Identification and characterization of a 25-lncRNA prognostic signature for early recurrence in hepatocellular carcinoma

Background

Early recurrence is the major cause of poor prognosis in hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) are deeply involved in HCC prognosis. In this study, we aimed to establish a prognostic lncRNA signature for HCC early recurrence.

Methods

The lncRNA expression profile and corresponding clinical data were retrieved from total 299 HCC patients in TCGA database. LncRNA candidates correlated to early recurrence were selected by differentially expressed gene (DEG), univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. A 25-lncRNA prognostic signature was constructed according to receiver operating characteristic curve (ROC). Kaplan-Meier and multivariate Cox regression analyses were used to evaluate the performance of this signature. ROC and nomogram were used to evaluate the integrated models based on this signature with other independent clinical risk factors. Gene set enrichment analysis (GSEA) was used to reveal enriched gene sets in the high-risk group. Tumor infiltrating lymphocytes (TILs) levels were analyzed with single sample Gene Set Enrichment Analysis (ssGSEA). Immune therapy response prediction was performed with TIDE and SubMap. Chemotherapeutic response prediction was conducted by using Genomics of Drug Sensitivity in Cancer (GDSC) pharmacogenomics database.

Results

Compared to low-risk group, patients in high-risk group showed reduced disease-free survival (DFS) in the training (p < 0.0001) and validation cohort (p = 0.0132). The 25-lncRNA signature, AFP, TNM and vascular invasion could serve as independent risk factors for HCC early recurrence. Among them, the 25-lncRNA signature had the best predictive performance, and combination of those four risk factors further improves the prognostic potential. Moreover, GSEA showed significant enrichment of “E2F TARGETS”, “G2M CHECKPOINT”, “MYC TARGETS V1” and “DNA REPAIR” pathways in the high-risk group. In addition, increased TILs were observed in the low-risk group compared to the high-risk group. The 25-lncRNA signature negatively associates with the levels of some types of antitumor immune cells. Immunotherapies and chemotherapies prediction revealed differential responses to PD-1 inhibitor and several chemotherapeutic drugs in the low- and high-risk group.

Conclusions

Our study proposed a 25-lncRNA prognostic signature for predicting HCC early recurrence, which may guide postoperative treatment and recurrence surveillance in HCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08827-z.

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.

The prognostic value of an autophagy-related lncRNA signature in hepatocellular carcinoma

Background

lncRNA may be involved in the occurrence, metastasis, and chemical reaction of hepatocellular carcinoma (HCC) through various pathways associated with autophagy. Therefore, it is urgent to reveal more autophagy-related lncRNAs, explore these lncRNAs’ clinical significance, and find new targeted treatment strategies.

Methods

The corresponding data of HCC patients and autophagy genes were obtained from the TCGA database, and the human autophagy database respectively. Based on the co-expression and Cox regression analysis to construct prognostic prediction signature.

Results

Finally, a signature containing seven autophagy-related lncRNAs (PRRT3-AS1, RP11-479G22.8, RP11-73M18.8, LINC01138, CTD-2510F5.4, CTC-297N7.9, RP11-324I22.4) was constructed. Based on the risk score of signature, Overall survival (OS) curves show that the OS of high-risk patients is significantly lower than that of low-risk patients (P = 2.292e−10), and the prognostic prediction accuracy of risk score (AUC = 0.786) is significantly higher than that of ALBI (0.532), child_pugh (0.573), AFP (0.5751), and AJCC_stage (0.631). Moreover, multivariate Cox analysis and Nomogram of risk score are indicated that the 1-year and 3-year survival rates of patients are obviously accuracy by the combined analysis of the risk score, child_pugh, age, M_stage, and Grade (The AUC of 1- and 3-years are 0.87, and 0.855). Remarkably, the 7 autophagy-related lncRNAs may participate in Spliceosome, Cell cycle, RNA transport, DNA replication, and mRNA surveillance pathway and be related to the biological process of RNA splicing and mRNA splicing.

Conclusion

In conclusion, the 7 autophagy-related lncRNAs might be promising prognostic and therapeutic targets for HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04123-6.