The Pyroptosis-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Immunotherapeutic Efficiency in Hepatocellular Carcinoma

Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach

Long non-coding RNAs (lncRNAs), which are RNA sequences greater than 200 nucleotides in length, play a crucial role in regulating gene expression and biological processes associated with cancer development and progression. Liver cancer is a major cause of cancer-related mortality worldwide, notably in Thailand. Although machine learning has been extensively used in analyzing RNA-sequencing data for advanced knowledge, the identification of potential lncRNA biomarkers for cancer, particularly focusing on lncRNAs as molecular biomarkers in liver cancer, remains comparatively limited. In this study, our objective was to identify candidate lncRNAs in liver cancer. We employed an expression data set of lncRNAs from patients with liver cancer, which comprised 40 699 lncRNAs sourced from The CancerLivER database. Various feature selection methods and machine-learning approaches were used to identify these candidate lncRNAs. The results showed that the random forest algorithm could predict lncRNAs using features extracted from the database, which achieved an area under the curve (AUC) of 0.840 for classifying lncRNAs between early (stage 1) and late stages (stages 2, 3, and 4) of liver cancer. Five of 23 significant lncRNAs (WAC-AS1, MAPKAPK5-AS1, ARRDC1-AS1, AC133528.2, and RP11-1094M14.11) were differentially expressed between early and late stage of liver cancer. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, higher expression of WAC-AS1, MAPKAPK5-AS1, and ARRDC1-AS1 was associated with shorter overall survival. In conclusion, the classification model could predict the early and late stages of liver cancer using the signature expression of lncRNA genes. The identified lncRNAs might be used as early diagnostic and prognostic biomarkers for patients with liver cancer.

A pyroptosis-related lncRNA-based prognostic index for hepatocellular carcinoma by relative expression orderings

Background

Hepatocellular carcinoma (HCC) is an invasive malignant tumor, and pyroptosis makes an important contribution to the pathology and progression of liver cancer. Many prognostic models have been proposed for HCC based on the quantitative expression level of candidate genes, which are unsuitable for clinical application due to their vulnerability against experimental batch effects. The aim of this study was to develop a novel pyroptosis-related long non-coding RNA (lncRNA)-based prognostic index (PLPI) for HCC based on relative expression orderings (REOs).

Methods

Firstly, the pyroptosis-related lncRNAs were identified through the Wilcoxon rank-sum test and gene co-expression analyses. Then, the novel prognostic model PLPI was constructed by pyroptosis-related lncRNA pairs, which were identified by multiple machine learning algorithms. Gene set enrichment, somatic mutation, and drug sensitivity analyses were conducted to measure the differences between high- and low-risk patients. Multiple immune analyses were used to explore the association between PLPI and the immunological microenvironment.

Results

In this study, a novel prognostic model PLPI based on 10 pyroptosis-related lncRNA pairs was constructed, which was proven to be an independent prognostic risk factor. The receiver operating characteristic (ROC) curves showed that the model had a good prognostic ability in the training, testing, and external set, respectively [5-year area under the curve (AUC) =0.73, 5-year AUC =0.81, 4-year AUC =0.79]. The results of survival, somatic mutation, and immune analyses showed that the patients in the low-risk group had a better prognosis, lower rates of somatic mutation, and better immune cell infiltration. Personalized chemotherapeutic drugs were also identified for the patients with HCC.

Conclusions

The novel PLPI not only greatly predicted the prognosis of patients with HCC but could also offer novel ideas and approaches for the therapeutic management of HCC.

A programmed cell death-related gene signature to predict prognosis and therapeutic responses in liver hepatocellular carcinoma

BACKGROUND

Programmed cell death (PCD) functions critically in cancers and PCD-related genes are associated with tumor microenvironment (TME), prognosis and therapeutic responses of cancer patients. This study stratified hepatocellular carcinoma (HCC) patients and develop a prognostic model for predicting prognosis and therapeutic responses.

METHODS

Consensus clustering analysis was performed to subtype HCC patients in The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) among the subtypes were filtered and subjected to the least absolute shrinkage and selection operator (LASSO) regression analysis and univariate Cox regression analysis to filter prognostic genes. A PCD-related prognostic gene signature in TCGA was constructed and validated in ICGC-LIRI-JP and GSE14520 datasets. TME was analyzed using CIBERSORT, MCP-counter, TIMER and EPIC algorithms. Drug sensitivity was predicted by oncoPredict package. Spearman analysis was used to detect correlation.

RESULTS

Four molecular subtypes were categorized based on PCD-related genes. Subtype C1 showed the poorest prognosis, the most infiltration of Fibroblasts, dentritic cell (DC) and cancer-associated fibroblasts (CAFs), and the highest TIDE score. C4 had a better prognosis survival outcome, and lowest immune cell infiltration. The survival outcomes of C2 and C3 were intermediate. Next, a total of 69 co-DEGs were screened among the four subtypes and subsequently we identified five prognostic genes (MCM2, SPP1, S100A9, MSC and EPO) for developing the prognostic model. High-risk patients not only had unfavorable prognosis, higher clinical stage and grade, and more inflammatory pathway enrichment, but also possessed higher possibility of immune escape and were more sensitive to Cisplatin and 5. Fluorouracil. The robustness of the prognostic model was validated in external datasets.

CONCLUSION

This study provides new insights into clinical subtyping and the PCD-related prognostic signature may serve as a useful tool to predict prognosis and guide treatments for patients with HCC.

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.

Clinical Significance of Non-Coding RNA Regulation of Programmed Cell Death in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a widely prevalent and malignantly progressive tumor. Most patients are typically diagnosed with HCC at an advanced stage, posing significant challenges in the execution of curative surgical interventions. Non-coding RNAs (ncRNAs) represent a distinct category of RNA molecules not directly involved in protein synthesis. However, they possess the remarkable ability to regulate gene expression, thereby exerting significant regulatory control over cellular processes. Notably, ncRNAs have been implicated in the modulation of programmed cell death (PCD), a crucial mechanism that various therapeutic agents target in the fight against HCC. This review summarizes the clinical significance of ncRNA regulation of PCD in HCC, including patient diagnosis, prognosis, drug resistance, and side effects. The aim of this study is to provide new insights and directions for the diagnosis and drug treatment strategies of HCC.

Identification of a novel lncRNA prognostic signature and analysis of functional lncRNA AC115619.1 in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is the deadliest malignancy. Long non-coding RNAs (lncRNAs) are involved in the development of multiple human malignancies. This study aimed to establish a reliable signature and identify novel biomarkers for HCC patients. Methods: Differentially expressed lncRNAs (DElncRNAs) were identified from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Univariate, LASSO, and multivariate Cox regression analyses were applied to screen the prognostic lncRNAs and establish a prognostic model. Receiver operating characteristic (ROC) curves and Kaplan-Meier analyses were conducted to validate the prognostic value of this model. The association between lncRNAs and differential m6A genes was analyzed by Spearman's analysis. A series of bioinformatic and in vitro experiments were applied to explore the function of hub lncRNA. Results: A total of 32 DElncRNAs were identified, and 12 DElncRNAs were associated with the prognosis of HCC patients. A prognostic signature comprising six prognostic lncRNAs (LINC02428, LINC02163, AC008549.1, AC115619.1, CASC9, and LINC02362) was constructed, and the model exhibited an excellent capacity for prognosis prediction. Furthermore, 12 differential m6A regulators were identified, and RBMX was found to be correlated negatively with the hub lncRNA AC115619.1. The expression level of AC115619.1 was lower in HCC tissues than that in normal tissues and was significantly related to clinicopathologic features, survival rate, and drug sensitivity. Overexpression of AC115619.1 notably inhibited the proliferation, migration, and invasion of HCC cells. Conclusion: This study provided a promising prognostic signature for HCC patients and identified AC115619.1 as a novel biomarker, which plays an essential role in regulating the progression of HCC.

Identification of an immune-related 6-lncRNA panel with a good performance for prognostic prediction in hepatocellular carcinoma by integrated bioinformatics analysis

Hepatocellular carcinoma (HCC) is one of the most malignant tumors with a poor prognosis. The long non-coding RNA (lncRNA) has been found to have great potential as a prognostic biomarker or therapeutic target for cancer patients. However, the prognostic value and tumor immune infiltration of lncRNAs in HCC has yet to be fully elucidated. To identify prognostic biomarkers of lncRNA in HCC by integrated bioinformatics analysis and explore their functions and relationship with tumor immune infiltration. The prognostic risk assessment model for HCC was constructed by comprehensively using univariate/multivariate Cox regression analysis, Kaplan-Meier survival analysis, and the least absolute shrinkage and selection operator regression analysis. Subsequently, the accuracy, independence, and sensitivity of our model were evaluated, and a nomogram for individual prediction in the clinic was constructed. Tumor immune microenvironment (TIME), immune checkpoints, and human leukocyte antigen alleles were compared in high- and low-risk patients. Finally, the functions of our lncRNA signature were examined using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and gene set enrichment analysis. A 6-lncRNA panel of HCC consisting of RHPN1-AS1, LINC01224, CTD-2510F5.4, RP1-228H13.5, LINC01011, and RP11-324I22.4 was eventually identified, and show good performance in predicting the survivals of patients with HCC and distinguishing the immunomodulation of TIME of high- and low-risk patients. Functional analysis also suggested that this 6-lncRNA panel may play an essential role in promoting tumor progression and immune regulation of TIME. In this study, 6 potential lncRNAs were identified as the prognostic biomarkers in HCC, and the regulatory mechanisms involved in HCC were initially explored.

LncRNAs and regulated cell death in tumor cells

Regulated Cell Death (RCD) is a mode of cell death that occurs through drug or genetic intervention. The regulation of RCDs is one of the significant reasons for the long survival time of tumor cells and poor prognosis of patients. Long non-coding RNAs (lncRNAs) which are involved in the regulation of tumor biological processes, including RCDs occurring on tumor cells, are closely related to tumor progression. In this review, we describe the mechanisms of eight different RCDs which contain apoptosis, necroptosis, pyroptosis, NETosis, entosis, ferroptosis, autosis and cuproptosis. Meanwhile, their respective roles in the tumor are aggregated. In addition, we outline the literature that is related to the regulatory relationships between lncRNAs and RCDs in tumor cells, which is expected to provide new ideas for tumor diagnosis and treatment.

Targeted Pyroptosis Is a Potential Therapeutic Strategy for Cancer

As a type of regulated cell death (RCD) mode, pyroptosis plays an important role in several kinds of cancers. Pyroptosis is induced by different stimuli, whose pathways are divided into the canonical pathway and the noncanonical pathway depending on the formation of the inflammasomes. The canonical pathway is triggered by the assembly of inflammasomes, and the activation of caspase-1 and then the cleavage of effector protein gasdermin D (GSDMD) are promoted. While in the noncanonical pathway, the caspase-4/5/11 (caspase 4/5 in humans and caspase 11 in mice) directly cleave GSDMD without the assembly of inflammasomes. Pyroptosis is involved in various cancers, such as lung cancer, gastric cancer, hepatic carcinoma, breast cancer, and colorectal carcinoma. Pyroptosis in gastric cancer, hepatic carcinoma, breast cancer, and colorectal carcinoma is related to the canonical pathway, while both the canonical and noncanonical pathway participate in lung cancer. Moreover, simvastatin, metformin, and curcumin have effect on these cancers and simultaneously promote the pyroptosis of cancer cells. Accordingly, pyroptosis may be an important therapeutic target for cancer.

Molecular subtypes and a prognostic model for hepatocellular carcinoma based on immune- and immunogenic cell death-related lncRNAs

Background

Accumulating evidence shows that immunogenic cell death (ICD) enhances immunotherapy effectiveness. In this study, we aimed to develop a prognostic model combining ICD, immunity, and long non-coding RNA biomarkers for predicting hepatocellular carcinoma (HCC) outcomes.

Methods

Immune- and immunogenic cell death-related lncRNAs (IICDLs) were identified from The Cancer Genome Atlas and Ensembl databases. IICDLs were extracted based on the results of differential expression and univariate Cox analyses and used to generate molecular subtypes using ConsensusClusterPlus. We created a prognostic signature based on IICDLs and a nomogram based on risk scores. Clinical characteristics, immune landscapes, immune checkpoint blocking (ICB) responses, stemness, and chemotherapy responses were also analyzed for different molecular subtypes and risk groups.

Result

A total of 81 IICDLs were identified, 20 of which were significantly associated with overall survival (OS) in patients with HCC. Cluster analysis divided patients with HCC into two distinct molecular subtypes (C1 and C2), with patients in C1 having a shorter survival time than those in C2. Four IICDLs (TMEM220-AS1, LINC02362, LINC01554, and LINC02499) were selected to develop a prognostic model that was an independent prognostic factor of HCC outcomes. C1 and the high-risk group had worse OS (hazard ratio > 1.5, p < 0.01), higher T stage (p < 0.05), higher clinical stage (p < 0.05), higher pathological grade (p < 0.05), low immune cell infiltration (CD4⁺ T cells, B cells, macrophages, neutrophils, and myeloid dendritic cells), low immune checkpoint gene expression, poor response to ICB therapy, and high stemness. Different molecular subtypes and risk groups showed significantly different responses to several chemotherapy drugs, such as doxorubicin (p < 0.001), 5-fluorouracil (p < 0.001), gemcitabine (p < 0.001), and sorafenib (p < 0.01).

Conclusion

Our study identified molecular subtypes and a prognostic signature based on IICDLs that could help predict the clinical prognosis and treatment response in patients with HCC.

Development and validation of cuproptosis-related gene signature in the prognostic prediction of liver cancer

Liver cancer is a generic term referring to several cancer types arising from the liver. Every year, liver cancer causes lots of deaths and other burdens to the people all over the world. Though the techniques in the diagnosis and therapy of liver cancer have undergone significant advances, the current status of treating liver cancer is not satisfactory enough. The improvement of techniques for the prognosis of liver cancer patients will be a great supplement for the treatment of liver cancer. Cuproptosis is a newly identified regulatory cell death type, which may have a close connection to liver cancer pathology. Here, we developed a prognostic model for liver cancer based on the cuproptosis-related mRNAs and lncRNAs. This model can not only effectively predict the potential survival of liver cancer patients, but also be applied to evaluate the infiltration of immune cell, tumor mutation burden, and sensitivity to anti-tumor drugs in liver cancer. In addition, this model has been successfully validated in lots of liver cancer patients’ data. In summary, we wish this model can become a helpful tool for clinical use in the therapy of liver cancer.