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

An extensive overview of the role of lncRNAs generated from immune cells in the etiology of cancer

Long non-coding RNAs (lncRNAs) are involved in the control of critical tumor-suppressor and oncogenic pathways in cancer. These types of non-coding RNAs could affect both immune and cancer cells. The thorough analysis of lncRNAs derived from immune cells and the incorporation of new findings significantly advance our understanding of the complex role of lncRNAs in the context of cancer. This work highlights the promise of lncRNAs for translational therapeutic approaches while also establishing a solid foundation for comprehending the complex link between lncRNAs and cancer through a coherent narrative. The main findings of this article are that types of lncRNAs derived from immune cells, such as MM2P and MALAT1, can affect the behaviors of cancer cells, like invasion, angiogenesis, and proliferation. As research in this area grows, the therapeutic potential of targeting these lncRNAs offers promising opportunities for expanding our understanding of cancer biology and developing cutting-edge, precision-based therapies for cancer therapy.

Developing risk models and subtypes of autophagy-associated LncRNAs for enhanced prognostic prediction and precision in therapeutic approaches for liver cancer patients

Background

Limited research has been conducted on the influence of autophagy-associated long non-coding RNAs (ARLncRNAs) on the prognosis of hepatocellular carcinoma (HCC).

Methods

We analyzed 371 HCC samples from TCGA, identifying expression networks of ARLncRNAs using autophagy-related genes. Screening for prognostically relevant ARLncRNAs involved univariate Cox regression, Lasso regression, and multivariate Cox regression. A Nomogram was further employed to assess the reliability of Riskscore, calculated from the signatures of screened ARLncRNAs, in predicting outcomes. Additionally, we compared drug sensitivities in patient groups with differing risk levels and investigated potential biological pathways through enrichment analysis, using consensus clustering to identify subgroups related to ARLncRNAs.

Results

The screening process identified 27 ARLncRNAs, with 13 being associated with HCC prognosis. Consequently, a set of signatures comprising 8 ARLncRNAs was successfully constructed as independent prognostic factors for HCC. Patients in the high-risk group showed very poor prognoses in most clinical categories. The Riskscore was closely related to immune cell scores, such as macrophages, and the DEGs between different groups were implicated in metabolism, cell cycle, and mitotic processes. Notably, high-risk group patients demonstrated a significantly lower IC50 for Paclitaxel, suggesting that Paclitaxel could be an ideal treatment for those at elevated risk for HCC. We further identified C2 as the Paclitaxel subtype, where patients exhibited higher Riskscores, reduced survival rates, and more severe clinical progression.

Conclusion

The 8 signatures based on ARLncRNAs present novel targets for prognostic prediction in HCC. The drug candidate Paclitaxel may effectively treat HCC by impacting ARLncRNAs expression. With the identification of ARLncRNAs-related isoforms, these results provide valuable insights for clinical exploration of autophagy mechanisms in HCC pathogenesis and offer potential avenues for precision medicine.

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.

Disulfidptosis-Associated lncRNAs are Potential Biomarkers for Predicting Immune Response and Prognosis Within Individuals Diagnosed with Hepatocellular Carcinoma

Purpose

Hepatocellular carcinoma (HCC) is a prevalent form of cancer that is distributed globally. Disulfidptosis, characterized by the fragility of the actin cytoskeleton, represents a distinct type of cell death and holds promise for novel cancer therapies. Nevertheless, the connection among disulfidptosis-associated long non-coding RNAs (lncRNAs) and HCC is still unexplored. This study uses an in silico approach to provide the novel biomarkers of disulfidptosis-associated lncRNAs for predicting the immune response and prognosis with HCC.

Methods

In order to address this gap, we integrated transcriptomic data of HCC from The Cancer Genome Atlas (TCGA) and identified genes that exhibit differential expression with disulfidptosis and lncRNAs. Through co-expression analysis, we identified disulfidptosis-related lncRNAs. Afterwards, by employing univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO), a model for disulfidptosis-associated lncRNA was constructed. The risk model underwent assessment through the utilization of diverse analytical methodologies, including functional enrichment annotation, Kaplan-Meier analysis, principal component analysis (PCA), immune infiltration and immune status analysis, as well as tumor mutation analysis. Furthermore, we discussed the implications of the model in predicting drug sensitivity.

Results

Our study culminated in the construction of a disulfidptosis-related lncRNA model comprising four prognostic disulfidptosis-related lncRNAs (ACYTOR, NRAV, AL080248.1, and AC069307.1). This model demonstrates exceptional diagnostic value for HCC patients and holds practical implications for guiding clinicians in personalizing immunotherapy and drug selection based on individual variations.

Conclusion

In summary, our research introduces a novel predictive tool utilizing disulfidptosis-related lncRNAs, offering potential guidance for the therapeutic management of HCC.

An update on methods for detection of prognostic and predictive biomarkers in melanoma

The approval of immunotherapy for stage II-IV melanoma has underscored the need for improved immune-based predictive and prognostic biomarkers. For resectable stage II-III patients, adjuvant immunotherapy has proven clinical benefit, yet many patients experience significant adverse events and may not require therapy. In the metastatic setting, single agent immunotherapy cures many patients but, in some cases, more intensive combination therapies against specific molecular targets are required. Therefore, the establishment of additional biomarkers to determine a patient's disease outcome (i.e., prognostic) or response to treatment (i.e., predictive) is of utmost importance. Multiple methods ranging from gene expression profiling of bulk tissue, to spatial transcriptomics of single cells and artificial intelligence-based image analysis have been utilized to better characterize the immune microenvironment in melanoma to provide novel predictive and prognostic biomarkers. In this review, we will highlight the different techniques currently under investigation for the detection of prognostic and predictive immune biomarkers in melanoma.

A Novel Gene Pair CSTF2/DPE2A Impacts Prognosis and Cell Cycle of Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC), one of the commonest cancers at present, possesses elevated mortality. This study explored the predictive value of CSTF2/PDE2A for HCC prognosis.

Methods

In this study, clinical information and RNA sequencing expression profiles of HCC patients were acquired from common databases. Kaplan-Meier curve compound with time-dependent ROC curve, nomogram model, and univariate/multivariate Cox analysis were carried out to access the prediction capacity of CSTF2/PDE2A. The immune status, tumor microenvironment, drug sensitivity, biological function and pathway between HCC and adjacent non-tumorous tissue were analyzed and compared. Finally, RT-qPCR, Western blot, and apoptosis assays were performed to verify the effect on HCC cells of CSTF2/PDE2A.

Results

The optimal cut-off value of CSTF2, PDE2A and CSTF2/PDE2A was 6.95, 0.95 and 3.63, respectively. In TCGA and ICGC cohorts, the high group of CSTF2/PDE2A presented higher OS compared to low group. The area under the curve (AUC) for OS at 1-, 2-, and 3-years predicted by CSTF2/PDE2A were 0.731/0.695, 0.713/0.732 and 0.689/0.755, higher than the counterparts of the single gene CSTF2 and PDE2A. Multivariate Cox analysis revealed that CSTF2/PDE2A (HR = 1.860/3.236, 95% CI = 1.265-2.733/1.575-6.645) was an independent prognostic factor for HCC. The OS nomogram model created according to five independent factors including CSTF2/PDE2A showed excellent capacity for HCC prognosis. Furthermore, the immune status of the CSTF2/PDE2A high group was deleted, cell cycle-related genes and chemotherapy resistance were increased. Finally, cell experiments revealed distinct differences in the proliferation, apoptosis, protein and mRNA expression of HCC cells after si-CSTF2 transfection compared with the negative control.

Conclusion

Taken together, the gene pair CSTF2/PDE2A is able to forecast the prognosis of HCC and regulates cell cycle, which is promising as a novel prognostic predictor of HCC.

A new prediction model of hepatocellular carcinoma based on N7-methylguanosine modification

PURPOSE

Hepatocellular carcinoma (HCC) is a kind of primary liver cancer. It is a common malignant tumor of digestive system that is difficult to predict the prognosis of patients. As an important epigenetic modification, N7 methyl guanosine (m7G) is indispensable in gene regulation. This regulation may affect the development and occurrence of cancer. However, the prognosis of long non coding RNAs (lncRNAs) in HCC is limited, especially how m7G-related lncRNAs regulate the development of HCC has not been reported.

METHODS

The Cancer Genome Atlas (TCGA) provides us with the expression data and corresponding clinical information of HCC patients we need. We used a series of statistical methods to screen four kinds of m7G-related lncRNAs related to HCC prognosis and through a series of verifications, the results were in line with our expectations. Finally, we also explored the IC50 difference and correlation analysis of various common chemotherapy drugs.

RESULT

Our study identified four differentially expressed m7g-related lncRNAs associated with HCC prognosis. Survival curve analysis showed that high risk lncRNAs would lead to poor prognosis of HCC patients. M7G signature's AUC was 0.789, which shows that the prognosis model we studied has certain significance in predicting the prognosis of HCC patients. Moreover, our study found that different risk groups have different immune and tumor related pathways through gene set enrichment analysis. In addition, many immune cell functions are significantly different among different risk groups, such as T cell functions, including coordination of type I INF response and coordination of type II INF response. The expression of PDCD1, HHLA2, CTLA-4 and many other immune checkpoints in different risk groups is also different. Additionally, we analyzed the differences of IC50 and risk correlation of 15 chemotherapeutic drugs among different risk groups.

CONCLUSION

A novel lncRNAs associated with m7G predicts the prognosis of HCC.

Construction of a prognostic model for HCC based on ferroptosis-related lncRNAs expression and its potential to predict the response and irAEs of immunotherapy

Background: Ferroptosis is an iron-dependent programmed cell death process, and studies have confirmed that it plays an important regulatory role in the occurrence and development of various malignancies including hepatocellular carcinoma (HCC). In addition, the role of abnormally expressed long non-coding RNAs (lncRNAs) in regulating and driving the occurrence and development of HCC has attracted more and more attention. However, there is still a lack of research on the role of ferroptosis-related lncRNAs in the prognosis prediction of HCC patients.

Method: In this study, we used the Pearson test method to analyze the association between differentially expressed lncRNAs and ferroptosis-related genes in HCC and normal tissues obtained from The Cancer Genome Atlas (TCGA), and found 68 aberrantly expressed and prognosis-related ferroptosis-related lncRNAs. Based on this, we established an HCC prognostic model composed of 12 ferroptosis-related lncRNAs. In addition, HCC patients were divided into a high-risk group and a low-risk group according to the risk score of this 12 ferroptosis-related lncRNAs prognostic model. Gene enrichment analysis indicated that ferroptosis-related lncRNA-based expression signatures may regulate HCC immune microenvironment signaling pathways through ferroptosis, chemical carcinogenesis-reactive oxygen species, and NK cell-mediated cytotoxicity pathways. In addition, immune cell correlation analysis showed that there were significant differences in immune infiltrating cell subtypes, such as Th cells, macrophages, monocytes, and Treg cells between the two groups. In addition, the expression of multiple immune checkpoint molecules was found to be significantly increased in the high-risk group (eg, PD1, CTLA-4, CD86, etc.).

Results: Our research provides a new method for predicting prognosis using a ferroptosis-related lncRNA expression signature prognostic model in hepatocellular carcinoma. And it provides new tools for predicting patient response and adverse effects of immunotherapy.

Conclusion: In conclusion, ferroptosis-related lncRNA expression signatures can be used to construct a prognostic prediction model to predict the overall survival of HCC patients, and can be used as an independent influencing factor for prognosis. Further analysis showed that ferroptosis-related lncRNAs may affect the efficacy of immunotherapy in patients with HCC by altering the tumor microenvironment, so this model may serve as a new indicator of the response and irAEs of HCC to immunotherapy.

Identification and Characterization of an Ageing-Associated 13-lncRNA Signature That Predicts Prognosis and Immunotherapy in Hepatocellular Carcinoma

Background

In cancer pathology, cell senescence not only alters cell function but also reshapes the immune microenvironments in tumours. However, the association between cell senescence, tumour microenvironment, and disease progression of hepatocellular carcinoma (HCC) is yet to be fully understood. Therefore, the role of cell senescence-related genes and long noncoding RNAs (lncRNAs) in evaluating the clinical prognosis and immune cell infiltration (ICI) of HCC patients requires further investigation.

Methods

The limma R package was utilised to investigate differentially expressed genes according to the multiomics data. The CIBERSORT R package was utilised to assess ICI, and unsupervised cluster analysis was conducted using the R software's ConsensusClusterPlus package. A polygenic prognostic model of lncRNAs was constructed by conducting univariate and least absolute shrinkage and selection operator (Lasso) cox proportional-hazards regression analyses. The time-dependent receiver operating characteristic (ROC) curves were used for validation. We utilised the survminer R package to evaluate the tumour mutational burden (TMB). Moreover, the gene set enrichment analysis (GSEA) helped in pathway enrichment analysis, and the immune infiltration level of the model was evaluated using the IMvigor210 cohort.

Results

The identification of 36 prognosis-related genes was achieved based on their differential expression between healthy and liver cancer tissues. Liver cancer individuals were categorised into 3 independent senescence subtypes using the gene list, revealing considerable survival differences (variations). We observed that the prognosis of patients in the ARG-ST2 subtype was substantially better as compared to that in the ARG-ST3 subtype. Differences were observed in gene expression profiles among the three subtypes, with the differentially expressed genes predominantly associated with cell cycle control. The enrichment of upregulated genes in the ARG-ST3 subtype was observed in pathways related to biological processes, for instance, organelle fission, nuclear division, and chromosome recombination. ICI in the ARG-ST1 and ARG-ST2 subtypes, with relatively better prognosis, was substantially higher as compared to the ARG-ST3 subtype. Furthermore, a risk-score model, which can be employed as a reliable prognostic factor in an independent manner for individuals suffering from liver cancer, was constructed based on 13 cell senescence-related lncRNAs (MIR99AHG, LINC01224, LINC01138, SLC25A30AS1, AC006369.2, SOCS2AS1, LINC01063, AC006037.2, USP2AS1, FGF14AS2, LINC01116, KIF25AS1, and AC002511.2). The individuals with higher risk scores had noticeably poor prognoses in contrast with those having low-risk scores. Moreover, increased levels of TMB and ICI were observed in individuals with low-risk scores and gaining more benefit from immune checkpoint therapy.

Conclusion

Cell senescence is an essential factor in HCC onset and progression. We identified 13 senescence-related lncRNAs as HCC prognostic markers, which can help understand their function in the onset and progression of HCC and guide clinical diagnosis and treatment.

Comprehensive analysis of cuproptosis-related lncRNAs in immune infiltration and prognosis in hepatocellular carcinoma

BACKGROUND

Being among the most common malignancies worldwide, hepatocellular carcinoma (HCC) accounting for the third cause of cancer mortality. The regulation of cell death is the most crucial step in tumor progression and has become a crucial target for nearly all therapeutic options. Cuproptosis, a copper-induced cell death, was recently reported in Science. However, its primary function in carcinogenesis is still unclear.

METHODS

Cuproptosis-related lncRNAs significantly associated with overall survival (OS) were screened by stepwise univariate Cox regression. The signature of cuproptosis-related lncRNAs for HCC prognosis was constructed by the LASSO algorithm and multivariate Cox regression. Further Kaplan-Meier analysis, proportional hazards model, and ROC analysis were performed. Functional annotation was performed using gene set enrichment analysis (GSEA). The relationship between prognostic cuproptosis-related lncRNAs and HCC prognosis was further explored by GEPIA( http://gepia.cancer-pku.cn/ ) online analysis tool. Finally, we used the ESTIMATE and XCELL algorithms to estimate stromal and immune cells in tumor tissue and cast each sample to infer the underlying mechanism of cuproptosis-related lncRNAs in the tumor immune microenvironment (TIME) of HCC patients.

RESULTS

Four cuproptosis-related lncRNAs were used to construct a prognostic lncRNA signature, which was an independent factor in predicting OS in HCC patients. Kaplan-Meier curves showed significant differences in survival rates between risk subgroups (p = 0.002). At the same time, we found that the expression levels of most immune checkpoint genes increased with increasing risk scores. Tumorigenesis and immunological-related pathways were primarily enhanced in the high-risk group, as determined by GSEA. The results of drug sensitivity analysis showed that compared with patients in the high-risk group, the IC50 values of erlotinib and lapatinib were lower in patients in the low-risk group, while the opposite was true for sunitinib, paclitaxel, gemcitabine, and imatinib. We also found that elevated AL133243.2 expression was significantly associated with worse OS and disease-free survival (DFS), more advanced T stage and higher tumor grade, and reduced immune cell infiltration, suggesting that HCC patients with low AL133243.2 expression in tumor tissues may have a better response to immunotherapy.

CONCLUSION

Collectively, the cuproptosis-associated lncRNA signature can serve as an independent predictor to guide individual treatment strategies. Furthermore, AL133243.2 is a promising marker for predicting immunotherapy response in HCC patients. This data may facilitate further exploration of more effective immunotherapy strategies for HCC.

LncRNA THAP9-AS1 highly expressed in tissues of hepatocellular carcinoma and accelerates tumor cell proliferation

BACKGROUND

Long noncoding RNAs (lncRNAs) are emerging as significant regulators of cancer development. The purposes of study were to analyze the expression levels of long noncoding RNA THAP9-AS1 (THAP9-AS1) in hepatocellular carcinoma (HCC) tissue samples and cell lines, evaluate the clinical significance of THAP9-AS1 in predicting the survival prognosis of HCC patients, and explore the biological function of THAP9-AS1 in regulating tumor progression of HCC.

METHODS

The expression of THAP9-AS1 was determined by quantitative real-time PCR. The determination of HCC cell proliferation was performed using cell counting kit-8 assay. Chi-square test was used to reveal the relationship between THAP9-AS1 and clinicopathological data of HCC patients. Kaplan-Meier method, log-rank test were used to perform analyze the relationship between THAP9-AS1 and prognostic survival in HCC patients. Cox regression was used to evaluate the abilities of THAP9-AS1 to predict survival outcomes in HCC patients.

RESULTS

The expression of THAP9-AS1 were markedly upregulated in HCC tissue and cells. THAP9-AS1 expression was correlated with tumor size, tumor node metastasis (TNM) stage. High THAP9-AS1 expression was associated with poor prognostic survival in HCC patients. THAP9-AS1 was an independent prognostic factor for HCC patients. Overexpression of THAP9-AS1 promoted HCC cell proliferation, silencing THAP9-AS1 inhibited HCC cell proliferation.

CONCLUSION

Aberrantly highly expressed THAP9-AS1 in HCC tissues and cells was associated with tumor size, TNM stage and poor survival prognosis, and promoted HCC cell proliferation. THAP9-AS1 had the potential to serve as independent prognostic biomarker for HCC patients and provide a novel target for HCC patients' prognostic treatment.

Cuproptosis-related lncRNAs predict the clinical outcome and immune characteristics of hepatocellular carcinoma

Cuproptosis, as a novel copper-dependent and non-apoptotic form of cell death, is induced by aggregation of lipoylated mitochondrial proteins and the instability of Fe-S cluster proteins. However, the role of cuproptosis-related long noncoding RNAs (CRLncRNAs) in hepatocellular carcinoma (HCC) has not been clearly elucidated. In this study, we identified and characterized cuproptosis-related lncRNAs in HCC. 343 HCC cases from The Cancer Genome Atlas (TCGA) with gene transcriptome data and clinical data were obtained for analysis after the screening. Univariate and multivariate Cox proportional hazards analyses were performed to establish a prognostic cuproptosis-related lncRNA signature (CRlncSig). We established a prognosis-related model consisting of nine cuproptosis-related lncRNAs: GSEC, AL158166.1, AC005479.2, AL365361.1, AC026412.3, AL031985.3, LINC00426, AC009974.2, AC245060.7, which was validated in the internal cohort. High-risk group stratified by the CRlncSig was significantly related to poor prognosis (p < 0.001). The area under the receiver operating characteristic curve (AUC) of 1 year, 3 years, and 5 years of survival were 0.813, 0.789, and 0.752, respectively. Furthermore, a prognostic nomogram including CRlncSig with clinicopathologic factors was built with favorable predictive power. In addition, GO and KEGG enrichment analysis suggested that CRlncSig was involved in many carcinogenesis and immune-related pathways. Additionally, we found that tumor microenvironment, immune infiltration, immune function, and drug response were significantly different between the high-risk and low-risk groups based on the risk model. These results highlight the value of cuproptosis-related lncRNAs on prognosis for HCC patients and provide insight into molecular and immune features underlying cuproptosis-related lncRNAs, which might play an important role in patient management and immunotherapy.

The integration of single-cell sequencing, TCGA, and GEO data analysis revealed that PRRT3-AS1 is a biomarker and therapeutic target of SKCM

INTRODUCTION

Skin cutaneous melanoma (SKCM) is the world's fourth deadliest cancer, and advanced SKCM leads to a poor prognosis. Novel biomarkers for SKCM diagnosis and prognosis are urgently needed. Long non-coding RNAs (lncRNAs) provide various biological functions and have been proved to play a significant role in tumor progression. Single-cell RNA sequencing (scRNA-seq) enables genome analysis at the single-cell level. This study explored prognostic lncRNAs in SKCM based on scRNA-seq and bulk RNA sequencing data.

MATERIALS AND METHODS

The TCGA cohort and melanoma samples in the GEO database (GSE72056, GSE19234, GSE15605, GSE7553, and GSE81383) were included in this study. Marker genes were filtered, and ensemble lncRNAs were annotated. The clinical significance of selected lncRNAs was verified through TCGA and GEO dataset analysis. SiRNA transfection, wound-healing and transwell assays were performed to evaluate the effect of PRRT3-AS1 on cellular function. Immune infiltration of the selected lncRNAs was also exhibited.

RESULTS

A 5-marker-lncRNAs model of significant prognostic value was constructed based on GSE72056 and the TCGA cohort. PRRT3-AS1 combined with DANCR was then found to provide significant prognostic value in SKCM. PRRT3-AS1 was filtered for its higher expression in more advanced melanoma and significant prognosis value. Cellular function experiments in vitro revealed that PRRT3-AS1 may be required for cancer cell migration in SKCM. PRRT3-AS1 was found to be related to epithelial-mesenchymal transition (EMT) signaling pathways. DNA methylation of PRRT3-AS1 was negatively related to PRRT3-AS1 expression and showed significant prognosis value. In addition, PRRT3-AS1 may suppress immune infiltration and be involved in immunotherapy resistance.

CONCLUSION

PRRT3-AS1 may be a diagnostic and prognostic biomarker of SKCM.

Hypoxia-related LncRNAs signature predicts prognosis and is associated with immune infiltration and progress of head and neck squamous cell carcinoma

Background

Disclosing prognostic information is necessary to enable good treatment selection and improve patient outcomes. Previous studies suggest that hypoxia is associated with an adverse prognosis in patients with HNSCC and that long non-coding RNAs (lncRNAs) show functions in hypoxia-associated cancer biology. Nevertheless, the understanding of lncRNAs in hypoxia related HNSCC progression remains confusing.

Methods

Data were downloaded from TCGA and GEO database. Bioinformatic tools including R packages GEOquery, limma, pheatmap, ggplot2, clusterProfiler, survivalROC and survcomp and LASSO cox analysis were utilized. Si-RNA transfection, CCK8 and real-time quantified PCR were used in functional study.

Results

GEO data (GSE182734) revealed that lncRNA regulation may be important in hypoxia related response of HNSCC cell lines. Further analysis in TCGA data identified 314 HRLs via coexpression analysis between differentially expressed lncRNAs and hypoxia-related mRNAs. 23 HRLs were selected to build the prognosis predicting model using lasso Cox regression analyses. Our model showed excellent performance in predicting survival outcomes among patients with HNSCC in both the training and validation sets. We also found that the risk scores were related to tumor stage and to tumor immune infiltration. Moreover, LINC01116 were selected as a functional study target. The knockdown of LINC01116 significantly inhibited the proliferation of HNSCC cells and effected the hypoxia induced immune and the NF-κB/AKT signaling.

Conclusions

Data analysis of large cohorts and functional experimental validation in our study suggest that hypoxia related lncRNAs play an important role in the progression of HNSCC, and its expression model can be used for prognostic prediction.

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NcRNAs regulations showed significance in hypoxia related response in HNSCC.

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314 lncRNAs coexpressed with hypoxia marker genes were identified as HRLs.

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An effective HRLs prognosis prediction model had been constructed and validated.

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Immune cells and pathways paly roles in hypoxia related progress of HNSCC.

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LINC01116 regulates HNSCC through hypoxia related immune and NF-κB/AKT signaling.

A novel Cuproptosis-related LncRNA signature to predict prognosis in hepatocellular carcinoma

Increased intracellular toxicity due to an imbalance in copper homeostasis caused by copper ion accumulation could regulate the rate of cancer cell growth and proliferation. The goal of this study was to create a novel Cuproptosis-related lncRNA signature that may be utilized to predict survival and immunotherapy in HCC patients. Cuproptosis-associated lncRNAs and differentially expressed lncRNAs between HCC tumor tissue and normal tissue were discovered first. By LASSO-Cox analysis, the overlapping lncRNAs were then utilized to build a Cuproptosis-associated lncRNA signature, which might be used to predict patient prognosis and responsiveness to immune checkpoint blockade (ICB) therapy. Differences in the infiltration of immune cell subpopulations between high and low-risk score subgroups were also analyzed. Moreover, a nomogram based on the Cuproptosis-associated lncRNA signature and clinical features was developed and demonstrated to have good predictive potential. Finally, qRT-PCR was performed in HerpG2 and MHCC-97H cell lines to explore whether these lncRNAs were indeed involved in the process of Cuproptosis. In summary, we created a prognostic lncRNA profile linked to Cuproptosis to forecast response to immunotherapy, which may provide a new potential non-apoptotic therapeutic perspective for HCC patients.

Prediction of Prognosis and Molecular Mechanism of Ferroptosis in Hepatocellular Carcinoma Based on Bioinformatics Methods

Background

As an iron-dependent type of programmed cell death, ferroptosis plays an important role in the pathogenesis and progression of hepatocellular carcinoma (HCC). Long noncoding RNAs (lncRNAs) have been linked to the prognosis of patients with HCC in a number of studies. Nevertheless, the predictive value of lncRNAs (FRLs) associated with ferroptosis in HCC has not been fully elucidated.

Methods

Download RNA sequencing data and clinical profiles of HCC patients from The Cancer Genome Atlas (TCGA) database. The FRLs associated with prognosis were determined by Pearson's correlation analysis. After that, prognostic signature for FRLs was established using Cox and LASSO regression analyses. Meanwhile, survival analysis, correlation analysis of clinicopathological features, Cox regression, receiver operating characteristic (ROC) curve, and nomogram were used to analyze the FRL signature's predictive capacity. The relationship between signature risk score, immune cell infiltration, and chemotherapy drug sensitivity is further studied.

Results

In total, 93 FRLs were found to be of prognostic value in patients with HCC. A five-FRL signature comprising AC015908.3, LINC01138, AC009283.1, Z83851.1, and LUCAT1 was created in order to enhance the prognosis prediction with HCC patients. The signature demonstrated a good predictive potency, according to the Kaplan-Meier and ROC curves. The five-FRL signature was found to be a risk factor independent of various clinical factors using Cox regression and stratified survival analysis. The high-risk group was shown to be enriched in tumorigenesis and immune-related pathways according to GSEA analysis. Additionally, immune cell infiltration, immune checkpoint molecules, and half-inhibitory concentrations differed considerably between risk groups, implying that this signature could be used to evaluate the clinical efficacy of chemotherapy and immunotherapy.

Conclusion

The five-FRL risk signature is helpful for assessing the prognosis of HCC patients and improving therapy options, so it can be further applied clinically.

Construction of a Novel LncRNA Signature Related to Genomic Instability to Predict the Prognosis and Immune Activity of Patients With Hepatocellular Carcinoma

Background

Genomic instability (GI) plays a crucial role in the development of various cancers including hepatocellular carcinoma. Hence, it is meaningful for us to use long non-coding RNAs related to genomic instability to construct a prognostic signature for patients with HCC.

Methods

Combining the lncRNA expression profiles and somatic mutation profiles in The Cancer Genome Atlas database, we identified GI-related lncRNAs (GILncRNAs) and obtained the prognosis-related GILncRNAs through univariate regression analysis. These lncRNAs obtained risk coefficients through multivariate regression analysis for constructing GI-associated lncRNA signature (GILncSig). ROC curves were used to evaluate signature performance. The International Cancer Genomics Consortium (ICGC) cohort, and in vitro experiments were used for signature external validation. Immunotherapy efficacy, tumor microenvironments, the half-maximal inhibitory concentration (IC50), and immune infiltration were compared between the high- and low-risk groups with TIDE, ESTIMATE, pRRophetic, and ssGSEA program.

Results

Five GILncRNAs were used to construct a GILncSig. It was confirmed that the GILncSig has good prognostic evaluation performance for patients with HCC by drawing a time-dependent ROC curve. Patients were divided into high- and low-risk groups according to the GILncSig risk score. The prognosis of the low-risk group was significantly better than that of the high-risk group. Independent prognostic analysis showed that the GILncSig could independently predict the prognosis of patients with HCC. In addition, the GILncSig was correlated with the mutation rate of the HCC genome, indicating that it has the potential to measure the degree of genome instability. In GILncSig, LUCAT1 with the highest risk factor was further validated as a risk factor for HCC in vitro. The ESTIMATE analysis showed a significant difference in stromal scores and ESTIMATE scores between the two groups. Multiple immune checkpoints had higher expression levels in the high-risk group. The ssGSEA results showed higher levels of tumor-antagonizing immune cells in the low-risk group compared with the high-risk group. Finally, the GILncSig score was associated with chemotherapeutic drug sensitivity and immunotherapy efficacy of patients with HCC.

Conclusion

Our research indicates that GILncSig can be used for prognostic evaluation of patients with HCC and provide new insights for clinical decision-making and potential therapeutic strategies.

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.

Stage-Specific Non-Coding RNA Expression Patterns during In Vitro Human B Cell Differentiation into Antibody Secreting Plasma Cells

The differentiation of B cells into antibody secreting plasma cells (PCs) is governed by a strict regulatory network that results in expression of specific transcriptomes along the activation continuum. In vitro models yielding significant numbers of PCs phenotypically identical to the in vivo state enable investigation of pathways, metabolomes, and non-coding (ncRNAs) not previously identified. The objective of our study was to characterize ncRNA expression during human B cell activation and differentiation. To achieve this, we used an in vitro system and performed RNA-seq on resting and activated B cells and PCs. Characterization of coding gene transcripts, including immunoglobulin (Ig), validated our system and also demonstrated that memory B cells preferentially differentiated into PCs. Importantly, we identified more than 980 ncRNA transcripts that are differentially expressed across the stages of activation and differentiation, some of which are known to target transcription, proliferation, cytoskeletal, autophagy and proteasome pathways. Interestingly, ncRNAs located within Ig loci may be targeting both Ig and non-Ig-related transcripts. ncRNAs associated with B cell malignancies were also identified. Taken together, this system provides a platform to study the role of specific ncRNAs in B cell differentiation and altered expression of those ncRNAs involved in B cell malignancies.

Sortilin 1 regulates hepatocellular carcinoma progression by activating the PI3K/AKT signaling

Background

Sortilin 1 (SORT1) has been reported as an oncogene in several human tumors. Nonetheless, the biological functions of SORT1 in hepatocellular carcinoma (HCC) remain poorly understood.

Methods

Western blotting was employed for the determination of protein expression. Hepatocellular carcinoma cell viability, apoptosis, migration, and invasion were measured via CCK-8, flow cytometry, wound healing, and Transwell assays.

Results

Sortilin 1 was upregulated in HCC and closely associated with unsatisfactory outcomes of HCC patients. Furthermore, in vitro and in vivo assays revealed that SORT1 knockdown significantly diminished HCC cell proliferation and metastasis but accelerated HCC cell apoptosis; moreover, SORT1 depletion also restrained the growth of xenografted HCC tumors. Mechanistically, SORT1 activated PI3K/AKT signaling in HCC cells, thereby promoting the malignant behaviors of HCC cells.

Conclusion

This study demonstrated that SORT1 might promote HCC progression by activating PI3K/AKT signaling, indicating that SORT1 might be a promising target and biomarker for HCC treatment and prognosis.

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.

Comprehensive analysis of N6 -methyladenosine-related long non-coding RNAs for prognosis prediction in liver hepatocellular carcinoma

Background

Liver hepatocellular carcinoma (LIHC) is a lethal cancer. This study aimed to identify the N⁶‐methyladenosine (m⁶A)‐targeted long non‐coding RNA (lncRNA) related to LIHC prognosis and to develop an m⁶A‐targeted lncRNA model for prognosis prediction in LIHC.

Methods

The expression matrix of mRNA and lncRNA was obtained, and differentially expressed (DE) mRNAs and lncRNAs between tumor and normal samples were identified. Univariate Cox and pathway enrichment analyses were performed on the m⁶A‐targeted lncRNAs and the LIHC prognosis‐related m⁶A‐targeted lncRNAs. Prognostic analysis, immune infiltration, and gene DE analyses were performed on LIHC subgroups, which were obtained from unsupervised clustering analysis. Additionally, a multi‐factor Cox analysis was used to construct a prognostic risk model based on the lncRNAs from the LASSO Cox model. Univariate and multivariate Cox analyses were used to assess prognostic independence.

Results

A total of 5031 significant DEmRNAs and 292 significant DElncRNAs were screened, and 72 LIHC‐specific m⁶A‐targeted binding lncRNAs were screened. Moreover, a total of 29 LIHC prognosis‐related m⁶A‐targeted lncRNAs were obtained and enriched in cytoskeletal, spliceosome, and cell cycle pathways. An 11‐m⁶A‐lncRNA prognostic model was constructed and verified; the top 10 lncRNAs included LINC00152, RP6‐65G23.3, RP11‐620J15.3, RP11‐290F5.1, RP11‐147L13.13, RP11‐923I11.6, AC092171.4, KB‐1460A1.5, LINC00339, and RP11‐119D9.1. Additionally, the two LIHC subgroups, Cluster 1 and Cluster 2, showed significant differences in the immune microenvironment, m⁶A enzyme genes, and prognosis of LIHC.

Conclusion

The m⁶A‐lncRNA prognostic model accurately and effectively predicted the prognostic survival of LIHC. Immune cells, immune checkpoints (ICs), and m⁶A enzyme genes could act as novel therapeutic targets for LIHC.