Identification of a CD4+ conventional T cells-related lncRNAs signature associated with hepatocellular carcinoma prognosis, therapy, and tumor microenvironment

YTHDC1-Mediated lncRNA MSC-AS1 m6A Modification Potentiates Laryngeal Squamous Cell Carcinoma Development via Repressing ATXN7 Transcription

Laryngeal squamous cell carcinoma (LSCC) has the highest mortality rate among head and neck squamous cell carcinoma. This study was designed to investigate the biological effect of long noncoding RNA (lncRNA) MSC antisense RNA 1 (MSC-AS1) on LSCC development and the underlying mechanism. The expression and prognostic value of lncRNAs in head and neck squamous cell carcinoma were predicted in the bioinformatics tools. The overexpression of MSC-AS1 in LSCC patients predicted a poor prognosis. Depletion of MSC-AS1 using shRNA repressed the malignant phenotype of AMC-HN-8 and TU-177 cells. MSC-AS1, mainly localized in the nucleus, interacted closely with the transcription factor CCCTC-binding factor (CTCF). CTCF played anti-tumor effects in vitro and in vivo. Ataxin-7 (ATXN7) was predicted to be a downstream target of CTCF, whose expression was negatively controlled by MSC-AS1. MSC-AS1 was found to block the expression of CTCF, thereby repressing ATXN7. Finally, MSC-AS1 overexpression in LSCC was governed by YTH domain-containing protein 1 (YTHDC1)-mediated m6A modification. In summary, our research identified the YTHDC1/MSC-AS1/CTCF/ATXN7 axis in LSCC development, which indicated that MSC-AS1 is an attractive biomarker in the LSCC treatment.

Copper metabolism-related lncRNAs predict prognosis and immune landscape in liver cancer patients

BACKGROUND

Characterized by its high mortality and easy recurrence, hepatocellular carcinoma (HCC) poses significant clinical challenges. The association between copper metabolism and development of cancer has been identified. However, the underlying mechanisms of copper metabolism-related long non-coding RNAs (CMRLs) in HCC remain elusive. To address the gap, our study analyzed the prognostic and immuno-therapeutic value of CMRLs in HCC.

METHODS

This research utilized The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) data (n=424) for analysis, applying the "limma" package in R software for differential gene analysis and construction of a prognostic signature. We validated the signature using training and validation groups stochastically divided at a ratio of 1:1 and assessed prognostic value via Kaplan-Meier, C-index, and receiver operating characteristic (ROC) curves. By multivariate Cox regression, independent prognostic indicators were identified, and a nomogram was formulated for survival forecasting. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses elucidated biological pathways, and the immune landscape was examined through multiple algorithms. Finally, drug sensitivity was determined from Genomics of Drug Sensitivity in Cancer (GDSC), with mutation analysis conducted via maftools.

RESULTS

In this study, a predictive model based on four pivotal CMRLs (PRRT3-AS1, AC108752.1, AC092115.3, AL031985.3) significantly associated with HCC progression and prognosis was constructed and validated with the overall survival (OS) prediction area under the curve (AUC) values for 1, 3, and 5 years of 0.718, 0.688, and 0.669, respectively. The calibration curves and C-index values showed a solid prognostic ability of the nomogram. The high-risk group was notably higher than the low-risk group both in OS and tumor mutational burdens (TMBs). Moreover, functional annotation enrichment analysis of CMRLs revealed that the signature was mainly associated with mitotic function, chromosome, kinetochore, cell cycle, and oocyte meiosis. Furthermore, therapeutic drugs, including fluorouracil, afatinib, alpelisib, cedranib, crizotinib, erlotinib, gefitinib, and ipatasertib, were found to induce higher sensitivity in high-risk group.

CONCLUSIONS

The prognostic signature consisting of four CMRLs displays an outstanding predictive performance and improves the precision of immuno-oncology.

The role of long noncoding ribonucleic acids in the central nervous system injury

Central nervous system (CNS) injury involves complex pathophysiological molecular mechanisms. Long noncoding ribonucleic acids (lncRNAs) are an important form of RNA that do not encode proteins but take part in the regulation of gene expression and various biological processes. Multitudinous studies have evidenced lncRNAs to have a significant role in the process of progression and recovery of various CNS injuries. Herein, we review the latest findings pertaining to the role of lncRNAs in CNS, both normal and diseased state. We aim to present a comprehensive clinical application prospect of lncRNAs in CNS, and thus, discuss potential strategies of lncRNAs in treating CNS injury.

Anoikis-Related Long Non-Coding RNA Signatures to Predict Prognosis and Immune Infiltration of Gastric Cancer

Anoikis is a distinct type of programmed cell death and a unique mechanism for tumor progress. However, its exact function in gastric cancer (GC) remains unknown. This study aims to investigate the function of anoikis-related lncRNA (ar-lncRNA) in the prognosis of GC and its immunological infiltration. The ar-lncRNAs were derived from RNA sequencing data and associated clinical information obtained from The Cancer Genome Atlas. Pearson correlation analysis, differential screening, LASSO and Cox regression were utilized to identify the typical ar-lncRNAs with prognostic significance, and the corresponding risk model was constructed, respectively. Comprehensive methods were employed to assess the clinical characteristics of the prediction model, ensuring the accuracy of the prediction results. Further analysis was conducted on the relationship between immune microenvironment and risk features, and sensitivity predictions were made about anticancer medicines. A risk model was built according to seven selected ar-lncRNAs. The model was validated and the calibration plots were highly consistent in validating nomogram predictions. Further analyses revealed the great accuracy of the model and its ability to serve as a stand-alone GC prognostic factor. We subsequently disclosed that high-risk groups display significant enrichment in pathways related to tumors and the immune system. Additionally, in tumor immunoassays, notable variations in immune infiltrates and checkpoints were noted between different risk groups. This study proposes, for the first time, that prognostic signatures of ar-lncRNA can be established in GC. These signatures accurately predict the prognosis of GC and offer potential biomarkers, suggesting new avenues for basic research, prognosis prediction and personalized diagnosis and treatment of GC.

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.

Prediction Model for Therapeutic Responses in Ovarian Cancer Patients using Paclitaxel-resistant Immune-related lncRNAs

BACKGROUND

Ovarian cancer (OC) is the deadliest malignant tumor in women with a poor prognosis due to drug resistance and lack of prediction tools for therapeutic responses to anti- cancer drugs.

OBJECTIVE

The objective of this study was to launch a prediction model for therapeutic responses in OC patients.

METHODS

The RNA-seq technique was used to identify differentially expressed paclitaxel (PTX)- resistant lncRNAs (DE-lncRNAs). The Cancer Genome Atlas (TCGA)-OV and ImmPort database were used to obtain immune-related lncRNAs (ir-lncRNAs). Univariate, multivariate, and LASSO Cox regression analyses were performed to construct the prediction model. Kaplan- meier plotter, Principal Component Analysis (PCA), nomogram, immune function analysis, and therapeutic response were applied with Genomics of Drug Sensitivity in Cancer (GDSC), CIBERSORT, and TCGA databases. The biological functions were evaluated in the CCLE database and OC cells.

RESULTS

The RNA-seq defined 186 DE-lncRNAs between PTX-resistant A2780-PTX and PTXsensitive A2780 cells. Through the analysis of the TCGA-OV database, 225 ir-lncRNAs were identified. Analyzing 186 DE-lncRNAs and 225 ir-lncRNAs using univariate, multivariate, and LASSO Cox regression analyses, 9 PTX-resistant immune-related lncRNAs (DEir-lncRNAs) acted as biomarkers were discovered as potential biomarkers in the prediction model. Single-cell RNA sequencing (scRNA-seq) data of OC confirmed the relevance of DEir-lncRNAs in immune responsiveness. Patients with a low prediction score had a promising prognosis, whereas patients with a high prediction score were more prone to evade immunotherapy and chemotherapy and had poor prognosis.

CONCLUSION

The novel prediction model with 9 DEir-lncRNAs is a valuable tool for predicting immunotherapeutic and chemotherapeutic responses and prognosis of patients with OC.

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.

CD4+ conventional T cells-related genes signature is a prognostic indicator for ovarian cancer

Introduction

It is believed that ovarian cancer (OC) is the most deadly form of gynecological cancer despite its infrequent occurrence, which makes it one of the most salient public health concerns. Clinical and preclinical studies have revealed that intratumoral CD4+ T cells possess cytotoxic capabilities and were capable of directly killing cancer cells. This study aimed to identify the CD4+ conventional T cells-related genes (CD4TGs) with respect to the prognosis in OC.

Methods

We obtained the transcriptome and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD4TGs were first identified from single-cell datasets, then univariate Cox regression was used to screen prognosis-related genes, LASSO was conducted to remove genes with coefficient zero, and multivariate Cox regression was used to calculate riskscore and to construct the CD4TGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), decision curve analysis (DCA), nomogram, and calibration were made to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in risk groups were conducted to explore the tightly correlated pathways with the risk group. The role of riskscore has been further explored in the tumor microenvironment (TME), immunotherapy, and chemotherapy. A risk signature with 11 CD4TGs in OC was finally established in the TCGA database and furtherly validated in several GEO cohorts.

Results

High riskscore was significantly associated with a poorer prognosis and proven to be an independent prognostic biomarker by multivariate Cox regression. The 1-, 3-, and 5-year ROC values, DCA curve, nomogram, and calibration results confirmed the excellent prediction power of this model. Compared with the reported risk models, our model showed better performance. The patients were grouped into high-risk and low-risk subgroups according to the riskscore by the median value. The low-risk group patients tended to exhibit a higher immune infiltration, immune-related gene expression and were more sensitive to immunotherapy and chemotherapy.

Discussion

Collectively, our findings of the prognostic value of CD4TGs in prognosis and immune response, provided valuable insights into the molecular mechanisms and clinical management of OC.