Identification of a seven-long non-coding RNA signature associated with Jab1/CSN5 in predicting hepatocellular carcinoma

Long non-coding RNA KB-1460A1.5 promotes ferroptosis by inhibiting mTOR/SREBP-1/SCD1-mediated polyunsaturated fatty acid desaturation in glioma

Ferroptosis is a new form of regulated cell death caused by the iron-dependent peroxidation of phospholipids and is related to cell metabolism, redox homeostasis and various signalling pathways related to cancer. The long non-coding RNA (lncRNA) KB-1460A1.5 acts as a tumour suppressor gene to regulate tumour growth in gliomas, but its molecular network regulatory mechanism is still unclear. In this study, we found that KB-1460A1.5 can induce ferroptosis in glioma and enhance sensitivity to RSL3, a ferroptosis inducer. Tandem mass tag proteomics and nontargeted metabolomics suggest that KB-1460A1.5 affects polyunsaturated fatty acid metabolic processes. Gas chromatography-mass spectrometry-based medium- and long-chain fatty acid-targeted metabolomics confirmed that upregulation of KB-1460A1.5 decreased the levels of monounsaturated fatty acids, oleic acid (OA) and palmitoleic acid (PO) in glioma cells. The addition of OA and PO restored KB-1460A1.5-induced cellular ferroptosis. Molecularly, KB-1460A1.5 inhibited the mammalian target of rapamycin signalling pathway to suppress the expression of downstream sterol regulatory element-binding protein 1 (SREBP-1), thereby attenuating the stearoyl-CoA desaturase-1 (SCD1)-mediated desaturation of polyunsaturated fatty acids. Finally, an animal model of subcutaneous glioma confirmed that KB-1460A1.5 could inhibit tumour progression, SREBP-1/SCD1 expression and ferroptosis. In conclusion, increasing the expression level of KB-1460A1.5 in glioma can promote the induction of oxidative stress and ferroptosis in cancer cells through SREBP-1/SCD1-mediated adipogenesis, demonstrating therapeutic potential in preclinical models.

Machine learning integrations develop an antigen-presenting-cells and T-Cells-Infiltration derived LncRNA signature for improving clinical outcomes in hepatocellular carcinoma

As a highly heterogeneous cancer, the prognostic stratification and personalized management of hepatocellular carcinoma (HCC) are still challenging. Recently, Antigen-presenting-cells (APCs) and T-cells-infiltration (TCI) have been reported to be implicated in modifying immunology in HCC. Nevertheless, the clinical value of APCs and TCI-related long non-coding RNAs (LncRNAs) in the clinical outcomes and precision treatment of HCC is still obscure. In this study, a total of 805 HCC patients were enrolled from three public datasets and an external clinical cohort. 5 machine learning (ML) algorithms were transformed into 15 kinds of ML integrations, which was used to construct the preliminary APC-TCI related LncRNA signature (ATLS). According to the criterion with the largest average C-index in the validation sets, the optimal ML integration was selected to construct the optimal ATLS. By incorporating several vital clinical characteristics and molecular features for comparison, ATLS was demonstrated to have a relatively more significantly superior predictive capacity. Additionally, it was found that the patients with high ATLS score had dismal prognosis, relatively high frequency of tumor mutation, remarkable immune activation, high expression levels of T cell proliferation regulators and anti-PD-L1 response as well as extraordinary sensitivity to Oxaliplatin/Fluorouracil/Lenvatinib. In conclusion, ATLS may serve as a robust and powerful biomarker for improving the clinical outcomes and precision treatment of HCC.

Development and validation of a novel ferroptosis‑related lncRNA prognostic signature for pancreatic adenocarcinoma

Long non‑coding RNAs (lncRNAs) serve a pivotal role in the regulation of cancer cell ferroptosis. However, the prognostic value of ferroptosis‑related lncRNAs in pancreatic adenocarcinoma (PAAD) largely remains unclear. We aimed at constructing a lncRNA‑based signature to improve the prognosis prediction of PAAD. In the present study, the transcriptome profiling data and clinical information of patients with PAAD were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Gene Consortium (ICGC) databases. Univariate Cox regression analysis of the TCGA cohort demonstrated that 26 ferroptosis‑related lncRNAs had significant prognostic value for PAAD (all P<0.01). Least absolute shrinkage and selection operator regression and multivariate Cox proportional hazards regression analyses were performed to construct a prognostic ferroptosis‑related lncRNA signature (FRLS) comprising nine ferroptosis‑related lncRNAs. The efficacy of this FRLS was verified in the training (TCGA) and validation (ICGC) cohorts. Based on the risk model, high risk scores were significantly correlated with poor overall survival (OS) (hazard ratio, 1.314; 95% confidence interval, 1.218‑1.418; P<0.001). The receiver operating characteristic curves and principal component analysis further demonstrated the robust prognostic ability of the FRLS. Furthermore, a nomogram with favorable predictive efficacy for the prediction of OS was constructed based on the FRLS and clinical features. Gene set enrichment analysis demonstrated that the genes in the FRLS participated in a number of cancer‑associated immunoregulatory pathways. Importantly, it was demonstrated that immune infiltration and response to cancer immunotherapy differed significantly between the high and low‑risk groups according to the FRLS. In conclusion, the risk signature based on the FRLS has potential for the clinical prediction of prognosis and immunotherapy response in patients with PAAD.

Machine learning integrations for development of a T-cell-tolerance derived signature to improve the clinical outcomes and precision treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is characterized by high rates of recurrence and metastasis and poor prognosis. A recently discovered concept of T cell tolerance (TCT) has become an entirely new target of cancer immunotherapy. Unfortunately, the effect of TCT on the outcomes of HCC has not been explored. In this study, 7 public datasets and one external clinical cohort, including 1716 HCC patients were explored. Through WGCNA analysis and differential analysis, we explored the key TCT-related modulates. A total of 95 machine learning integrations across all validation cohorts were compared and the optimal method with the highest average C-index value was selected to construct the TCT derived signature (TCTS). In all independent clinical cohorts, TCTS showed accurate prediction of the prognosis, and was significantly correlated with clinical indicators and molecular features. Compared with 77 published gene signatures, the TCTS exhibited superior predictive performance. In the external clinical cohort, a novel nomogram (comprising TNM stage, Hepatitis B, Vascular invasion, Perineural invasion, AFP and TCTS) was constructed to test the clinical performance of TCTS. The results showed that the high TCTS scoring group showed dismal prognosis, improved sensitivity to oxaliplatin and good response to anti-PD-1/PD-L1 immunotherapy. Moreover, the low TCTS score group had few genomic alterations, low immune activation and low PD-1/PD-L1 expression levels. In conclusion, TCTS is an ideal biomarker for predicting the clinical outcomes and improving precision treatment of HCC.

(-)-Gossypol enhances the anticancer activity of epirubicin via downregulating survivin in hepatocellular carcinoma

Epirubicin (EPI)-based transarterial chemoembolization is an effective therapy for advanced hepatocellular carcinoma (HCC). However, EPI-induced survivin expression limits its tumor-killing potential in HCC. Interestingly, (-)-gossypol ((-)-Gsp), a male contraceptive, suppresses various malignancies. More importantly, (-)-Gsp also holds promise for enhancing the antitumor effects of chemotherapy in numerous cancer types. In the present study, we demonstrated for the first time that (-)-Gsp-sensitized EPI inhibited cell growth and induced apoptosis of HCC cells in vitro. Furthermore, (-)-Gsp sensitized EPI by attenuating the EPI-elevated survivin protein levels. Mechanistic studies showed that EPI stimulated survivin protein synthesis by promoting translation initiation, which was alleviated by (-)-Gsp mainly through suppressing the AKT-4EBP1/p70S6K-survivin and ERK-4EBP1-survivin pathways. HCC xenograft experiments in nude mice also showed that (-)-Gsp treatment acted synergistically with EPI to repress xenograft tumor growth. Overall, our proof-of-concept results may pave the way for novel strategies for the treatment of HCC based on the combination of EPI and (-)-Gsp.

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.

Long noncoding RNA KB-1460A1.5 inhibits glioma tumorigenesis via miR-130a-3p/TSC1/mTOR/YY1 feedback loop

Long noncoding RNAs (lncRNAs) have been shown to be closely related to cancer progression and therapy. However, the clinical significance of lncRNAs and the mechanisms by which they function in glioma are largely unknown. In this study, using online data sets combined with collected clinical glioma tissues, we determined that the lncRNA KB-1460A1.5 is downregulated and positively correlated with prognosis in glioma. Functional experiments showed that overexpression of KB-1460A1.5 inhibits glioma cell proliferation, migration and invasion in vitro and in vivo, while downregulation of KB-1460A1.5 has the opposite effects. Mechanistically, tandem mass tag (TMT)-based quantitative proteomic analysis revealed that KB-1460A1.5 preferentially affects the Akt/TSC1/mTOR pathway. KB-1460A1.5 was found to function as a competing endogenous RNA (ceRNA) to regulate the expression of TSC1, a key regulatory component of the mTOR pathway, by sponging miR-130a-3p in glioma cells. Furthermore, our data demonstrate that the mTOR pathway regulates the expression of the transcription factor Yin Yang 1 (YY1), which in turn binds directly to the KB-1460A1.5 promoter and affects the expression of KB-1460A1.5. Untargeted metabolomics and quantitative real-time PCR (qRT-PCR) analysis further confirmed the effects of KB-1460A1.5 on amino acid metabolism. In conclusion, this study revealed that lncRNA KB-1460A1.5 inhibits glioma tumorigenesis via miR-130a-3p/TSC1/mTOR/YY1 feedback loop.

A Radioresponse-Related lncRNA Biomarker Signature for Risk Classification and Prognosis Prediction in Non-Small-Cell Lung Cancer

Purpose

Radiotherapy resistance is now recognized as the major obstacle to the effective therapeutic management of non-small-cell lung cancer (NSCLC). As a single biomarker has limited effect in stratifying NSCLC patients, this research aimed to identify long non-coding RNAs (lncRNAs) correlated with radiotherapy response to ameliorate forecast of NSCLC prognosis.

Methods

In a cohort of NSCLC patients with radiotherapy history (n = 96) from TCGA, genetic data of lncRNA expression profiling were performed. To identify radioresponse-related lncRNA sets which dysregulated significantly between radiosensitive (RS) and radioresistant (RR) groups, differential expression analysis was carried out. Cox relative regression was implemented to set up a radioresponse-related risk model. Moreover, we adopted survival analysis to measure the predictive potentiality of the prognosis model.

Results

Four radioresponse-related lncRNAs (CASC19, LINC01977, LINC02471, and MAGI2-AS3) were screened to create a prognostic signature. Then, we described a lncRNA signature-based regulatory network and explored the correlation of the immune microenvironment and the signature. Additionally, in vitro assays uncovered inhibition of LINC01977 weakened radioresistance of NSCLC cells.

Conclusion

We provided a novel radioresponse-related lncRNAs signature with excellent clinical potency for an effective prognostic forecast of patients.