Establishment of a lncRNA-Based Prognostic Gene Signature Associated With Altered Immune Responses in HCC

A novel mitochondrial-related lncRNA signature mediated prediction of overall survival, immune landscape, and the chemotherapeutic outcomes for bladder cancer patients

OBJECTIVE

To develop a prognostic risk model for Bladder Cancer (BLCA) based on mitochondrial-related long non-coding RNAs (lncRNAs).

METHODS

Transcriptome and clinical data of BLCA patients were retrieved from the TCGA database. Mitochondrial-related lncRNAs with independent prognostic significance were screened to develop a prognostic risk model. Patients were categorized into high- and low-risk groups using the model. Various methods including Kaplan-Meier (KM) analysis, ROC curve analysis, Gene Set Enrichment Analysis (GSEA), immune analysis, and chemotherapy drug analysis were used to verify and evaluate the model.

RESULTS

A mitochondrial-associated lncRNA prognostic risk model with independent prognostic significance was developed. High-risk group (HRG) patients exhibited significantly shorter survival periods compared to low-risk group (LRG) patients (P < 0.01). The risk score from the model was an independent predictor of BLCA prognosis, correlating with tumor grade, pathological stage, and lymph node metastasis (P < 0.05). The HRG showed significant positive correlations with high expressions of immune checkpoints (CTLA4, LAG3, PD-1, TIGIT, PD-L1, PD-L2, and TIM-3) and lower IC50 for chemotherapy drugs (cisplatin, docetaxel, paclitaxel, methotrexate, and vinblastine) (P < 0.001).

CONCLUSIONS

The mitochondrial-related lncRNA-based prognostic risk model effectively predicts BLCA prognosis and can guide individualized treatment for BLCA patients.

LINC01134: a pivotal oncogene with promising predictive maker and therapeutic target in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) represents a leading and fatal malignancy within the gastrointestinal tract. Recent advancements highlight the pivotal role of long non-coding RNAs (lncRNAs) in diverse biological pathways and pathologies, particularly in tumorigenesis. LINC01134, a particular lncRNA, has attracted considerable attention due to its oncogenic potential in hepatoma. Current research underscores LINC01134's potential in augmenting the onset and progression of HCC, with notable implications in drug resistance. This review comprehensively explores the molecular functions and regulatory mechanisms of LINC01134 in HCC, offering a fresh perspective for therapeutic interventions. By delving into LINC01134's multifaceted roles, we aim to foster novel strategies in HCC management.

Molecular subgroup establishment and signature creation of lncRNAs associated with acetylation in lung adenocarcinoma

BACKGROUND

The significance of long non-coding RNAs (lncRNAs) as pivotal mediators of histone acetylation and their influential role in predicting the prognosis of lung adenocarcinoma (LUAD) has been increasingly recognized. However, there remains uncertainty regarding the potential utility of acetylation-related lncRNAs (ARLs) in prognosticating the overall survival (OS) of LUAD specimens.

METHODS

The RNA-Seq and clinical information were downloaded from The Cancer Genome Atlas (TCGA). Through the differential analysis, weighted correlation network analysis (WGCNA), Pearson correlation test and univariate Cox regression, we found out the prognosis associated ARLs and divided LUAD specimens into two molecular subclasses. The ARLs were employed to construct a unique signature through the implementation of the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Subsequently, the predictive performance was evaluated using ROC analysis and Kaplan-Meier survival curve analysis. Finally, ARL expression in LUAD was confirmed by quantitative real-time PCR (qRT-PCR).

RESULTS

We triumphantly built a ARLs prognostic model with excellent predictive accuracy for LUAD. Univariate and multivariate Cox analysis illustrated that risk model served as an independent predictor for influencing the overall survival OS of LUAD. Furthermore, a nomogram exhibited strong prognostic validity. Additionally, variations were observed among subgroups in the field of immunity, biological functions, drug sensitivity and gene mutations within the field.

CONCLUSIONS

Nine ARLs were identified as promising indicators of personalized prognosis and drug selection for people suffering with LUAD.

LncRNA CRNDE Drives the Progression of Hepatocellular Carcinoma by inducing the Immunosuppressive Niche

As a crucial protumorigenic long noncoding RNA, colorectal tumor differential expression (CRNDE) has been confirmed to facilitate the progression of various cancers. However, its role in the tumor microenvironment (TME) of hepatocellular carcinoma (HCC) is still unclear. Here we determined that CRNDE was upregulated in HCC samples and that CRNDE-positive cells were predominantly enriched in malignant tumor cells. In vivo functional assays revealed that CRNDE-induced tumor cells supported HCC progression, recruited abundant granulocyte myeloid-derived suppressor cells (G-MDSCs) and restricted the infiltration of T cells. In terms of mechanisms, CRNDE bound with Toll-like receptor 3 (TLR3) and activated NF-κB signaling to increase the secretion of c-x-c motif chemokine ligand 3 (CXCL3). CRNDE knockdown could significantly suppress the accumulation of G-MDSCs and enhance the infiltration of T cells in the TME of HCC in vivo. Taken together, our study reveals the CRNDE-NF-κB-CXCL3 axis plays a crucial role in driving the immunosuppressive niche to facilitate HCC progression by recruiting G-MDSCs.

Unlocking the predictive potential of long non-coding RNAs: a machine learning approach for precise cancer patient prognosis

The intricate web of cancer biology is governed by the active participation of long non-coding RNAs (lncRNAs), playing crucial roles in cancer cells' proliferation, migration, and drug resistance. Pioneering research driven by machine learning algorithms has unveiled the profound ability of specific combinations of lncRNAs to predict the prognosis of cancer patients. These findings highlight the transformative potential of lncRNAs as powerful therapeutic targets and prognostic markers. In this comprehensive review, we meticulously examined the landscape of lncRNAs in predicting the prognosis of the top five cancers and other malignancies, aiming to provide a compelling reference for future research endeavours. Leveraging the power of machine learning techniques, we explored the predictive capabilities of diverse lncRNA combinations, revealing their unprecedented potential to accurately determine patient outcomes.

Integrated analysis of tumour-derived exosome-related immune genes to predict progression and immune status of hepatocellular carcinoma

Tumour-derived exosomes (TDEs) play an important role in tumourigenesis and progression by regulating components in the tumour microenvironment (TME), however, the role of TDE-related immune genes in hepatocellular carcinoma is not fully known. We systematically analysed TDE genes from ExoCarta and immune genes from Immport,Machine learning ultimately identified eight TDE-related prognostic immune genes and used them as the basis for constructing a risk model, which was constructed to better predict patients with hepatocellular carcinoma (HCC) compared with published prognostic models. There were significant differences between the high and low risk groups in terms of biological functioning. Low-risk group were more sensitive to immunotherapy, the sensitivity to oxaliplatin and cisplatin differed between the high- and low-risk groups, and knockout of the core gene RAC1 limited the malignant biological behaviour of hepatocellular carcinoma cells. In conclusion, TIRGs are effective in predicting the prognosis of patients with hepatocellular carcinoma and provide a new perspective on immunotherapy and chemotherapy for patients.

Comprehensive analysis of prognostic value and immune infiltration of IAPs family in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality rates. The inhibitors of apoptosis (IAP) family act as oncogenes in various tumor types; however, their functions in HCC remain unclear. Here, we used integrated bioinformatics analysis and experimental verification to assess the expression and the prognostic and clinical value of the IAP family in HCC. Using the University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) and the Tumor Immune Estimation Resource (TIMER), we analyzed the expression profiles of IAP family members in HCC tissue, normal tissues, and in patients with different stages and grades of HCC. We further verified the expression level of BIRC2 in 25 HCC samples and matched adjacent normal tissues using quantitative real-time PCR (qRT-PCR), and analyzed its correlation with the marker gene of T-helper type 1 cells (Th1)-STAT1. Meanwhile, the association between BIRC2 and the immunotherapeutic response or immunomodulators was confirmed using the Biomarker Exploration of Solid Tumors (BEST) database. The results showed that NAIP, BIRC2, BIRC3, XIAP, BIRC5, and BIRC6 mRNAs were overexpressed in HCC. The clinical stages, pathological grades, and other clinicopathological features of HCC were closely related to the expression levels of the IAP family members, especially the BIRC2 and BIRC5, which were found to be potential prognostic biomarkers for HCC. Expression of the IAPs was strongly associated with immune cell infiltration. Based on the infiltrative status of various immune cells, HCC patients with high BIRC2 and BIRC5 expression demonstrated poor overall survival (OS) rates. In patients with HCC, BIRC2 expression was noticeably elevated. Concurrently, the expression levels of BIRC2 and STAT1 showed a favorable correlation. BEST database analysis revealed that BIRC2 was a negative predictor of responsiveness to anti-programmed cell death ligand 1 (PD-L1)/cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) inhibitor treatment in HCC, and BIRC2 mRNA expression levels were positively correlated with the expression levels of the immune checkpoint genes programmed cell death protein 1 (PD-1), PD-L1, and CTLA-4 in HCC. Consequently, the IAP family may play a role in carcinogenesis and cancer-immune system interactions in HCC. Our results demonstrate that IAP family members may be viable predictive biomarkers and therapeutic targets for HCC.

Development of a prognostic model for anoikis and identifies hub genes in hepatocellular carcinoma

Considering the high fatality of hepatocellular carcinoma (HCC), current prognostic systems are insufficient to accurately forecast HCC patients' outcomes. In our study, nine anoikis‑related genes (PTRH2, ITGAV, ANXA5, BIRC5, BDNF, BSG, DAP3, SKP2, and EGF) were determined to establish a risk scoring model using LASSO regression, which could be validated in ICGC dataset. Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curve analysis confirmed the risk score possessed an accurate predictive value for the prognosis of HCC patients. The high-risk group showed a higher infiltration of aDCs, macrophages, T-follicular helper cells, and Th2 cells. Besides, PD-L1 was significantly higher in the high-risk group compared to the low-risk group. Several anoikis‑related genes, such as ANX5, ITGAV, BDNF and SKP2, were associated with drug sensitivity in HCC. Finally, we identified BIRC5 and SKP2 as hub genes among the nine model genes using WGCNA analysis. BIRC5 and SKP2 were over-expressed in HCC tissues, and their over-expression was associated with poor prognosis, no matter in our cohort by immunohistochemical staining or in the TCGA cohort by mRNA-Seq. In our cohort, BIRC5 expression was highly associated with the T stage, pathologic stage, histologic grade and AFP of HCC patients. In general, our anoikis-related risk model can enhance the ability to predict the survival outcomes of HCC patients and provide a feasible therapeutic strategy for immunotherapy and drug resistance in HCC. BIRC5 and SKP2 are hub genes of anoikis‑related genes in HCC.

Transcriptomic Analyses Reveal Long Non-Coding RNA in Peripheral Blood Mononuclear Cells as a Novel Biomarker for Diagnosis and Prognosis of Hepatocellular Carcinoma

Novel biomarkers are highly required for the diagnosis and predicting prognosis of hepatocellular carcinoma (HCC). In this study, we investigated the profiles of long non-coding RNAs (lncRNAs) obtained from the peripheral blood mononuclear cells (PBMCs) of patients with HCC and PBMCs from a co-culture model using transcriptomic analysis. The differentially expressed lncRNAs (DElncRNAs) were then characterized and integrated as cancer-induced lncRNAs. Among them, three up-regulating DElncRNAs including MIR4435-2HG, SNHG9 and lnc-LCP2-1 and one down-regulating, lnc-POLD3-2, were identified. The functional analysis showed that these enriched lncRNAs were mainly associated with carcinogenesis and immune responses. Following further validation in PBMCs samples (100 HBV-related HCC, 100 chronic hepatitis B and 100 healthy controls), MIR4435-2HG, lnc-POLD3-2 and their combination were revealed to be sensitive biomarkers in discriminating HCC from non-HCC (AUROC = 0.78, 0.80, and 0.87, respectively), particularly among individuals with normal serum alpha-fetoprotein levels. Additionally, high circulating SNHG9 expression was shown to be an independent prognostic factor of overall survival in patients with HCC. These results indicate that determining these lncRNAs in PBMCs could serve as novel diagnostic and prognostic biomarkers for HBV-related HCC.