LncRNA HOXA­AS2 is a prognostic and clinicopathological predictor in patients with cancer: A meta­analysis.

Elevated expression of long non-coding RNA homeobox A cluster antisense RNA 2 (lncRNA HOXA-AS2) is known to have prognostic value in various solid tumors. The present meta-analysis aimed to comprehensively quantify its prognostic significance across a wider spectrum of malignancies and to provide an updated synthesis of evidence that could refine prognostic models. To achieve this aim, multiple databases were carefully searched for lncRNA HOXA-AS2-related articles published in the past 10 years. Hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to demonstrate the prognostic value of lncRNA HOXA-AS2 using Stata 15.0 software. The function of lncRNA HOXA-AS2 was inferred from its associations with key clinical outcomes such as lymph node metastasis, distant metastasis, tumor stage and tumor size, which may reflect its role in tumor biology. In the present systematic review and meta-analysis of 454 patients across 7 studies, it was found that high lncRNA HOXA-AS2 expression was significantly associated with a shorter overall survival (OS) time in patients with cancer (HR=2.14; 95% CI, 1.40-3.27; P<0.001). High lncRNA HOXA-AS2 expression was also associated with lymph node metastasis [odds ratio (OR)=2.06; 95% CI, 1.07-3.99; P=0.032], distant metastasis (OR=2.11; 95% CI, 1.15-3.88; P=0.016), advanced tumor stage (OR=2.71; 95% CI, 1.50-4.89; P=0.001) and larger tumor size (OR=2.02; 95% CI, 0.86-4.78; P=0.006). However, no significant association was observed with age (OR=1.00; 95% CI, 0.63-1.59; P=0.991) or sex (OR=1.55; 95% CI, 0.72-3.34; P=0.258). In conclusion, elevated expression of lncRNA HOXA-AS2 was significantly related to poor clinical outcomes in various cancer types, such as osteosarcoma, non-small cell lung cancer and papillary thyroid carcinoma, a finding that was further confirmed by the present study. Specifically, the potential of lncRNAHOXA-AS2 as a biomarker in assessing tumor stage, metastasis risk and OS in patients was demonstrated. However, the results of the present study also indicated that the expression of lncRNA HOXA-AS2 was not significantly associated with age or sex, suggesting its role in cancer progression might be independent of these factors. This insight may direct future research to place more focus on the relationship between lncRNA HOXA-AS2 and specific cancer types and clinical characteristics.

Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients.

Immunotherapy has made great progress in hepatocellular carcinoma (HCC), yet there is still a lack of biomarkers for predicting response to it. Cancer stem cells (CSCs) are the primary cause of the tumorigenesis, metastasis, and multi-drug resistance of HCC. This study aimed to propose a novel CSCs-related cluster of HCC to predict patients' response to immunotherapy. Based on RNA-seq datasets from The Cancer Genome Atlas (TCGA) and Progenitor Cell Biology Consortium (PCBC), one-class logistic regression (OCLR) algorithm was applied to compute the stemness index (mRNAsi) of HCC patients. Unsupervised consensus clustering was performed to categorize HCC patients into two stemness subtypes which further proved to be a predictor of tumor immune microenvironment (TIME) status, immunogenomic expressions and sensitivity to neoadjuvant therapies. Finally, four machine learning algorithms (LASSO, RF, SVM-RFE and XGboost) were applied to distinguish different stemness subtypes. Thus, a five-hub-gene based classifier was constructed in TCGA and ICGC HCC datasets to predict patients' stemness subtype in a more convenient and applicable way, and this novel stemness-based classification system could facilitate the prognostic prediction and guide clinical strategies of immunotherapy and targeted therapy in HCC.

Interplay of four types of RNA modification writers revealed distinct tumor microenvironment and biological characteristics in pancreatic cancer.

Background: Pancreatic cancer (PC) is one of the most lethal malignancies and carries a dismal mortality and morbidity. Four types of RNA modification (namely m6A, m1A, APA and A-to-I) could be catalyzed by distinct enzymatic compounds ("writers"), mediating numerous epigenetic events in carcinogenesis and immunomodulation. We aim to investigate the interplay mechanism of these writers in immunogenomic features and molecular biological characteristics in PC. Methods: We first accessed the specific expression pattern and transcriptional variation of 26 RNA modification writers in The Cancer Genome Atlas (TCGA) dataset. Unsupervised consensus clustering was performed to divide patients into two RNA modification clusters. Then, based on the differentially expressed genes (DEGs) among two clusters, RNA modification score (WM_Score) model was established to determine RNA modification-based subtypes and was validated in International Cancer Genome Consortium (ICGC) dataset. What's more, we manifested the unique status of WM_Score in transcriptional and post-transcriptional regulation, molecular biological characteristics, targeted therapies and immunogenomic patterns. Results: We documented the tight-knit correlations between transcriptional expression and variation of RNA modification writers. We classified patients into two distinct RNA modification patterns (WM_Score_high and _low), The WM_Score_high subgroup was correlated with worse prognosis, Th2/Th17 cell polarization and oncogenic pathways (e.g. EMT, TGF-ß, and mTORC1 signaling pathways), whereas the WM_Score_low subgroup associated with favorable survival rate and Th1 cell trend. WM_Score model also proved robust predictive power in interpreting transcriptional and post-transcriptional events. Additionally, the potential targeted compounds with related pathways for the WM_Score model were further identified. Conclusions: Our research unfolds a novel horizon on the interplay network of four RNA modifications in PC. This WM_Score model demonstrated powerful predictive capacity in epigenetic, immunological and biological landscape, providing a theoretical basis for future clinical judgments of PC.