A Greedy Algorithm-Based Stem Cell LncRNA Signature Identifies a Novel Subgroup of Lung Adenocarcinoma Patients With Poor Prognosis

An anoikis-related lncRNA signature is a useful tool for predicting the prognosis of patients with lung adenocarcinoma

Background

Anoikis-related long non-coding RNAs (ARLs) play a critical role in tumor metastasis and progression, suggesting that they may serve as risk markers for cancer. This study aimed to investigate the prognostic value of ARLs in patients with lung adenocarcinoma (LUAD).

Methods

Clinical data, RNA sequencing (RNA-seq) data, and mutation data from the LUAD project were obtained from The Cancer Genome Atlas (TCGA) database. The Molecular Signatures Database (MSigDB) and the GeneCard database were used to collect an anoikis-related gene (ARG) set. Pearson correlation analysis was performed to identify ARLs. LASSO and Cox regression were then used to establish a prognostic risk signature for ARLs. The median risk score served as the basis for categorizing patients into high and low-risk groups. Kaplan-Meier analysis was utilized to compare the prognosis between these two groups. The study also examined the associations between risk scores and prognosis, clinicopathological characteristics, immune status, tumor mutation burden (TMB), and chemotherapeutic agents. LncRNA expression was assessed using quantitative real-time PCR (qRT-PCR).

Results

A total of 480 RNA expression profiles, 501 ARGs, and 2698 ARLs were obtained from the database. A prognostic ARL signature for LUAD was established, consisting of 9 lncRNAs. Patients in the low-risk group exhibited significantly better prognosis compared to those in the high-risk group (P < 0.001). The 9 lncRNAs from the ARL signature were identified as independent prognostic factors (P < 0.001). The signature demonstrated high accuracy in predicting LUAD prognosis, with area under the curve values exceeding 0.7. The risk scores for ARLs showed strong negative correlations with stroma score (P = 5.9E-07, R = -0.23), immune score (P = 9.7E-09, R = -0.26), and microenvironment score (P = 8E-11, R = -0.29). Additionally, the low-risk group exhibited significantly higher TMB compared to the high-risk group (P = 4.6E-05). High-risk status was significantly associated with lower half-maximal inhibitory concentrations for most chemotherapeutic drugs.

Conclusion

This newly constructed signature based on nine ARLs is a useful instrument for the risk stratification of LUAD patients. The signature has potential clinical significance for predicting the prognosis of LUAD patients and guiding personalized immunotherapy.

The non-coding genome in Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) are a group of neurodevelopmental disorders (NDDs) characterized by difficulties in social interaction and communication, repetitive behavior, and restricted interests. While ASD have been proven to have a strong genetic component, current research largely focuses on coding regions of the genome. However, non-coding DNA, which makes up for ∼99% of the human genome, has recently been recognized as an important contributor to the high heritability of ASD, and novel sequencing technologies have been a milestone in opening up new directions for the study of the gene regulatory networks embedded within the non-coding regions. Here, we summarize current progress on the contribution of non-coding alterations to the pathogenesis of ASD and provide an overview of existing methods allowing for the study of their functional relevance, discussing potential ways of unraveling ASD's "missing heritability".

Identification of Cancer Cell Stemness-Associated Long Noncoding RNAs for Predicting Prognosis of Patients with Hepatocellular Carcinoma

Long noncoding RNAs (lncRNAs) are emerging as crucial contributors to the development of hepatocellular carcinoma (HCC) and are involved in the stemness regulation of liver cancer stem cells (LCSCs). However, cancer cell stemness-associated lncRNAs and their relevance in prediction of clinical prognosis remain largely unexplored. In this study, through the transcriptome-wide screen, we identified a total of 136 LCSC-associated lncRNAs. We evaluated the prognostic value of these lncRNAs and optimally established an 11-lncRNA (including AC008622.2, AC015908.3, AC020915.2, AC025176.1, AC026356.2, AC099850.3, CYTOR, DDX11-AS1, HTR2A-AS1, LINC02870, and SNHG3) prognostic risk model. Multivariate analysis revealed that the risk score is an independent prognostic predictor for HCC patients, which outperforms the traditional clinical pathological factors. Gene set enrichment analysis suggested that the high-risk score reflects the alteration of pathways involved in cell cycle, oxidative phosphorylation, and metabolism. Furthermore, functional studies on SNHG12, the leading candidate of the risk lncRNAs, revealed that knockdown of SNHG12 reduces the abilities of HCC cells stemness, proliferation, migration, and invasion. In summary, we constructed a prognostic risk model based on 11 LCSC-associated lncRNAs, which might be a promising prognostic predictor for HCC patients and highlight the involvement of lncRNAs in LCSC-associated treatment strategy in clinical practice.