Identification of Novel Multi-Omics Expression Landscapes and Meta-Analysis of Landscape-Based Competitive Endogenous RNA Networks in ALDH+ Lung Adenocarcinoma Stem Cells

Glioblastoma stem cell long non-coding RNAs: therapeutic perspectives and opportunities

Glioblastoma poses a formidable challenge among primary brain tumors: its tumorigenic stem cells, capable of self-renewal, proliferation, and differentiation, contribute substantially to tumor initiation and therapy resistance. These glioblastoma stem cells (GSCs), resembling conventional stem and progenitor cells, adopt pathways critical for tissue development and repair, promoting uninterrupted tumor expansion. Long non-coding RNAs (lncRNAs), a substantial component of the human transcriptome, have garnered considerable interest for their pivotal roles in normal physiological processes and cancer pathogenesis. They display cell- or tissue-specific expression patterns, and extensive investigations have highlighted their impact on regulating GSC properties and cellular differentiation, thus offering promising avenues for therapeutic interventions. Consequently, lncRNAs, with their ability to exert regulatory control over tumor initiation and progression, have emerged as promising targets for innovative glioblastoma therapies. This review explores notable examples of GSC-associated lncRNAs and elucidates their functional roles in driving glioblastoma progression. Additionally, we delved deeper into utilizing a 3D in vitro model for investigating GSC biology and elucidated four primary methodologies for targeting lncRNAs as potential therapeutics in managing glioblastoma.

Diagnostic and predictive significance of the ferroptosis-related gene TXNIP in lung adenocarcinoma stem cells based on multi-omics

BACKGROUND

Lung cancer stands as the foremost cause of cancer-related fatalities globally. The presence of cancer stem cells (CSCs) poses a challenge, rendering current targeted tumor therapies ineffective. This study endeavors to investigate a novel therapeutic approach focusing on ferroptosis and delves into the expression of ferroptosis-related genes within lung CSCs.

METHODS

We systematically examined RNA-seq datasets derived from lung tumor cells (LTCs) and lung cancer stem cells (LSCs), as previously investigated in our research. Our focus was on analyzing differentially expressed genes (DEGs) related to ferroptosis. Utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO), we conducted functional analysis of these ferroptosis-related DEGs. Additionally, we employed protein‒protein interaction networks to identify hub genes. LC‒MS/MS analysis of LTCs and LSCs was conducted to pinpoint the crucial ferroptosis-related gene-thioredoxin-interacting protein (TXNIP).Further, we delved into the immune cell infiltration landscape of LTCs and LSCs, examining the correlation between TXNIP and lung adenocarcinoma (LUAD) using data from The Cancer Genome Atlas (TCGA) database. To complement these findings, we measured the expression levels of TXNIP, glutathione peroxidase 4(GPX4), nuclear receptor coactivator 4 (NCOA4) in LUAD tissues through immunohistochemistry (IHC) staining.

RESULTS

A total of 651 DEGs were identified, with 17 of them being ferroptosis-related DEGs. These seventeen genes were categorized into four groups: driver genes, suppressor genes, unclassified genes, and inducer genes. Enrichment analysis revealed significant associations with oxidative stress, cell differentiation, tissue development, and cell death processes. The RNA-seq analysis demonstrated consistent gene expression patterns with protein expression, as evidenced by mass spectrometry analysis. Among the identified genes, SFN and TXNIP were singled out as hub genes, with TXNIP showing particularly noteworthy expression. The expression of the ferroptosis-related gene TXNIP exhibited correlations with the presence of an immunosuppressive microenvironment, TNM stages, and the degree of histological differentiation.Also, the ferroptosis-markers GPX4 and NCOA4 displayed correlations with LUAD. This comprehensive analysis underscores the significance of TXNIP in the context of ferroptosis-related processes and their potential implications in cancer development and progression.

CONCLUSION

The investigation conducted in this study systematically delved into the role of the ferroptosis-related gene TXNIP in Lung CSCs. The identification of TXNIP as a potentially valuable biomarker in this context could have significant implications for refining prognostic assessments and optimizing therapeutic strategies for advanced lung cancer.

Integrative models of histopathological images and multi-omics data predict prognosis in endometrial carcinoma

Objective

This study aimed to predict the molecular features of endometrial carcinoma (EC) and the overall survival (OS) of EC patients using histopathological imaging.

Methods

The patients from The Cancer Genome Atlas (TCGA) were separated into the training set (n = 215) and test set (n = 214) in proportion of 1:1. By analyzing quantitative histological image features and setting up random forest model verified by cross-validation, we constructed prognostic models for OS. The model performance is evaluated with the time-dependent receiver operating characteristics (AUC) over the test set.

Results

Prognostic models based on histopathological imaging features (HIF) predicted OS in the test set (5-year AUC = 0.803). The performance of combining histopathology and omics transcends that of genomics, transcriptomics, or proteomics alone. Additionally, multi-dimensional omics data, including HIF, genomics, transcriptomics, and proteomics, attained the largest AUCs of 0.866, 0.869, and 0.856 at years 1, 3, and 5, respectively, showcasing the highest discrepancy in survival (HR = 18.347, 95% CI [11.09-25.65], p < 0.001).

Conclusions

The results of this experiment indicated that the complementary features of HIF could improve the prognostic performance of EC patients. Moreover, the integration of HIF and multi-dimensional omics data might ameliorate survival prediction and risk stratification in clinical practice.