Gene co-expression network construction and analysis for identification of genetic biomarkers associated with glioblastoma multiforme using topological findings

Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification

Gliomas are primary malignant brain tumors with a typically poor prognosis, exhibiting significant heterogeneity across different cancer types. Each glioma type possesses distinct molecular characteristics determining patient prognosis and therapeutic options. This study aims to explore the molecular complexity of gliomas at the transcriptome level, employing a comprehensive approach grounded in network discovery. The graphical lasso method was used to estimate a gene co-expression network for each glioma type from a transcriptomics dataset. Causality was subsequently inferred from correlation networks by estimating the Jacobian matrix. The networks were then analyzed for gene importance using centrality measures and modularity detection, leading to the selection of genes that might play an important role in the disease. To explore the pathways and biological functions these genes are involved in, KEGG and Gene Ontology (GO) enrichment analyses on the disclosed gene sets were performed, highlighting the significance of the genes selected across several relevent pathways and GO terms. Spectral clustering based on patient similarity networks was applied to stratify patients into groups with similar molecular characteristics and to assess whether the resulting clusters align with the diagnosed glioma type. The results presented highlight the ability of the proposed methodology to uncover relevant genes associated with glioma intertumoral heterogeneity. Further investigation might encompass biological validation of the putative biomarkers disclosed.

A Novel Lysosome-Related Gene Signature Predicts Lung Cancer Prognosis: A Bioinformatics-Driven Study

Background and Aims

The biological function of lysosomes has been increasingly appreciated in cancer. However, the relationship between lysosome and lung adenocarcinoma (LUAD) was not well understood. In this study, a lysosome-related signature was developed for LUAD risk stratification and prognosis prediction.

Methods

Download RNA-seq data of LUAD and clinical information of corresponding samples from the UCSC-Xena platform. GSE31210 databases is used as a validation cohort. The lysosome-related genes was obtained from molecular signature database. The differentially expressed genes (DEGs) as well as lysosome-associated prognosis signatures were identified by using univariate, multivariate cox, and Lasso regression. A nomogram was constructed and evaluated using ROC and DCA.

Results

A total of 109 lysosome-related DEGs were identified and 30 prognostic related DEGs were subsequently screened. Cluster analysis further divides the TCGA cohort into clusters 1 and 2. Patients in cluster 2 had a worse prognosis (p = 0.016), lower LYSOSOME score. Enrichment analysis showed that 21 significantly enriched gene sets in the cluster 2 were activated. And 10 pathways, such as E2F_TARGETS, G2M_CHECKPOINT were upregulated. Multivariate Cox regression analysis identified 17 best prognostic genes as risk signature. An independent prognostic factor, the risk signature, was identified. A prognostic nomogram including risk signature, age, TNM stage, and gender was constructed, and ROC and DCA curves proved its excellent performance. We examined CTSZ and AP3S2 protein expression in 48 stage 3-4 NSCLC samples. Low AP3S2 expression was associated with better prognosis (median overall survival: 37.87 vs. 8.53 months, p = 0.0211). Increased CTSZ expression also indicated better prognosis (median overall survival: 6.77 vs. 30.50 months, p = 0.0306).

Conclusion

We identified molecular subtypes and lysosomal-based prognostic signatures for LUAD patients, as well as 17 genes that serve as a biomarker for evaluating the prognosis of LUAD patients.

Single-cell analysis of matrisome-related genes in breast invasive carcinoma: new avenues for molecular subtyping and risk estimation

Background

The incidence of breast cancer remains high and severely affects human health. However, given the heterogeneity of tumor cells, identifying additional characteristics of breast cancer cells is essential for accurate treatment.

Purpose

This study aimed to analyze the relevant characteristics of matrix genes in breast cancer through the multigroup data of a breast cancer multi-database.

Methods

The related characteristics of matrix genes in breast cancer were analyzed using multigroup data from the breast cancer multi database in the Cancer Genome Atlas, and the differential genes of breast cancer matrix genes were identified using the elastic net penalty logic regression method. The risk characteristics of matrix genes in breast cancer were determined, and matrix gene expression in different breast cancer cells was evaluated using real-time fluorescent quantitative polymerase chain reaction (PCR). A consensus clustering algorithm was used to identify the biological characteristics of the population based on the matrix molecular subtypes in breast cancer, followed by gene mutation, immune correlation, pathway, and ligand-receptor analyses.

Results

This study reveals the genetic characteristics of cell matrix related to breast cancer. It is found that 18.1% of stromal genes are related to the prognosis of breast cancer, and these genes are mostly concentrated in the biological processes related to metabolism and cytokines in protein. Five different matrix-related molecular subtypes were identified by using the algorithm, and it was found that the five molecular subtypes were obviously different in prognosis, immune infiltration, gene mutation and drug-making gene analysis.

Conclusions

This study involved analyzing the characteristics of cell-matrix genes in breast cancer, guiding the precise prevention and treatment of the disease.

Role of MARK2 in the nervous system and cancer

Microtubule-Affinity Regulating Kinase 2 (MARK2), a member of the serine/threonine protein kinase family, phosphorylates microtubule-associated proteins, playing a crucial role in cancer and neurodegenerative diseases. This kinase regulates multiple signaling pathways, including the WNT, PI3K/AKT/mTOR (PAM), and NF-κB pathways, potentially linking it to cancer and the nervous system. As a crucial regulator of the PI3K/AKT/mTOR pathway, the loss of MARK2 inhibits the growth and metastasis of cancer cells. MARK2 is involved in the excessive phosphorylation of tau, thus influencing neurodegeneration. Therefore, MARK2 emerges as a promising drug target for the treatment of cancer and neurodegenerative diseases. Despite its significance, the development of inhibitors for MARK2 remains limited. In this review, we aim to present detailed information on the structural features of MARK2 and its role in various signaling pathways associated with cancer and neurodegenerative diseases. Additionally, we further characterize the therapeutic potential of MARK2 in neurodegenerative diseases and cancer, and hope to facilitate basic research on MARK2 and the development of inhibitors targeting MARK2.