Identification of altered biological processes in heterogeneous RNA-sequencing data by discretization of expression profiles

Abstract

Heterogeneity is a fundamental feature of complex phenotypes. So far, genomic screenings have profiled thousands of samples providing insights into the transcriptome of the cell. However, disentangling the heterogeneity of these transcriptomic Big Data to identify defective biological processes remains challenging. Here we present GSECA, a method exploiting the bimodal behavior of RNA-sequencing gene expression profiles to identify altered gene sets in heterogeneous patient cohorts. Using simulated and experimental RNA-sequencing data sets, we show that GSECA provides higher performances than other available algorithms in detecting truly altered biological processes in large cohorts. Applied to 5941 samples from 14 different cancer types, GSECA correctly identified the alteration of the PI3K/AKT signaling pathway driven by the somatic loss of PTEN and verified the emerging role of PTEN in modulating immune-related processes. In particular, we showed that, in prostate cancer, PTEN loss appears to establish an immunosuppressive tumor microenvironment through the activation of STAT3, and low PTEN expression levels have a detrimental impact on patient disease-free survival. GSECA is available at https://github.com/matteocereda/GSECA.

LncRNA BCYRN1 inhibits glioma tumorigenesis by competitively binding with miR-619-5p to regulate CUEDC2 expression and the PTEN/AKT/p21 pathway

“…Knockdown BCYRN1, as well as overexpression of miR-619-5p, activated AKT signaling, increased expression of P-AKT protein, decreased expression of PTEN and P21 through downregulating CUEDC2. In fact, PTEN/AKT signaling has been widely reported to play vital regulatory roles in cancers including glioma [ 63 – 65 ]. Our findings demonstrated that the role of the regulatory network between BCYRN1/miR-619-5p/CUEDC2 in glioma was achieved by affecting PTEN/AKT signaling pathway activity.…”

An Integrated Analysis of the Identified PRPF19 as an Onco-immunological Biomarker Encompassing the Tumor Microenvironment, Disease Progression, and Prognoses in Hepatocellular Carcinoma

“…Clinical outcome metrics of overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS) were derived from the TCGA-LIHC cohort. The “surv-cutpoint” function of the survminer R package was used to investigate the optimal cutoff for dividing high and low expression samples ( Lauria et al, 2020 ). Subgroup analyses were also carried out to evaluate the effect of PRPF19 prognosis on OS across various subgroups.…”

Single-cell sequencing: a promising approach for uncovering the mechanisms of tumor metastasis

“…The development of AI industry enables easier and more visually appealing solutions for SCS technology. For example, AI can be widely exploited in all aspects of the SCS workflow, such as batch correction for technical heterogeneity [ 133 , 134 ], feature extraction [ 135 , 136 ], data distribution transformation [ 137 , 138 ], classification of cancer subtypes [ 139 , 140 ], and biomarker identification [ 141 – 143 ]. Most notably, SCS in combination with AI is also widely used to identify and analyze CTCs, a class of cells that can be used for searching therapeutic targets for tumor metastasis [ 133 – 144 ].…”