Identification of Epigenetic Regulatory Networks of Gene Methylation-miRNA-Transcription Factor Feed-Forward Loops in Basal-like Breast Cancer.

Basal-like breast cancer (BLBC) is associated with poor prognosis, high recurrence rates, and limited therapeutic options, largely due to its molecular heterogeneity and complexity, which include epigenetic alterations. This study investigated epigenetic regulatory networks in BLBC by analyzing DNA methylation in distal cis-regulatory regions and its impact on genes, transcription factors (TFs), and microRNAs (miRNAs) expression. Data from TCGA were processed using the ELMER and DESeq2 tools to identify differentially methylated regions and differentially expressed genes, TFs, and miRNAs.

Steroidogenic factor-1 regulates a core set of target genes to promote malignancy in adrenocortical carcinoma.

Objective: Gene dosage is at the core of the biological activity of the steroidogenic factor-1 (SF-1/NR5A1) transcription factor. Its overexpression in adrenocortical carcinoma (ACC) is associated with enhanced proliferation and invasive capacities, steroid modulation, immune suppression, and poor prognosis. Surprisingly, 3 independent studies showed less than 10% agreement in identifying SF-1-regulated genes in the same ACC cell line, raising concerns about technical reproducibility and methodological consistency.

Protocol for obtaining cancer type and subtype predictions using subSCOPE.

We present a protocol for obtaining cancer type and subtype predictions using a machine learning method (subSCOPE). We describe steps for data preparation, subSCOPE setup, and running subSCOPE inference on prepared data. The protocol supports five -omics data types as input (DNA methylation, gene expression, microRNA [miRNA] expression, point mutations, and copy-number variants) and allows individual cancer type and data type selection.

Protocol for assessing distances in pathway space for classifier feature sets from machine learning methods.

As genes tend to be co-regulated as gene modules, feature selection in machine learning (ML) on gene expression data can be challenged by the complexity of gene regulation. Here, we present a protocol for reconciling differences in classifier features identified using different ML approaches. We describe steps for loading the PathwaySpace R package, preparing input for analysis, and creating density plots of gene sets.

Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets.

Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suitable for assigning subtype labels to new cancer specimens from other studies or clinical trials. Here, we address this barrier by applying five different machine learning approaches to multi-omic data from 8,791 TCGA tumor samples comprising 106 subtypes from 26 different cancer cohorts to build models based upon small numbers of features that can classify new samples into previously defined TCGA molecular subtypes-a step toward molecular subtype application in the clinic.

Bridge: A New Algorithm for Rooting Orthologous Genes in Large-Scale Evolutionary Analyses.

Orthology information has been used for searching patterns in high-dimensional data, allowing transferring functional information between species. The key concept behind this strategy is that orthologous genes share ancestry to some extent. While reconstructing the history of a single gene is feasible with the existing computational resources, the reconstruction of entire biological systems remains challenging.

PD-1/PD-L1 Inhibitors Response in Triple-Negative Breast Cancer: Can Long Noncoding RNAs Be Associated?

As immune checkpoint inhibitors (ICI) emerge as a paradigm-shifting treatment option for patients with advanced or metastatic cancer, there is a growing demand for biomarkers that can distinguish which patients are likely to benefit. In the case of triple-negative breast cancer (TNBC), characterized by a lack of therapeutic targets, pembrolizumab approval for high-risk early-stage disease occurred regardless of PD-L1 status, which keeps the condition in a biomarker limbus. In this review, we highlight the participation of long non-coding RNAs (lncRNAs) in the regulation of the PD-1/PD-L1 pathway, as well as in the definition of prognostic immune-related signatures in many types of tumors, aiming to shed light on molecules that deserve further investigation for a potential role as biomarkers.

Sertraline as a potential cancer therapeutic approach: Biological relevance of TCTP in breast cancer cell lines and tumors.

Purpose: This study aimed to evaluate the role of Translationally Controlled Tumor Protein (TCTP) in breast cancer (BC) and investigate the effects of sertraline, a serotonin selective reuptake inhibitor (SSRI), on BC cells. The objective was to assess the potential of sertraline as a therapeutic agent in BC treatment by examining its ability to inhibit TCTP expression and exert antitumor effects.

Material And Methods: We utilized five different BC cell lines representing the molecular heterogeneity and distinct subtypes of BC, including luminal, normal-like, HER2-positive, and triple-negative BC.

Expression-based subtypes define pathologic response to neoadjuvant immune-checkpoint inhibitors in muscle-invasive bladder cancer.

Checkpoint immunotherapy (CPI) has increased survival for some patients with advanced-stage bladder cancer (BCa). However, most patients do not respond. Here, we characterized the tumor and immune microenvironment in pre- and post-treatment tumors from the PURE01 neoadjuvant pembrolizumab immunotherapy trial, using a consolidative approach that combined transcriptional and genetic profiling with digital spatial profiling.

spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape.

Motivation: Cancer is one of the leading causes of death worldwide. Despite significant improvements in prevention and treatment, mortality remains high for many cancer types. Hence, innovative methods that use molecular data to stratify patients and identify biomarkers are needed.

Comprehensive Characterization of the Regulatory Landscape of Adrenocortical Carcinoma: Novel Transcription Factors and Targets Associated with Prognosis.

We reconstructed a transcriptional regulatory network for adrenocortical carcinoma (ACC) using transcriptomic and clinical data from The Cancer Genome Atlas (TCGA)-ACC cohort. We investigated the association of transcriptional regulatory units (regulons) with overall survival, molecular phenotypes, and immune signatures. We annotated the ACC regulons with cancer hallmarks and assessed single sample regulon activities in the European Network for the Study of Adrenal Tumors (ENSAT) cohort.

FOXA1 repression drives lineage plasticity and immune heterogeneity in bladder cancers with squamous differentiation.

Cancers arising from the bladder urothelium often exhibit lineage plasticity with regions of urothelial carcinoma adjacent to or admixed with regions of divergent histomorphology, most commonly squamous differentiation. To define the biologic basis for and clinical significance of this morphologic heterogeneity, here we perform integrated genomic analyses of mixed histology bladder cancers with separable regions of urothelial and squamous differentiation. We find that squamous differentiation is a marker of intratumoral genomic and immunologic heterogeneity in patients with bladder cancer and a biomarker of intrinsic immunotherapy resistance.

A transcriptional network of cell cycle dysregulation in noninvasive papillary urothelial carcinoma.

Human cancers display a restricted set of expression profiles, despite diverse mutational drivers. This has led to the hypothesis that select sets of transcription factors act on similar target genes as an integrated network, buffering a tumor's transcriptional state. Noninvasive papillary urothelial carcinoma (NIPUC) with higher cell cycle activity has higher risk of recurrence and progression.

Stage-stratified molecular profiling of non-muscle-invasive bladder cancer enhances biological, clinical, and therapeutic insight.

Understanding the molecular determinants that underpin the clinical heterogeneity of non-muscle-invasive bladder cancer (NMIBC) is essential for prognostication and therapy development. Stage T1 disease in particular presents a high risk of progression and requires improved understanding. We present a detailed multi-omics study containing gene expression, copy number, and mutational profiles that show relationships to immune infiltration, disease recurrence, and progression to muscle invasion.

TreeAndLeaf: an R/Bioconductor package for graphs and trees with focus on the leaves.

Motivation: Dendrogram is a classical diagram for visualizing binary trees. Although efficient to represent hierarchical relations, it provides limited space for displaying information on the leaf elements, especially for large trees.

Results: Here, we present TreeAndLeaf, an R/Bioconductor package that implements a hybrid layout strategy to represent tree diagrams with focus on the leaves.

Adrenocortical Carcinoma Steroid Profiles: Pan-Cancer Analysis of TCGA Data Uncovers Immunotherapy Targets for Potential Improved Outcomes.

Despite progress in understanding the biology of adrenocortical carcinoma (ACC), treatment options have not dramatically changed in the last three decades, nor have we learned how to avoid some of its long-term side effects. Our goal was to improve the understanding of immune pathways that may include druggable targets to enhance immune responses of patients with ACC, focusing on immune evasion and the activation of immune cells against ACC. Our strategy was aimed at improving insight regarding gene expression without steroid interference.

Unraveling Immune-Related lncRNAs in Breast Cancer Molecular Subtypes.

Breast cancer (BRCA) is the most leading cause of cancer worldwide. It is a heterogeneous disease with at least five molecular subtypes including luminal A, luminal B, basal-like, HER2-enriched, and normal-like. These five molecular subtypes are usually stratified according to their mRNA profile patterns; however, ncRNAs are increasingly being used for this purpose.

Dysregulation of Transcription Factor Networks Unveils Different Pathways in Human Papillomavirus 16-Positive Squamous Cell Carcinoma and Adenocarcinoma of the Uterine Cervix.

Squamous cell carcinoma (SCC) and adenocarcinoma (ADC) are the most common histological types of cervical cancer (CC). The worse prognosis of ADC cases highlights the need for better molecular characterization regarding differences between these CC types. RNA-Seq analysis of seven SCC and three ADC human papillomavirus 16-positive samples and the comparison with public data from non-tumoral human papillomavirus-negative cervical tissue samples revealed pathways exclusive to each histological type, such as the epithelial maintenance in SCC and the maturity-onset diabetes of the young (MODY) pathway in ADC.

AKT3 Expression in Mesenchymal Colorectal Cancer Cells Drives Growth and Is Associated with Epithelial-Mesenchymal Transition.

Colorectal cancer (CRC) is a heterogeneous disease that can currently be subdivided into four distinct consensus molecular subtypes (CMS) based on gene expression profiling. The CMS4 subtype is marked by high expression of mesenchymal genes and is associated with a worse overall prognosis compared to other CMSs. Importantly, this subtype responds poorly to the standard therapies currently used to treat CRC.

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells.

Long non-coding RNAs (lncRNAs) are functional transcripts with more than 200 nucleotides. These molecules exhibit great regulatory capacity and may act at different levels of gene expression regulation. Despite this regulatory versatility, the biology of these molecules is still poorly understood.

Whole-genome characterization of lung adenocarcinomas lacking the RTK/RAS/RAF pathway.

RTK/RAS/RAF pathway alterations (RPAs) are a hallmark of lung adenocarcinoma (LUAD). In this study, we use whole-genome sequencing (WGS) of 85 cases found to be RPA(-) by previous studies from The Cancer Genome Atlas (TCGA) to characterize the minority of LUADs lacking apparent alterations in this pathway. We show that WGS analysis uncovers RPA(+) in 28 (33%) of the 85 samples.