Pan-cancer gene expression analysis: Identification of deregulated autophagy genes and drugs to target them

Targeting histone deacetylases in head and neck squamous cell carcinoma: molecular mechanisms and therapeutic targets

The onerous health and economic burden associated with head and neck squamous cell carcinoma (HNSCC) is a global predicament. Despite the advent of novel surgical techniques and therapeutic protocols, there is an incessant need for efficacious diagnostic and therapeutic targets to monitor the invasion, metastasis and recurrence of HNSCC due to its substantial morbidity and mortality. The differential expression patterns of histone deacetylases (HDACs), a group of enzymes responsible for modifying histones and regulating gene expression, have been demonstrated in neoplastic tissues. However, there is limited knowledge regarding the role of HDACs in HNSCC. Consequently, this review aims to summarize the existing research findings and explore the potential association between HDACs and HNSCC, offering fresh perspectives on therapeutic approaches targeting HDACs that could potentially enhance the efficacy of HNSCC treatment. Additionally, the Cancer Genome Atlas (TCGA) dataset, CPTAC, HPA, OmicShare, GeneMANIA and STRING databases are utilized to provide supplementary evidence on the differential expression of HDACs, their prognostic significance and predicting functions in HNSCC patients.

Integrative approach for classifying male tumors based on DNA methylation 450K data

Malignancies such as bladder urothelial carcinoma, colon adenocarcinoma, liver hepatocellular carcinoma, lung adenocarcinoma and prostate adenocarcinoma significantly impact men's well-being. Accurate cancer classification is vital in determining treatment strategies and improving patient prognosis. This study introduced an innovative method that utilizes gene selection from high-dimensional datasets to enhance the performance of the male tumor classification algorithm. The method assesses the reliability of DNA methylation data to distinguish the five most prevalent types of male cancers from normal tissues by employing DNA methylation 450K data obtained from The Cancer Genome Atlas (TCGA) database. First, the chi-square test is used for dimensionality reduction and second, L1 penalized logistic regression is used for feature selection. Furthermore, the stacking ensemble learning technique was employed to integrate seven common multiclassification models. Experimental results demonstrated that the ensemble learning model utilizing multiple classification models outperformed any base classification model. The proposed ensemble model achieved an astonishing overall accuracy (ACC) of 99.2% in independent testing data. Moreover, it may present novel ideas and pathways for the early detection and treatment of future diseases.

Insights from a Computational-Based Approach for Analyzing Autophagy Genes across Human Cancers

In the last decade, there has been a boost in autophagy reports due to its role in cancer progression and its association with tumor resistance to treatment. Despite this, many questions remain to be elucidated and explored among the different tumors. Here, we used omics-based cancer datasets to identify autophagy genes as prognostic markers in cancer. We then combined these findings with independent studies to further characterize the clinical significance of these genes in cancer. Our observations highlight the importance of innovative approaches to analyze tumor heterogeneity, potentially affecting the expression of autophagy-related genes with either pro-tumoral or anti-tumoral functions. In silico analysis allowed for identifying three genes (TBC1D12, KERA, and TUBA3D) not previously described as associated with autophagy pathways in cancer. While autophagy-related genes were rarely mutated across human cancers, the expression profiles of these genes allowed the clustering of different cancers into three independent groups. We have also analyzed datasets highlighting the effects of drugs or regulatory RNAs on autophagy. Altogether, these data provide a comprehensive list of targets to further the understanding of autophagy mechanisms in cancer and investigate possible therapeutic targets.

Advanced Bioinformatics Analysis and Genetic Technologies for Targeting Autophagy in Glioblastoma Multiforme

As the most malignant primary brain tumor in adults, a diagnosis of glioblastoma multiforme (GBM) continues to carry a poor prognosis. GBM is characterized by cytoprotective homeostatic processes such as the activation of autophagy, capability to confer therapeutic resistance, evasion of apoptosis, and survival strategy even in the hypoxic and nutrient-deprived tumor microenvironment. The current gold standard of therapy, which involves radiotherapy and concomitant and adjuvant chemotherapy with temozolomide (TMZ), has been a game-changer for patients with GBM, relatively improving both overall survival (OS) and progression-free survival (PFS); however, TMZ is now well-known to upregulate undesirable cytoprotective autophagy, limiting its therapeutic efficacy for induction of apoptosis in GBM cells. The identification of targets utilizing bioinformatics-driven approaches, advancement of modern molecular biology technologies such as clustered regularly interspaced short palindromic repeats (CRISPR)—CRISPR-associated protein (Cas9) or CRISPR-Cas9 genome editing, and usage of microRNA (miRNA)-mediated regulation of gene expression led to the selection of many novel targets for new therapeutic development and the creation of promising combination therapies. This review explores the current state of advanced bioinformatics analysis and genetic technologies and their utilization for synergistic combination with TMZ in the context of inhibition of autophagy for controlling the growth of GBM.

Identification and validation of a prognostic risk-scoring model based on sphingolipid metabolism-associated cluster in colon adenocarcinoma

Colon adenocarcinoma (COAD) is the primary factor responsible for cancer-related mortalities in western countries, and its development and progression are affected by altered sphingolipid metabolism. The current study aimed at investigating the effects of sphingolipid metabolism-related (SLP) genes on multiple human cancers, especially on COAD. We obtained 1287 SLP genes from the GeneCard and MsigDb databases along with the public transcriptome data and the related clinical information. The univariate Cox regression analysis suggested that 26 SLP genes were substantially related to the prognosis of COAD, and a majority of SLP genes served as the risk genes for the tumor, insinuating a potential pathogenic effect of SLP in COAD development. Pan-cancer characterization of SLP genes summarized their expression traits, mutation traits, and methylation levels. Subsequently, we focused on the thorough research of COAD. With the help of unsupervised clustering, 1008 COAD patients were successfully divided into two distinct subtypes (C1 and C2). C1 subtype is characterized by a poor prognosis, activation of SLP pathways, high expression of SLP genes, disordered carcinogenic pathways, and immune microenvironment. Based on the clusters of SLP, we developed and validated a novel prognostic model, consisting of ANO1, C2CD4A, EEF1A2, GRP, HEYL, IGF1, LAMA2, LSAMP, RBP1, and TCEAL2, to quantitatively evaluate the clinical outcomes of COAD. The Kaplain-Meier survival curves and ROC curves highlighted the accuracy of our SLP model in both internal and external cohorts. Compared to normal colon tissues, expression of C2CD4A was detected to be significantly higher in COAD; whereas, expression levels of EEF1A2, IGF1, and TCEAL2 were detected to be significantly lower in COAD. Overall, our research emphasized the pathogenic role of SLP in COAD and found that targeting SLP might help improve the clinical outcomes of COAD. The risk model based on SLP metabolism provided a new horizon for prognosis assessment and customized patient intervention.