The overexpression of DNA repair genes in invasive ductal and lobular breast carcinomas: Insights on individual variations and precision medicine

PathExpSurv: pathway expansion for explainable survival analysis and disease gene discovery

BACKGROUND

In the field of biology and medicine, the interpretability and accuracy are both important when designing predictive models. The interpretability of many machine learning models such as neural networks is still a challenge. Recently, many researchers utilized prior information such as biological pathways to develop neural networks-based methods, so as to provide some insights and interpretability for the models. However, the prior biological knowledge may be incomplete and there still exists some unknown information to be explored.

RESULTS

We proposed a novel method, named PathExpSurv, to gain an insight into the black-box model of neural network for cancer survival analysis. We demonstrated that PathExpSurv could not only incorporate the known prior information into the model, but also explore the unknown possible expansion to the existing pathways. We performed downstream analyses based on the expanded pathways and successfully identified some key genes associated with the diseases and original pathways.

CONCLUSIONS

Our proposed PathExpSurv is a novel, effective and interpretable method for survival analysis. It has great utility and value in medical diagnosis and offers a promising framework for biological research.

The impact of microRNAs on the resistance of breast cancer subtypes to chemotherapy

Breast cancer (BC) formation is primarily influenced by genetics, epigenetics and environmental factors. Aberrant Genetics and epigenetics leads to a condition known as heterogeneity. The heterogeneity of BC can be divided into several subtypes. Among the epigenetic factors, microRNAs (miRNAs) have been shown to play a crucial role in the development and progression of malignancies. These small non-coding RNAs regulate gene expression through a variety of mechanisms, resulting in either mRNA degradation or translation repression. As miRNAs directly control many proteins, genetic anomalies affect tumor metastasis, apoptosis, proliferation, and cell transportation. Consequently, miRNA dysregulations contribute not only in cancer development but also in invasiveness, proliferation rate and more importantly, drug response. Findings mostly indicate subtype-specified identical miRNA profile in BC. Among the BC subtypes, TNBC, HER2 + and luminal are the most resistant to therapy, respectively. Therapy resistance is greatly associated with miRNA expression profile. Hence, concentration of miRNA is the first marker of its role in chemotherapy response. Overexpressed miRNAs may disrupt drug efflux transporters and decrease the drug accumulation in cell. While down-regulated miRNAs which mediate drug resistance processes are mostly correlated with poor treatment response. Moreover, other mechanisms in which miRNAs play crucial roles in chemoresistance such as cell receptor mediations, dysregulation by environmental factors, DNA defects, etc. Recently, several miRNA-based treatments have shown promising results in cancer treatment. Inhibition of up-regulated miRNAs is one of these therapeutic approaches whilst transfecting cell with down-regulated miRNAs also show promising results. Moreover, drug-resistance could also be determined while in the pre-treatment phase via expression levels of miRNAs. Therefore, miRNAs provide intriguing insights and challenges in overcoming chemoresistance. In this article, we have discussed how miRNAs regulate breast cancer subtypes-specific chemoresistance.

DNA repair pathways in breast cancer: from mechanisms to clinical applications

BACKGROUND

Breast cancer (BC) is a complex disease with various subtypes and genetic alterations that impact DNA repair pathways. Understanding these pathways is essential for developing effective treatments and improving patient outcomes.

AREA COVERED

This study investigates the significance of DNA repair pathways in breast cancer, specifically focusing on various pathways such as nucleotide excision repair, base excision repair, mismatch repair, homologous recombination repair, non-homologous end joining, fanconi anemia pathway, translesion synthesis, direct repair, and DNA damage tolerance. The study also examines the role of these pathways in breast cancer resistance and explores their potential as targets for cancer treatment.

CONCLUSION

Recent advances in targeted therapies have shown promise in exploiting DNA repair pathways for BC treatment. However, much research is needed to improve the efficacy of these therapies and identify new targets. Additionally, personalized treatments that target specific DNA repair pathways based on tumor subtype or genetic profile are being developed. Advances in genomics and imaging technologies can potentially improve patient stratification and identify biomarkers of treatment response. However, many challenges remain, including toxicity, resistance, and the need for more personalized treatments. Continued research and development in this field could significantly improve BC treatment.

High Glucose Increases DNA Damage and Elevates the Expression of Multiple DDR Genes

The DNA Damage Response (DDR) pathways sense DNA damage and coordinate robust DNA repair and bypass mechanisms. A series of repair proteins are recruited depending on the type of breaks and lesions to ensure overall survival. An increase in glucose levels was shown to induce genome instability, yet the links between DDR and glucose are still not well investigated. In this study, we aimed to identify dysregulation in the transcriptome of normal and cancerous breast cell lines upon changing glucose levels. We first performed bioinformatics analysis using a microarray dataset containing the triple-negative breast cancer (TNBC) MDA-MB-231 and the normal human mammary epithelium MCF10A cell lines grown in high glucose (HG) or in the presence of the glycolysis inhibitor 2-deoxyglucose (2DG). Interestingly, multiple DDR genes were significantly upregulated in both cell lines grown in HG. In the wet lab, we remarkably found that HG results in severe DNA damage to TNBC cells as observed using the comet assay. In addition, several DDR genes were confirmed to be upregulated using qPCR analysis in the same cell line. Our results propose a strong need for DDR pathways in the presence of HG to oppose the severe DNA damage induced in cells.

A feature selection-based framework to identify biomarkers for cancer diagnosis: A focus on lung adenocarcinoma

Lung cancer (LC) represents most of the cancer incidences in the world. There are many types of LC, but Lung Adenocarcinoma (LUAD) is the most common type. Although RNA-seq and microarray data provide a vast amount of gene expression data, most of the genes are insignificant to clinical diagnosis. Feature selection (FS) techniques overcome the high dimensionality and sparsity issues of the large-scale data. We propose a framework that applies an ensemble of feature selection techniques to identify genes highly correlated to LUAD. Utilizing LUAD RNA-seq data from the Cancer Genome Atlas (TCGA), we employed mutual information (MI) and recursive feature elimination (RFE) feature selection techniques along with support vector machine (SVM) classification model. We have also utilized Random Forest (RF) as an embedded FS technique. The results were integrated and candidate biomarker genes across all techniques were identified. The proposed framework has identified 12 potential biomarkers that are highly correlated with different LC types, especially LUAD. A predictive model has been trained utilizing the identified biomarker expression profiling and performance of 97.99% was achieved. In addition, upon performing differential gene expression analysis, we could find that all 12 genes were significantly differentially expressed between normal and LUAD tissues, and strongly correlated with LUAD according to previous reports. We here propose that using multiple feature selection methods effectively reduces the number of identified biomarkers and directly affects their biological relevance.

Transcriptional Analysis of Nuclear-Encoded Mitochondrial Genes in Eight Neurodegenerative Disorders: The Analysis of Seven Diseases in Reference to Friedreich’s Ataxia

Neurodegenerative diseases (NDDs) are challenging to understand, diagnose, and treat. Revealing the genomic and transcriptomic changes in NDDs contributes greatly to the understanding of the diseases, their causes, and development. Moreover, it enables more precise genetic diagnosis and novel drug target identification that could potentially treat the diseases or at least ease the symptoms. In this study, we analyzed the transcriptional changes of nuclear-encoded mitochondrial (NEM) genes in eight NDDs to specifically address the association of these genes with the diseases. Previous studies show strong links between defects in NEM genes and neurodegeneration, yet connecting specific genes with NDDs is not well studied. Friedreich’s ataxia (FRDA) is an NDD that cannot be treated effectively; therefore, we focused first on FRDA and compared the outcome with seven other NDDs, including Alzheimer’s disease, amyotrophic lateral sclerosis, Creutzfeldt–Jakob disease, frontotemporal dementia, Huntington’s disease, multiple sclerosis, and Parkinson’s disease. First, weighted correlation network analysis was performed on an FRDA RNA-Seq data set, focusing only on NEM genes. We then carried out differential gene expression analysis and pathway enrichment analysis to pinpoint differentially expressed genes that are potentially associated with one or more of the analyzed NDDs. Our findings propose a strong link between NEM genes and NDDs and suggest that our identified candidate genes can be potentially used as diagnostic markers and therapeutic targets.