Epigenetic study of early breast cancer (EBC) based on DNA methylation and gene integration analysis

Cellular functions of heat shock protein 20 (HSPB6) in cancer: A review

Heat shock proteins (HSP) are a large family of peptide proteins that are widely found in cells. Studies have shown that the expression and function of HSPs in cells are very complex, and they can participate in cellular physiological and pathological processes through multiple pathways. Multiple heat shock proteins are associated with cancer cell growth, proliferation, metastasis, and resistance to anticancer drugs, and they play a key role in cancer development by ensuring the correct folding or degradation of proteins in cancer cells. As research hotspots, HSP90, HSP70 and HSP27 have been extensively studied in cancer so far. However, HSP20, also referred to as HSPB6, as a member of the small heat shock protein family, has been shown to play an important role in the cardiovascular system, but little research has been conducted on HSP20 in cancer. This review summarizes the current cellular functions of HSP20 in different cancer types, as well as its effects on cancer proliferation, progression, prognosis, and its other functions in cancer, to illustrate the close association between HSP20 and cancer. We show that, unlike most HSPs, HSP20 mainly plays an active anticancer role in cancer development, which is expected to provide new ideas and help for cancer diagnosis and treatment and research.

Omics Technologies Improving Breast Cancer Research and Diagnostics

Breast cancer (BC) has yielded approximately 2.26 million new cases and has caused nearly 685,000 deaths worldwide in the last two years, making it the most common diagnosed cancer type in the world. BC is an intricate ecosystem formed by both the tumor microenvironment and malignant cells, and its heterogeneity impacts the response to treatment. Biomedical research has entered the era of massive omics data thanks to the high-throughput sequencing revolution, quick progress and widespread adoption. These technologies-liquid biopsy, transcriptomics, epigenomics, proteomics, metabolomics, pharmaco-omics and artificial intelligence imaging-could help researchers and clinicians to better understand the formation and evolution of BC. This review focuses on the findings of recent multi-omics-based research that has been applied to BC research, with an introduction to every omics technique and their applications for the different BC phenotypes, biomarkers, target therapies, diagnosis, treatment and prognosis, to provide a comprehensive overview of the possibilities of BC research.

Taxifolin Inhibits Breast Cancer Growth by Facilitating CD8+ T Cell Infiltration and Inducing a Novel Set of Genes including Potential Tumor Suppressor Genes in 1q21.3

Taxifolin inhibits breast cancer (BC) via novel mechanisms. In a syngeneic mouse BC model, taxifolin suppressed 4T-1 cell-derived allografts. RNA-seq of 4T-1 tumors identified 36 differentially expressed genes (DEGs) upregulated by taxifolin. Among their human homologues, 19, 7, and 2 genes were downregulated in BCs, high-proliferative BCs, and BCs with high-fatality risks, respectively. Three genes were established as tumor suppressors and eight were novel to BC, including HNRN, KPRP, CRCT1, and FLG2. These four genes exhibit tumor suppressive actions and reside in 1q21.3, a locus amplified in 70% recurrent BCs, revealing a unique vulnerability of primary and recurrent BCs with 1q21.3 amplification with respect to taxifolin. Furthermore, the 36 DEGs formed a multiple gene panel (DEG36) that effectively stratified the fatality risk in luminal, HER2+, and triple-negative (TN) equivalent BCs in two large cohorts: the METABRIC and TCGA datasets. 4T-1 cells model human TNBC cells. The DEG36 most robustly predicted the poor prognosis of TNBCs and associated it with the infiltration of CD8+ T, NK, macrophages, and Th2 cells. Of note, taxifolin increased the CD8+ T cell content in 4T-1 tumors. The DEG36 is a novel and effective prognostic biomarker of BCs, particularly TNBCs, and can be used to assess the BC-associated immunosuppressive microenvironment.

Differential Expression and Function of SVIP in Breast Cancer Cell Lines and In Silico Analysis of Its Expression and Prognostic Potential in Human Breast Cancer

The heterogeneity of cancer strongly suggests the need to explore additional pathways to target. As cancer cells have increased proteotoxic stress, targeting proteotoxic stress-related pathways such as endoplasmic reticulum stress is attracting attention as a new anticancer treatment. One of the downstream responses to endoplasmic reticulum stress is endoplasmic reticulum-associated degradation (ERAD), a major degradation pathway that facilitates proteasome-dependent degradation of unfolded or misfolded proteins. Recently, SVIP (small VCP/97-interacting protein), an endogenous ERAD inhibitor, has been implicated in cancer progression, especially in glioma, prostate, and head and neck cancers. Here, the data of several RNA-sequencing (RNA-seq) and gene array studies were combined to evaluate the SVIP gene expression analysis on a variety of cancers, with a particular focus on breast cancer. The mRNA level of SVIP was found to be significantly higher in primary breast tumors and correlated well with its promoter methylation status and genetic alterations. Strikingly, the SVIP protein level was found to be low despite increased mRNA levels in breast tumors compared to normal tissues. On the other hand, the immunoblotting analysis showed that the expression of SVIP protein was significantly higher in breast cancer cell lines compared to non-tumorigenic epithelial cell lines, while most of the key proteins of gp78-mediated ERAD did not exhibit such an expression pattern, except for Hrd1. Silencing of SVIP enhanced the proliferation of p53 wt MCF-7 and ZR-75-1 cells but not p53 mutant T47D and SK-BR-3 cells; however, it increased the migration ability of both types of cell lines. Importantly, our data suggest that SVIP may increase p53 protein levels in MCF7 cells by inhibiting Hrd1-mediated p53 degradation. Overall, our data reveal the differential expression and function of SVIP on breast cancer cell lines together with in silico data analysis.

Multi-omics integration analysis of GPCRs in pan-cancer to uncover inter-omics relationships and potential driver genes

G protein-coupled receptors (GPCRs) are the largest drug target family. Unfortunately, applications of GPCRs in cancer therapy are scarce due to very limited knowledge regarding their correlations with cancers. Multi-omics data enables systematic investigations of GPCRs, yet their effective integration remains a challenge due to the complexity of the data. Here, we adopt two types of integration strategies, multi-staged and meta-dimensional approaches, to fully characterize somatic mutations, somatic copy number alterations (SCNAs), DNA methylations, and mRNA expressions of GPCRs in 33 cancers. Results from the multi-staged integration reveal that GPCR mutations cannot well predict expression dysregulation. The correlations between expressions and SCNAs are primarily positive, while correlations of the methylations with expressions and SCNAs are bimodal with negative correlations predominating. Based on these correlations, 32 and 144 potential cancer-related GPCRs driven by aberrant SCNA and methylation are identified, respectively. In addition, the meta-dimensional integration analysis is carried out by using deep learning models, which predict more than one hundred GPCRs as potential oncogenes. When comparing results between the two integration strategies, 165 cancer-related GPCRs are common in both, suggesting that they should be prioritized in future studies. However, 172 GPCRs emerge in only one, indicating that the two integration strategies should be considered concurrently to complement the information missed by the other such that obtain a more comprehensive understanding. Finally, correlation analysis further reveals that GPCRs, in particular for the class A and adhesion receptors, are generally immune-related. In a whole, the work is for the first time to reveal the associations between different omics layers and highlight the necessity of combing the two strategies in identifying cancer-related GPCRs.

Aldehyde dehydrogenase in solid tumors and other diseases: Potential biomarkers and therapeutic targets

The family of aldehyde dehydrogenases (ALDHs) contains 19 isozymes and is involved in the oxidation of endogenous and exogenous aldehydes to carboxylic acids, which contributes to cellular and tissue homeostasis. ALDHs play essential parts in detoxification, biosynthesis, and antioxidants, which are of important value for cell proliferation, differentiation, and survival in normal body tissues. However, ALDHs are frequently dysregulated and associated with various diseases like Alzheimer's disease, Parkinson's disease, and especially solid tumors. Notably, the involvement of the ALDHs in tumor progression is responsible for the maintenance of the stem-cell-like phenotype, triggering rapid and aggressive clinical progressions. ALDHs have captured increasing attention as biomarkers for disease diagnosis and prognosis. Nevertheless, these require further longitudinal clinical studies in large populations for broad application. This review summarizes our current knowledge regarding ALDHs as potential biomarkers in tumors and several non-tumor diseases, as well as recent advances in our understanding of the functions and underlying molecular mechanisms of ALDHs in disease development. Finally, we discuss the therapeutic potential of ALDHs in diseases, especially in tumor therapy with an emphasis on their clinical implications.

A novel marker based on necroptosis-related long non-coding RNA for forecasting prognostic in patients with clear cell renal cell carcinoma

Background: The incidence of clear cell renal cell carcinoma (ccRCC) is high and has increased gradually in recent years. At present, due to the lack of effective prognostic indicators, the prognosis of ccRCC patients is greatly affected.Necroptosis is a type of cell death, and along with cell necrosis is considered a new cancer treatment strategy. The aim of this study was to construct a new marker for predicting the prognosis of ccRCC patients based on long non-coding RNA (nrlncRNAs) associated with necroptosis.

Methods: RNA sequence data and clinical information of ccRCC patients from the Cancer Genome Atlas database (TCGA) were downloaded. NrlncRNA was identified by Pearson correlation study. The differentially expressed nrlncRNA and nrlncRNA pairs were identified by univariate Cox regression and Lasso-Cox regression. Finally, a Kaplan-Meier survival study, Cox regression, clinicopathological features correlation study, and receiver operating characteristic (ROC) spectrum were used to evaluate the prediction ability of 25-nrlncrnas for markers. In addition, correlations between the risk values and sensitivity to tumor-infiltrating immune cells, immune checkpoint inhibitors, and targeted drugs were also investigated.

Results: In the current research, a novel marker of 25-nrlncRNAs pairs was developed to improve prognostic prediction in patients with ccRCC. Compared with clinicopathological features, nrlncRNAs had a higher diagnostic validity for markers, with the 1-year, 3-years, and 5-years operating characteristic regions being 0.902, 0.835, and 0.856, respectively, and compared with the stage of 0.868, an increase of 0.034. Cox regression and stratified survival studies showed that this marker could be an independent predictor of ccRCC patients. In addition, patients with different risk scores had significant differences in tumor-infiltrating immune cells, immune checkpoint, and semi-inhibitory concentration of targeted drugs. The feature could be used to evaluate the clinical efficacy of immunotherapy and targeted drug therapy.

Conclusion: 25-nrlncRNAs pair markers may help to evaluate the prognosis and molecular characteristics of ccRCC patients, which improve treatment methods and can be more used in clinical practice.

MarkerGenie: an NLP-enabled text-mining system for biomedical entity relation extraction

Motivation

Natural language processing (NLP) tasks aim to convert unstructured text data (e.g. articles or dialogues) to structured information. In recent years, we have witnessed fundamental advances of NLP technique, which has been widely used in many applications such as financial text mining, news recommendation and machine translation. However, its application in the biomedical space remains challenging due to a lack of labeled data, ambiguities and inconsistencies of biological terminology. In biomedical marker discovery studies, tools that rely on NLP models to automatically and accurately extract relations of biomedical entities are valuable as they can provide a more thorough survey of all available literature, hence providing a less biased result compared to manual curation. In addition, the fast speed of machine reader helps quickly orient research and development.

Results

To address the aforementioned needs, we developed automatic training data labeling, rule-based biological terminology cleaning and a more accurate NLP model for binary associative and multi-relation prediction into the MarkerGenie program. We demonstrated the effectiveness of the proposed methods in identifying relations between biomedical entities on various benchmark datasets and case studies.

Availability and implementation

MarkerGenie is available at https://www.genegeniedx.com/markergenie/. Data for model training and evaluation, term lists of biomedical entities, details of the case studies and all trained models are provided at https://drive.google.com/drive/folders/14RypiIfIr3W_K-mNIAx9BNtObHSZoAyn?usp=sharing.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.