Investigation of genes and pathways involved in breast cancer subtypes through gene expression meta-analysis

A comprehensive genome-based analysis identifies the anti-cancerous role of the anoikis-related gene ADH1A in modulating the pathogenesis of breast cancer

Breast cancer (BC), a widespread and lethal neoplasm, is irrespective of the subtype of BC. Metastasis remains a crucial determinant for unfavorable outcome. The identification of novel diagnostic markers is instrumental in optimizing the treatment regime for BC. The direct correlation between anoikis and the progression/outcome of BC is well established. Nevertheless, the contribution of anoikis-related genes (ARGs) in BC remains obscure at present. We implemented the METABRIC dataset to scrutinize and assess differentially expressed ARGs in BC versus healthy breast tissues. An unsupervised consensus clustering approach for ARGs was employed to classify patients into diverse subtypes. ESTIMATE algorithms were utilized to assess immune infiltrative patterns. Prognostic gene expression patterns were derived from LASSO regression and univariate COX regression analysis. Subsequently, these signatures underwent examination via use of the Kaplan-Meier survival curve. 6 pairs of fresh tissue specimens (tumor and adjacent non-tumor) were employed to assess the expression of 7 ARGs genes via qPCR. Notably, DCN and FOS were not expressed in BC tissue, which had been excluded in our subsequent experiments. Also, among remaining 5 ARGs, solely the expression of ADH1A demonstrated a statistically remarkable disparity between freshly collected cancer tissues and the adjacent ones. ADH1A-overexpressed and ADH1A-sh vectors were transfected into MCF-7 and MCF-7-AR cell lines, respectively. The expression status of FABP4, CALML5, ADH1A, C1orf106, CIDEC, β-catenin, N-cadherin, and Vimentin in the clinical samples were scrutinized using RT-qPCR and western blotting techniques. Migration and invasion through transwell chambers were employed to assess the migratory and invasive potential of the cells. Detailed evaluation of cell proliferation was conducted utilizing a Cell Counting Kit-8 (CCK-8) assay. The apoptotic index of the cells was determined by flow cytometry analysis. An innovative anoikis-associated signature consisting of seven genes, namely ADH1A, DCN, CIEDC, FABP4, FOS, CALML5, and C1orf106, was devised to stratify BC patients into high- and low-risk cohorts. This unique risk assessment model, formulated via the distinctive signature approach, has been validated as an independent prognostic indicator. Additional analysis demonstrated that distinct risk subtypes manifested variances in the tumor microenvironment and drug sensitivities. Suppression of ADH1A enhanced the migratory and invasive capacities and reduced these tumorigenesis-related protein levels, underscoring the prognostic role of ADH1A in the progression of BC. Through our meticulous study, we have elucidated the possible molecular markers and clinical implications of ARGs in BC. Our model, which incorporate seven ARGs, has proven to accurately forecast the survival outcomes of BC patients. Moreover, the thorough molecular study of ADH1A has augmented our comprehension of ARGs in BC and opened a novel avenue for guiding personalized and precise therapeutic interventions for BC patients.

Pan-Cancer Screening and Validation of CALU's Role in EMT Regulation and Tumor Microenvironment in Triple-Negative Breast Cancer

Purpose

Cancer-associated fibroblasts (CAFs) significantly contribute to tumor progression and the development of resistance to therapies across a range of malignancies, notably breast cancer. This study aims to elucidate the specific role and prognostic relevance of CALU across multiple cancer types.

Patients and Methods

The association between CALU expression and prognosis, along with clinical characteristics in BRCA, HNSC, KIRP, LGG, and LIHC, was analyzed using data from the TCGA, GTEx, and GEO databases. Transcriptomic analysis of TCGA BRCA project data provided insights into the interaction between CALU and epithelial-mesenchymal transition (EMT) marker genes. Using TIMER and TISCH databases, the correlation between CALU expression and tumor microenvironment infiltration was assessed, alongside an evaluation of CALU expression across various cell types. Furthermore, CALU's influence on TNBC BRCA cell lines was explored, and its expression in tumor tissues was confirmed through immunohistochemical analysis of clinical samples.

Results

This study revealed a consistent upregulation of CALU across several tumor types, including BRCA, KIRP, LIHC, HNSC, and LGG, with elevated CALU expression being associated with unfavorable prognoses. CALU expression was particularly enhanced in clinical contexts linked to poor outcomes. Genomic analysis identified copy number alterations as the principal factor driving CALU overexpression. Additionally, a positive correlation between CALU expression and CAF infiltration was observed, along with its involvement in the EMT process in both CAFs and malignant cells. In vitro experiments demonstrated that CALU is highly expressed in TNBC-BRCA cell lines, and knockdown of CALU effectively reversed EMT progression and inhibited cellular migration. Immunohistochemical analysis of clinical samples corroborated the elevated expression of CALU in tumors, along with alterations in EMT markers.

Conclusion

This comprehensive pan-cancer analysis underscores CALU's critical role in modulating the tumor microenvironment and facilitating cell migration via the EMT pathway, identifying it as a potential therapeutic target.

Evaluation of lncRNAs as Potential Biomarkers for Diagnosis of Metastatic Triple-Negative Breast Cancer through Bioinformatics and Machine Learning

Background

Triple-negative breast cancer (TNBC) is highly invasive and metastatic to the lymph nodes. Therefore, it is an urgent priority to distinguish novel biomarkers and molecular mechanisms of lymph node metastasis as the first step to the disease investigation. Long non-coding RNAs (lncRNAs) have widely been explored in cancer tumorigenesis, progression, and invasion.

Objectives

This study aimed to identify and evaluate lncRNAs in the signaling pathway of MMP11 gene in both metastatic and non-metastatic TNBC samples. The potential of lncRNAs in prognosis and diagnosis of the disease was also assessed using bioinformatics analysis, machine learning, and quantitative real-time PCR.

Materials and Methods

Using machine learning algorithms, we analyzed the available BC data from the Cancer Genome Atlas Network (TCGA) and identified three potential lncRNAs, gastric adenocarcinoma-associated, positive CD44 regulator, long intergenic noncoding RNA (GAPLINC), TPT1-AS1, and EIF1B antisense RNA 1 (EIF1B-AS1) that could successfully distinguish between metastatic and non-metastatic TNBC.

Results

The results showed the upregulation of GAPLINC lncRNA in metastatic BC tissues, compared to non-metastatic (P<0.01) and normal samples, though TPT1-AS1 and EIF1B-AS1 were downregulated in metastatic TNBC samples (P<0.01).

Conclusion

Given the aberrant expression of candidate lncRNAs and the underlying mechanisms, the above-mentioned RNAs could act as novel diagnostic and prognostic biomarkers in metastatic BC.

MiRNA-miRNA interaction network in peripheral blood of patients with myocardial infarction: a gene expression meta-analysis

In recent years, investigations have revealed that microRNAs (miRNAs) can bind together and form a miRNA-miRNA-mRNA regulatory network that alters the consequence of miRNA-mRNA interaction. If we consider the miRNA that binds to mRNA as the primary miRNA and the miRNA that binds to the primary miRNA as the secondary one, secondry miRNAs can act as master regulators upstream of primary miRNAs and their target mRNAs. One of the distinguishing characteristics of secondary miRNAs as master regulators within a diverse set of differentially expressed genes is the absence of direct target mRNA for them. Instead, these master regulators exclusively govern the regulation of miRNAs that target specific mRNAs. Through in silico analysis, we identified 18 miRNAs among 385 differentially expressed miRNAs (DEmiRNAs) with no direct target mRNAs among 58 differentially expressed mRNAs (DEmRNAs) in peripheral blood of patients with myocardial infarction (MI). Instead, these secondary miRNAs targeted 9 primary miRNAs that had 36 direct targets among 58 DEmRNAs. We found that one primary miRNA might be regulated by more than one secondary miRNAs and each secondary miRNA can target more than one primary miRNAs. Among identified miRNA-miRNA-mRNA networks miR-188-5p/miR-299-3p/natural killer cell granule protein (NKG7), miR-200a-3p/miR-199b-5p/granzyme B (GZMB), and miR-377-3p/miR-581/oviductal glycoprotein 1 (OVGP1) exhibited higher scors in terms of expression levels (>2-fold increase or decrease) and strengh of interactions (ΔG < -5). Given the extensive network of miRNA interactions, focusing on master regulators opens up avenues for identifying key regulatory nodes for more effective therapeutic strategies.

Expression pattern of non-coding RNAs in non-functioning pituitary adenoma

Non-functioning pituitary adenoma (NFPA) is a benign tumor arising from the adenohypophyseal cells. They can be associated with symptoms arising from mass effect. Although these tumors are regarded to be benign tumors, they are associated with increased comorbidity and mortality. Several studies have indicated abnormal expression of genes in these tumors. In the current study, we have used existing methods to identify differentially expressed genes (DEGs) including DE long non-coding RNAs (DElncRNAs) and DE microRNAs (DEmiRNAs) in NFPAs compared with normal samples. Then, we have assessed the relation between these genes and important signaling pathways. Our analyses led to identification of 3131 DEGs, including 189 downregulated DEGs (such as RPS4Y1 and DDX3Y) and 2898 upregulated DEGs (such as ASB3 and DRD4), and 44 DElncRNAs, including 8 downregulated DElncRNAs (such as NUTM2B-AS1 and MALAT1) and 36 upregulated DElncRNAs (such as BCAR4 and SRD5A3-AS1). GnRH signaling pathway, Tight junction, Gap junction, Melanogenesis, DNA replication, Nucleotide excision repair, Mismatch repair and N-Glycan biosynthesis have been among dysregulated pathways in NFPAs. Taken together, our study has revealed differential expression of several genes and signaling pathways in this type of tumors.