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Abstract Background Accuracy in the diagnosis of breast cancer and classification of cancer subtypes has improved over the years with the development of well-established immunohistopathological criteria. More recently, diagnostic gene-sets at the mRNA expression level have been tested as better predictors of disease state. However, breast cancer is heterogeneous in nature; thus extraction of differentially expressed gene-sets that stably distinguish normal tissue from various pathologies poses challenges. Meta-analysis of high-throughput expression data using a collection of statistical methodologies leads to the identification of robust tumor gene expression signatures. Methods A resampling-based meta-analysis strategy, which involves the use of resampling and application of distribution statistics in combination to assess the degree of significance in differential expression between sample classes, was developed. Two independent microarray datasets that contain normal breast, invasive ductal carcinoma (IDC), and invasive lobular carcinoma (ILC) samples were used for the meta-analysis. Expression of the genes, selected from the gene list for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes were tested on 10 independent primary IDC samples and matched non-tumor controls by real-time qRT-PCR. Other existing breast cancer microarray datasets were used in support of the resampling-based meta-analysis. Results The two independent microarray studies were found to be comparable, although differing in their experimental methodologies (Pearson correlation coefficient, R = 0.9389 and R = 0.8465 for ductal and lobular samples, respectively). The resampling-based meta-analysis has led to the identification of a highly stable set of genes for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes. The expression results of the selected genes obtained through real-time qRT-PCR supported the meta-analysis results. Conclusion The proposed meta-analysis approach has the ability to detect a set of differentially expressed genes with the least amount of within-group variability, thus providing highly stable gene lists for class prediction. Increased statistical power and stringent filtering criteria used in the present study also make identification of novel candidate genes possible and may provide further insight to improve our understanding of breast cancer development.

Spinal muscular atrophy (SMA) is one of the most common childhood onset neurodegenerative disorders in global health whereby novel biomarkers and therapeutic targets are sorely needed. SMA is an autosomal recessive genetic disorder resulting in degeneration of α-motor neurons in the brain stem and spinal cord that leads to mortality in infants worldwide. In majority of the patients, SMA is caused by homozygous deletion of the SMN1 gene. The clinical spectrum of the SMA displays, however, large person-to-person variations where the underlying mechanisms are poorly understood. We report in this study transcriptomics insights gleaned from patients with the severe type I (GM03813 and GM09677) and the mild type III. Pathway enrichment and functional analysis showed that especially extracellular matrix (ECM), synapse organization, and ECM receptor interaction pathways were affected. Among the neural ECM components, hyaluronan and proteoglycan link protein (HAPLN1), which is a key triggering molecule of the perineuronal net (PNN), was significantly downregulated in type I fibroblasts compared to type III. PNN is a specialized form of neural ECM around the neuronal cell bodies and dendrites in the central nervous system. In addition, we evaluated the PNN expression in vitro in a model established by SMN silencing in the PC12 rat pheochromocytoma cell line which can be differentiated into neurons with nerve growth factor treatment. In this neuronal in vitro model, we found that HAPLN1 showed a significant 50% decrease. Our results describe the association between PNN elements, especially HAPLN1, and SMA pathophysiology for the first time. These observations collectively inform future translational research on SMA for discovery of novel molecular targets for diagnostics and precision medicine innovation. [ABSTRACT FROM AUTHOR]

Pertuzumab (Omnitarg®, 2C4) is a recombinant humanized monoclonal antibody targeted to extracellular region of HER-2. Previous results proved the inhibitory effect of Pertuzumab on the survival of breast cancer cells via MAPK and Akt pathway. Focal adhesion kinase (FAK) regulates multiple cellular processes including growth, differentiation, adhesion, motility and apoptosis. Here, we aimed to investigate the effects of Pertuzumab on ligand activated total FAK expression and phosphorylation in the HER-2 overexpressing BT-474 breast cancer cell line. Heregulin was used for ligand activation. We have found that FAK expression and phosphorylation were inhibited in with Pertuzumab in breast cancer cells. [ABSTRACT FROM AUTHOR]

Conventional molecular and genetic methods for studying cancer are limited to the analysis of one locus at a time. A cluster of genes that are regulated together can be identified by DNA microarray, and the functional relationships can uncover new aspects of cancer biology. Breast cancer can be used to provide a model to demonstrate the current approaches to the molecular analysis of cancer. Meta-analysis is an important tool for the identification and validation of differentially expressed genes to increase power in clinical and biological studies across different sets of data. Recently, meta-analysis approaches have been applied to large collections of microarray datasets to investigate molecular commonalities of multiple cancer types not only to find the common molecular pathways in tumour development but also to compare the individual datasets to other cancer datasets to identify new sets of genes. Several investigators agree that microarray results should be validated. One commonly used method is quantitative reverse transcription PCR (qRT-PCR) to validate the expression profiles of the target genes obtained through microarray experiments, qRT-PCR is attractive for clinical use, since it can be automated and performed on fresh or archived formalin-fixed, paraffin-embedded tissue samples. The outcome of these analyses might accelerate the application of basic research findings into daily clinical practice through translational research and may have an impact on foreseeing the clinical outcome, predicting tumour response to specific therapy, identification of new prognostic biomarkers, discovering targets for the development of novel therapies and providing further insights into tumour biology. [ABSTRACT FROM AUTHOR]

Cervical squamous cell carcinoma (SCC) is one of the common cancer types among women. MicroRNAs (miRNAs) are small non-coding RNAs that play an important role in the formation and development of many cancer types by regulating expression of their targets. While many studies have investigated the relationship between miRNAs and cervical cancer, no robust miRNA biomarkers have been defined yet for diagnosis of cervical lesions. In this study, we performed a statistical meta-analysis to identify miRNAs and a class compassion analysis to evaluate mRNAs with the power to discriminate between normal, intraepithelial lesions and invasive cancer samples. Differentially expressed (DE) mRNAs were compared with the targets of meta-miRNAs. After bioinfomatics analysis and qRT-PCR validations with cytology samples and FFPE tissues, we defined miR-25 and its target KLF4 (Kruppel-like factor 4) as candidate biomarkers for in vitro studies. Our results showed that miR-25 expression was significantly higher in precancerous lesions and invasive carcinoma while presenting consistent expression patterns in both cytological and FFPE tissue samples. In line with this, its direct target KLF4 expression decreased in precancerous lesions in cytological samples and also in the invasive cancer group in FFPE tissues. Furthermore, in vitro studies showed that mir-25 inhibition decreased proliferation and motility of HeLa cells and promoted an increase in the protein level of KLF4. We conclude that inhibition of miR-25 may upregulate KLF4 expression and regulate cell proliferation and motility in cervical cancer., (Copyright © 2021 Elsevier GmbH. All rights reserved.)

Breast cancer is one of the leading causes of morbidity and mortality that is in need of novel diagnostics and therapeutics. Meta-analysis of microarray data offers promise to combine studies and provide more robust results. We report here a molecular classification of pathological subtypes (estrogen receptor [ER], progesterone receptor [PR], and Human Epidermal Growth Factor Receptor 2 [HER2]) of breast cancers with microRNA (miRNA)-dependent signatures. A ranking-based meta-analysis approach was applied to eight independent microarray data sets and meta-miRNA lists were obtained that are specific to each breast cancer subtype. The comparison of the lists with miRCancer and the PhenomiR 2.0 databases pointed out nine prominent miRNAs: let-7b-5p, let-7c-5p, let-7e-5p, miR-130a-3p, miR-30a-5p, miR-92a-1-5p, miR-211-5p, miR-500a-3p, and miR-516b-3p. Further analysis conducted with the TCGA data showed that these miRNAs can differentiate tumors from normal samples as well as discriminate the molecular subtypes of breast cancer. According to the PAM50 classification, three of these miRNAs (let-7b-5p, let-7c-5p, and miR-30a-5p) downregulated significantly, whereas miR-130a-3p, miR-92a-1-5p, miR-211-5p, and miR-500a-3p upregulated in tumors from the luminal A to the basal-like subtypes. When the prominent meta-miRNAs and their targets were analyzed, they appeared to be taking part in important signaling pathways in cancer such as the PI3K-Akt signaling and the p53 signaling pathways. Furthermore, the regulatory genes, which are key players for ER, PR, and ErBb signaling pathways, were found to be under control of several meta-miRNAs. These meta-miRNAs and the genes they are regulating offer new promise for future translational research and potential targets for precision medicine diagnostics.

Background: A T-to-G polymorphism (SNP309) at the promoter region of MDM2 has been recently reported to extend the Sp1 binding site that positively regulates the MDM2 transcription level and consequently, its expression level. MDM2 is the negative regulator of p53 tumor suppressor protein and elevated levels of MDM2 hamper the stress response driven by the p53 pathway. Whether MDM2-SNP309 was associated with breast cancer as a predisposing factor was investigated., Patients and Methods: A case-control study of 223 females diagnosed with breast cancer and 149 female controls sampled from the Turkish population was carried out and the T/G MDM2-SNP309 genotype of participants was determined., Results: There was no significant association of the G/G or G/T genotypes with breast cancer risk (odds ratio (OR) 1.14, 95% confidence interval (CI) 0.59-2.22, and OR 1.20, 95% CI 0.67-2.12, respectively). Stratification of the data for onset age or for menopausal status at the time of diagnosis also revealed no association for either group.