Microarray Analysis for Differentially Expressed Genes Between Stromal and Epithelial Cells in Development and Metastasis of Invasive Breast Cancer

In vitro model of retinoblastoma derived tumor and stromal cells for tumor microenvironment (TME) studies

Retinoblastoma (RB) is an intraocular tumor arising from retinal cone progenitor cells affecting young children. In the last couple of years, RB treatment evolved towards eye preserving therapies. Therefore, investigating intratumoral differences and the RB tumor microenvironment (TME), regulating tumorigenesis and metastasis, is crucial. How RB cells and their TME are involved in tumor development needs to be elucidated using in vitro models including RB derived stromal cells. In the study presented, we established primary RB derived tumor and stromal cell cultures and compared them by RNAseq analysis to identify their gene expression signatures. RB tumor cells cultivated in serum containing medium were more differentiated compared to RB tumor cells grown in serum-free medium displaying a stem cell like phenotype. In addition, we identified differentially expressed genes for RB tumor and stromal derived cells. Furthermore, we immortalized cells of a RB1 mutated, MYCN amplified and trefoil factor family peptid 1 (TFF1) positive RB tumor and RB derived non-tumor stromal tissue. We characterized both immortalized cell lines using a human oncology proteome array, immunofluorescence staining of different markers and in vitro cell growth analyses. Tumor formation of the immortalized RB tumor cell line was investigated in a chicken chorioallantoic membrane (CAM) model. Our studies revealed that the RB stromal derived cell line comprises tumor associated macrophages (TAMs), glia and cancer associated fibroblasts (CAFs), we were able to successfully separate via magnetic cell separation (MACS). For co-cultivation studies, we established a 3D spheroid model with RB tumor and RB derived stromal cells. In summary, we established an in vitro model system to investigate the interaction of RB tumor cells with their TME. Our findings contribute to a better understanding of the relationship between RB tumor malignancy and its TME and will facilitate the development of effective treatment options for eye preserving therapies.

Screening Nonlinear miRNA Features of Breast Cancer by Using Ensemble Regularized Polynomial Logistic Regression

Differentiating breast cancer subtypes based on miRNA data helps doctors provide more personalized treatment plans for patients. This paper explored the interaction between miRNA pairs and developed a novel ensemble regularized polynomial logistic regression method for screening nonlinear features of breast cancer. Three different types of second-order polynomial logistic regression with elastic network penalty (SOPLR-EN) in which each type contains 10 identical models were integrated to determine the most suitable sample set for feature screening by using bootstrap sampling strategy. A single feature and 39 nonlinear features were obtained by screening features that appeared at least 15 times in 30 integrations and were involved in the classification of at least 4 subtypes. The second-order polynomial logistic regression with ridge penalty (SOPLR-R) built on screened feature set achieved 82.30% classification accuracy for distinguishing breast cancer subtypes, surpassing the performance of other six methods. Further, 11 nonlinear miRNA biomarkers were identified, and their significant relevance to breast cancer was illustrated through six types of biological analysis.

Identification and validation of key genes in gastric cancer: insights from in silico analysis, clinical samples, and functional assays

INTRODUCTION

The underlying mechanisms of gastric cancer (GC) remain unknown. Therefore, in this study, we employed a comprehensive approach, combining computational and experimental methods, to identify potential key genes and unveil the underlying pathogenesis and prognosis of GC.

METHODS

Gene expression profiles from GEO databases (GSE118916, GSE79973, and GSE29272) were analyzed to identify DEGs between GC and normal tissues. A PPI network was constructed using STRING and Cytoscape, followed by hub gene identification with CytoHubba. Investigations included expression and promoter methylation analysis, survival modeling, mutational and miRNA analysis, gene enrichment, drug prediction, and in vitro assays for cellular behaviors.

RESULTS

A total of 83 DEGs were identified in the three datasets, comprising 41 up-regulated genes and 42 down-regulated genes. Utilizing the degree and MCC methods, we identified four hub genes that were hypomethylated and up-regulated: COL1A1, COL1A2, COL3A1, and FN1. Subsequent validation of their expression and promoter methylation on clinical GC samples through targeted bisulfite sequencing and RT-qPCR analysis further confirmed the hypomethylation and overexpression of these genes in local GC patients. Furthermore, it was observed that these hub genes regulate tumor proliferation and metastasis in in vivo and exhibited mutations in GC patients.

CONCLUSION

We found four potential diagnostic and prognostic biomarkers, including COL1A1, COL1A2, COL3A1, and FN1 that may be involved in the occurrence and progression of GC.

Predicting the molecular mechanism-driven progression of breast cancer through comprehensive network pharmacology and molecular docking approach

Identification of key regulators is a critical step toward discovering biomarker that participate in BC. A gene expression dataset of breast cancer patients was used to construct a network identifying key regulators in breast cancer. Overexpressed genes were identified with BioXpress, and then curated genes were used to construct the BC interactome network. As a result of selecting the genes with the highest degree from the BC network and tracing them, three of them were identified as novel key regulators, since they were involved at all network levels, thus serving as the backbone. There is some evidence in the literature that these genes are associated with BC. In order to treat BC, drugs that can simultaneously interact with multiple targets are promising. When compared with single-target drugs, multi-target drugs have higher efficacy, improved safety profile, and are easier to administer. The haplotype and LD studies of the FN1 gene revealed that the identified variations rs6707530 and rs1250248 may both cause TB, and endometriosis respectively. Interethnic differences in SNP and haplotype frequencies might explain the unpredictability in association studies and may contribute to predicting the pharmacokinetics and pharmacodynamics of drugs using FN1.

Bioinformatics and pathway enrichment analysis identified hub genes and potential biomarker for gastric cancer prognosis

Introduction

Gastric cancer is one of the most common cancers in the world. This study aimed to identify genes, biomarkers, and metabolic pathways affecting gastric cancer using bioinformatic analysis and meta-analysis.

Methods

Datasets containing gene expression profiles of tumor lesions and adjacent non-tumor mucosa samples were downloaded. Common differentially expressed genes between data sets were selected to identify hub genes and further analysis. Gene Expression Profiling and Interactive Analyses (GEPIA) and the Kaplan-Meier method were used to further validate the expression level of genes and plot the overall survivalcurve, respectively.

Results and disscussion

KEGG pathway analysis showed that the most important pathway was enriched in ECM-receptor interaction. Hub genes includingCOL1A2, FN1, BGN, THBS2, COL5A2, COL6A3, SPARC and COL12A1 wereidentified. The top interactive miRNAs including miR-29a-3p, miR-101-3p,miR-183-5p, and miR-15a-5p targeted the most hub genes. The survival chart showed an increase in mortality in patients with gastric cancer, which shows the importance of the role of these genes in the development of the disease and can be considered candidate genes in the prevention and early diagnosis of gastric cancer.

Statistics and network-based approaches to identify molecular mechanisms that drive the progression of breast cancer

Breast cancer (BC) is one of the most malignant tumors and the leading cause of cancer-related death in women worldwide. So, an in-depth investigation on the molecular mechanisms of BC progression is required for diagnosis, prognosis and therapies. In this study, we identified 127 common differentially expressed genes (cDEGs) between BC and control samples by analyzing five gene expression profiles with NCBI accession numbers GSE139038, GSE62931, GSE45827, GSE42568 and GSE54002, based-on two statistical methods LIMMA and SAM. Then we constructed protein-protein interaction (PPI) network of cDEGs through the STRING database and selected top-ranked 7 cDEGs (BUB1, ASPM, TTK, CCNA2, CENPF, RFC4, and CCNB1) as a set of key genes (KGs) by cytoHubba plugin in Cytoscape. Several BC-causing crucial biological processes, molecular functions, cellular components, and pathways were significantly enriched by the estimated cDEGs including at-least one KGs. The multivariate survival analysis showed that the proposed KGs have a strong prognosis power of BC. Moreover, we detected some transcriptional and post-transcriptional regulators of KGs by their regulatory network analysis. Finally, we suggested KGs-guided three repurposable candidate-drugs (Trametinib, selumetinib, and RDEA119) for BC treatment by using the GSCALite online web tool and validated them through molecular docking analysis, and found their strong binding affinities. Therefore, the findings of this study might be useful resources for BC diagnosis, prognosis and therapies.

Radiation increases COL1A1, COL3A1, and COL1A2 expression in breast cancer

Background

Radiotherapy-associated secondary cancer is an important issue for the treatment of breast cancer (BCa). This study aimed to investigate the molecular mechanism and genetic risk factors for radiation-associated secondary diseases in BCa.

Methods

The differentially expressed genes (DEGs) between preradiation and postradiation BCa samples in the GSE65505 dataset were obtained. The pathways related to the radiation-associated DEGs in the protein–protein interaction (PPI) network modules were identified. miRNAs targeted to the key genes in the PPI network were identified, and their association with BCa prognosis was analyzed.

Results

A total of 136 radiation-associated DEGs preradiation and postradiation BCa samples were screened out. The PPI network consisted of a significant module that consisted of 21 upregulated DEGs that were associated with “hsa04512: ECM–receptor interaction,” “hsa04151: PI3K-Akt signaling pathway,” and “hsa04115: p53 signaling pathway.” Sixteen DEGs, including three collagen genes collagen type I alpha 1 chain (COL1A1), COL3A1, and COL1A2, were enriched in 17 radiation-associated pathways. The three genes were upregulated in BCa tissues compared with controls and were also elevated by radiation. They were targeted by hsa-miR-29a/c, and the expression levels of hsa-miR-29a/c were associated with a poor prognosis of BCa.

Conclusions

The upregulation of COL1A1, COL3A1, and COL1A2 might be genetic risk factors for radiation-associated secondary diseases in BCa.

Global signalling network analysis of luminal T47D breast cancer cells in response to progesterone

BACKGROUND

Breast cancer cells enter into the cell cycle following progestin exposure by the activation of signalling cascades involving a plethora of enzymes, transcription factors and co-factors that transmit the external signal from the cell membrane to chromatin, ultimately leading to a change of the gene expression program. Although many of the events within the signalling network have been described in isolation, how they globally team up to generate the final cell response is unclear.

METHODS

In this study we used antibody microarrays and phosphoproteomics to reveal a dynamic global signalling map that reveals new key regulated proteins and phosphor-sites and links between previously known and novel pathways. T47D breast cancer cells were used, and phospho-sites and pathways highlighted were validated using specific antibodies and phenotypic assays. Bioinformatic analysis revealed an enrichment in novel signalling pathways, a coordinated response between cellular compartments and protein complexes.

RESULTS

Detailed analysis of the data revealed intriguing changes in protein complexes involved in nuclear structure, epithelial to mesenchyme transition (EMT), cell adhesion, as well as transcription factors previously not associated with breast cancer cell proliferation. Pathway analysis confirmed the key role of the MAPK signalling cascade following progesterone and additional hormone regulated phospho-sites were identified. Full network analysis shows the activation of new signalling pathways previously not associated with progesterone signalling in T47D breast cancer cells such as ERBB and TRK. As different post-translational modifications can mediate complex crosstalk mechanisms and massive PARylation is also rapidly induced by progestins, we provide details of important chromatin regulatory complexes containing both phosphorylated and PARylated proteins.

CONCLUSIONS

This study contributes an important resource for the scientific community, as it identifies novel players and connections meaningful for breast cancer cell biology and potentially relevant for cancer management.