No difference in CCND1 gene expression between breast cancer patients with and without lymph node metastasis in a Southern Brazilian sample

Genetic and expression variations of cell cycle pathway genes in brain tumor patients

The present study was designed to determine the association between the genetic polymorphisms/expression variations of RB1 and CCND1 genes and brain tumor risk. For this purpose, 250 blood samples of brain tumor patients along with 250 controls (cohort I) and 96 brain tumor tissues (cohort II) with adjacent control section were collected. Mutation analysis of RB1 (rs137853294, rs121913300) and CCND1 (rs614367, rs498136) genes was performed using ARMS-PCR followed by sequencing, and expression analysis was performed using real-time PCR and immunohistochemistry. The results showed homozygous mutant genotype of RB1 gene polymorphism, rs121913300 (P=0.003) and CCND1 gene polymorphism rs614367 (P=0.01) were associated significantly with brain tumor risk. Moreover, significant down-regulation of RB1 (P=0.005) and up-regulation of CCND1 (P=0.0001) gene was observed in brain tumor sections vs controls. Spearman correlation showed significant negative correlation between RB1 vs proliferation marker, Ki-67 (r = -0.291*, P<0.05) in brain tumors. Expression levels of selected genes were also assessed at protein level using immunohistochemical analysis (IHC) and signification down-regulation of RB1 (P=0.0001) and up-regulation of CCND1 (P=0.0001) was observed in brain tumor compared with control sections. In conclusion, it is suggested that polymorphisms/expression variations of RB1 and CCND1 genes may be associated with increased risk of brain tumor.

Exploring Mechanism of Key Chinese Herbal Medicine on Breast Cancer by Data Mining and Network Pharmacology Methods

OBJECTIVE

To screen the key Chinese Herbal Medicines (KCHMs) against breast cancer by data mining, and analyze the potential mechanism of KCHMs using network pharmacology method.

METHODS

Clinical prescriptions consisted of CHMs for treating breast cancer were screened, and then Traditional Chinese Medicine Inheritance Support System (TCMISS) was applied to obtain the KCHMs. Subsequently, active ingredients and corresponding target genes of KCHMs were searched by Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, and target genes of breast cancer were collected using OMIM and MalaCards. After that, the overlapping target genes of KCHMs and breast cancer were screened, and the protein-protein interaction (PPI) network was built. In addition, a network of "KCHMs-active ingredients-breast cancer-targets" was constructed by Cytoscape 3.7.1. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis were performed with Database for Annotation, Visualization and Integrated Discovery (DAVID) database to reveal the action mechanism of KCHMs.

RESULTS

A total of 7 KCHMs were identified, whose active ingredients include quercetin, luteolin, nobiletin, kaempferol, isorhamnetin, naringenin, and be-ta-sitosterol, etc. Based on protein-protein interaction analysis, core targets were ESR1, MYC, CCND1, EGFR, CASP3, ERBB2, etc. Several KEGG pathways (e.g, PI3K-Akt, p53, ErbB, and HIF-1 signaling pathways) were found.

CONCLUSION

Based on the combination of the data mining method and network pharmacology approach, the therapeutic effect of KCHMs on breast cancer may be realized by acting on target genes and signaling pathways related to the formation and progression of breast cancer.

Association of FOSL1 copy number alteration and triple negative breast tumors

Copy number alterations (CNAs) are a frequent feature in human breast cancer, and one of the hallmarks of genomic instability. The FOSL1, GSTP1 and CCND1 genes are located at 11q13, a cytoband commonly affected by CNA in breast cancer, with relevant function in progression and invasion. Our main goal was to analyze CNAs of these genes and determine their association with breast cancer subtypes. Seventy-three cases of invasive breast tumors [52 Luminal, 7 HER2+ and 14 triple negative (TNBC) subtypes] were analyzed by TaqMan assays. CNAs were observed for all genes, with gains more frequently observed. Gains of the FOSL1 gene were observed in 71% of the cases. This gene was the only one with a statistically significant difference (p<0.001) among tumor subtypes, with increased copy number in TNBC compared to luminal and HER2+. No significant association of CNA and clinical and histopathological parameters from the patients was observed. Additional studies in larger breast cancer patient cohorts based on more refined molecular subtypes are necessary to confirm the observed association of FOSL1 gain with aggressive breast tumors phenotypes.

Subpathway-LNCE: Identify dysfunctional subpathways competitively regulated by lncRNAs through integrating lncRNA-mRNA expression profile and pathway topologies

Recently, studies have reported that long noncoding RNAs (lncRNAs) can act as modulators of mRNAs through competitively binding to microRNAs (miRNAs) and have relevance to tumorigenesis as well as other diseases. Identify lncRNA competitively regulated subpathway not only can gain insight into the initiation and progression of disease, but also help for understanding the functional roles of lncRNAs in the disease context. Here, we present an effective method, Subpathway-LNCE, which was specifically designed to identify lncRNAs competitively regulated functions and the functional roles of these competitive regulation lncRNAs have not be well characterized in diseases. Moreover, the method integrated lncRNA-mRNA expression profile and pathway topologies. Using prostate cancer datasets and LUAD data sets, we confirmed the effectiveness of our method in identifying disease associated dysfunctional subpathway that regulated by lncRNAs. By analyzing kidney renal clear cell carcinoma related lncRNA competitively regulated subpathway network, we show that Subpathway-LNCE can help uncover disease key lncRNAs. Furthermore, we demonstrated that our method is reproducible and robust. Subpathway-LNCE provide a flexible tool to identify lncRNA competitively regulated signal subpathways underlying certain condition, and help to expound the functional roles of lncRNAs in various status. Subpathway-LNCE has been developed as an R package freely available at https://cran.rstudio.com/web/packages/SubpathwayLNCE/.