Rabbit Anti-Human Integrin α3 (a.a. 482-531) Polyclonal antibody

Background: Chemosensitivity and tumor metastasis are two primary issues in cancer management. Cancer cells often exhibit a wide range of sensitivity to anti-cancer compounds. To gain insight on the genetic mechanism of drug sensitivity, one powerful approach is to employ the panel of 60 human cancer cell lines developed by the National Cancer Institute (NCI). Cancer cells also show a broad range of invasion ability. However, a genome-wide portrait on the contributing molecular factors to invasion heterogeneity is lacking. Methods: Our lab performed an invasion assay on the NCI-60 panel. We identified invasion-associated (IA) genes by correlating our invasion profiling data with the Affymetrix gene expression data on NCI-60. We then employed the recently released chemosensitivity data of 99 anti-cancer drugs of known mechanism to investigate the gene-drug correlation, focusing on the IA genes. Afterwards, we collected data from four independent drug-testing experiments to validate our findings on compound response prediction. Finally, we obtained published clinical and molecular data from two recent adjuvant chemotherapy cohorts, one on lung cancer and one on breast cancer, to test the performance of our gene signature for patient outcome prediction. Results: First, we found 633 IA genes from the invasion-gene expression correlation study. Then, for each of the 99 drugs, we obtained a subset of IA genes whose expression levels correlated with drug-sensitivity profiles. We identified a set of eight genes (EGFR, ITGA3, MYLK, RAI14, AHNAK, GLS, IL32 and NNMT) showing significant gene-drug correlation with paclitaxel, docetaxel, erlotinib, everolimus and dasatinib. This eight-gene signature (derived from NCI-60) for chemosensitivity prediction was validated by a total of 107 independent drug tests on 78 tumor cell lines, most of which were outside of the NCI-60 panel. The eight-gene signature predicted relapse-free survival for the lung and breast cancer patients (log-rank P = 0.0263; 0.00021). Multivariate Cox regression yielded a hazard ratio of our signature of 5.33 (95% CI = 1.76 to 16.1) and 1.81 (95% CI = 1.19 to 2.76) respectively. The eight-gene signature features the cancer hallmark epidermal growth factor receptor (EGFR) and genes involved in cell adhesion, migration, invasion, tumor growth and progression. Conclusions: Our study sheds light on the intricate three-way interplay among gene expression, invasion and compound-sensitivity. We report the finding of a unique signature that predicts chemotherapy survival for both lung and breast cancer. Augmenting the NCI-60 model with in vitro characterization of important phenotype-like invasion potential is a cost-effective approach to power the genomic chemosensitivity analysis.

In this study, we examined the molecular mechanism of thyroid carcinoma (THCA) using bioinformatics. RNA-sequencing data of THCA (N = 498) and normal thyroid tissue (N = 59) were downloaded from The Cancer Genome Atlas. Next, gene expression levels were calculated using the TCC package and differentially expressed genes (DEGs) were identified using the edgeR package. A co-expression network was constructed using the EBcoexpress package and visualized by Cytoscape, and functional and pathway enrichment of DEGs in the co-expression network was analyzed with DAVID and KOBAS 2.0. Moreover, modules in the co-expression network were identified and annotated using MCODE and BiNGO plugins. Small-molecule drugs were analyzed using the cMAP database, and miRNAs and transcription factors regulating DEGs were identified by WebGestalt. A total of 254 up-regulated and 59 down-regulated DEGs were identified between THCA samples and controls. DEGs enriched in biological process terms were related to cell adhesion, death, and growth and negatively correlated with various small-molecule drugs. The co-expression network of the DEGs consisted of hub genes (ITGA3, TIMP1, KRT19, and SERPINA1) and one module (JUN, FOSB, and EGR1). Furthermore, 5 miRNAs and 5 transcription factors were identified, including E2F, HSF2, and miR-26. miR-26 may participate in THCA by targeting CITED1 and PLA2R1; E2F may participate in THCA by regulating ITGA3, TIMP1, KRT19, EGR1, and JUN; HSF2 may be involved in THCA development by regulating SERPINA1 and FOSB; and small-molecule drugs may have anti-THCA effects. Our results provide novel directions for mechanistic studies and drug design of THCA.