Introduction: Abnormal status of gene expression plays an important role in tumorigenesis, progression and metastasis of breast cancer. Mechanisms of gene silence or activation were varied. Methylation of genes may contribute to alteration of gene expression. This study aimed to identify differentially expressed hub genes which may be regulated by DNA methylation and evaluate their prognostic value in breast cancer by bioinformatic analysis. Methods: GEO2R was used to obtain expression microarray data from GSE54002, GSE65194 and methylation microarray data from GSE20713, GSE32393. Differentially expressed-aberrantly methylated genes were identified by FunRich. Biological function and pathway enrichment analysis were conducted by DAVID. PPI network was constructed by STRING and hub genes was sorted by Cytoscape. Expression and DNA methylation of hub genes was validated by UALCAN and MethHC. Clinical outcome analysis of hub genes was performed by Kaplan Meier-plotter database for breast cancer. IHC was performed to analyze protein levels of EXO1 and Kaplan-Meier was used for survival analysis. Results: 677 upregulated-hypomethylated and 361 downregulated-hypermethylated genes were obtained from GSE54002, GSE65194, GSE20713 and GSE32393 by GEO2R and FunRich. The most significant biological process, cellular component, molecular function enriched and pathway for upregulated-hypomethylated genes were viral process, cytoplasm, protein binding and cell cycle respectively. For downregulated-hypermethylated genes, the result was peptidyl-tyrosine phosphorylation, plasma membrane, transmembrane receptor protein tyrosine kinase activity and Rap1 signaling pathway (All p< 0.05). 12 hub genes (TOP2A, MAD2L1, FEN1, EPRS, EXO1, MCM4, PTTG1, RRM2, PSMD14, CDKN3, H2AFZ, CCNE2) were sorted from 677 upregulated-hypomethylated genes. 4 hub genes (EGFR, FGF2, BCL2, PIK3R1) were sorted from 361 downregulated-hypermethylated genes. Differential expression of 16 hub genes was validated in UALCAN database (p<0.05). 7 in 12 upregulated-hypomethylated and 2 in 4 downregulated-hypermethylated hub genes were confirmed to be significantly hypomethylated or hypermethylated in breast cancer using MethHC database (p<0.05). Finally, 12 upregulated hub genes (TOP2A, MAD2L1, FEN1, EPRS, EXO1, MCM4, PTTG1, RRM2, PSMD14, CDKN3, H2AFZ, CCNE2) and 3 downregulated genes (FGF2, BCL2, PIK3R1) contributed to significant unfavorable clinical outcome in breast cancer (p<0.05). High expression level of EXO1 protein was significantly associated with poor OS in breast cancer patients (p=0.03).
The heterogeneities of colorectal cancer (CRC) lead to staging inadequately of patients' prognosis. Here, we performed a prognostic analysis based on the tumor mutational profile and explored the characteristics of the high-risk tumors. We sequenced 338 colorectal carcinomas as the training dataset, constructed a novel five-gene (SMAD4, MUC16, COL6A3, FLG and LRP1B) prognostic signature, and validated it in an independent dataset from The Cancer Genome Atlas (TCGA). Kaplan-Meier and Cox regression analyses confirmed that the five-gene signature is an independent predictor of recurrence and prognosis in patients with Stage III colon cancer. The mutant signature translated to an increased risk of death (hazard ratio = 2.45, 95% confidence interval = 1.15-5.22, p = 0.016 in our dataset; hazard ratio = 4.78, 95% confidence interval = 1.33-17.16, p = 0.008 in TCGA dataset). RNA and bacterial 16S rRNA sequencing of high-risk tumors indicated that mutations of the fivegene signature may lead to intestinal barrier integrity, translocation of gut bacteria and deregulation of immune response and extracellular related genes. The high-risk tumors overexpressed IL23A and IL1RN genes and enriched with cancer-related bacteria (Bacteroides fragilis, Peptostreptococcus, Parvimonas, Alloprevotella and Gemella) compared to the low-risk tumors. The signature identified the high-risk group characterized by gut bacterial translocation and upregulation of interleukins of the tumor microenvironment, which was worth further researching.
The hypoxic microenvironment contributes to the chemoresistance of many malignant tumors including colorectal cancer (CRC). Accumulating studies have indicated that long non-coding RNAs (lncRNAs) play important roles in chemotherapy resistance. In this study, we aimed to determine the effect of lncRNAs in hypoxia-mediated resistance in CRC and its potential mechanism. Here, we discovered that hypoxia-induced oxaliplatin resistance and HOX transcript antisense RNA (HOTAIR) expression was increased in hypoxia-treated CRC cell lines and CRC tumors. Knockdown of HOTAIR by siRNA reduced the viability and proliferation of CRC cells treated with oxaliplatin and reversed hypoxia-induced resistance. Mechanically, we found that HOTAIR modulates zinc finger E-box binding homeobox 1 (ZEB1) expression by negative regulations of miR-1277-5p. When miR-1277-5p was silenced, knockdown of HOTAIR was unable to reduce the oxaliplatin resistance in CRC cells. In mouse models of CRC, HOTAIR knockdown markedly inhibited the tumor growth when treated with oxaliplatin. Thus, HOTAIR/miR-1277-5p/ZEB1 axis appears a promising therapeutic target for improving the oxaliplatin efficacy in CRC.
Dysregulation of protein tyrosine phosphatase, receptor type B (PTPRB) correlates with the development of a variety of tumors. Here we show that PTPRB promotes metastasis of colorectal cancer (CRC) cells via inducing epithelial-mesenchymal transition (EMT). We find that PTPRB is expressed at significantly higher levels in CRC tissues compared to adjacent nontumor tissues and in CRC cell lines with high invasion. PTPRB knockdown decreased the number of invasive CRC cells in an in vitro wound healing model, and also reduced tumor metastasis in vivo. Conversely, PTPRB overexpression promoted CRC cell invasion in vitro and metastasis in vivo. PTPRB overexpression decreased vimentin expression and promoted E-cadherin expression, consistent with promotion of EMT, while PTPRB knockdown had the opposite effect. Hypoxic conditions induced EMT and promoted invasion in CRC cells, but these effects were eliminated by PTPRB knockdown. EMT blockade via TWIST1 knockdown inhibited the migration and invasiveness of CRC cells, and even increased PTPRB expression could not reverse this effect. Altogether, these data support the conclusion that PTPRB promotes invasion and metastasis of CRC cells via inducing EMT, and that PTPRB would be a novel therapeutic target for the treatment of CRC.
Dear editors, Colorectal cancer (CRC) is the second leading cause of cancer deaths in developed countries [1]. The malignant transformation from small clumps to cancer takes about 10 years [2]. This study aimed to characterize proteomic dynamics associated with CRC development and progression, and identify novel therapeutic targets for intercepting the underlying oncogenic processes. We have optimized pressure cycling technology (PCT) coupled with dataindependent acquisition mass spectrometry (DIA-MS) for robust and reproducible proteomic analysis of biopsy-level formalin-fixed paraffin-embedded (FFPE) tissues [3].In this study, we profiled the proteomic tissue landscape of CRC evolving from normal colon to hyperplastic polyps, adenomas, adenocarcinoma not otherwise specified (AC) or mucinous adenocarcinoma (MC). We identified 69,949 peptides, 6,359 protein groups, and 4,830 unique proteins (Supplementary Table S1) based on our previously established spectral library for DIA analysis [4] from 170 FFPE tissue samples (85 patients, each with 2 biological replicates) (Figure 1A). Pearson's correlation coefficient between biological replicates was 0.813, and 0.953 between technical replicates.We identified 928 differentially expressed proteins by comparing protein expression in samples from different CRC clinical stages to normal colon tissue samples (Figure 1B). Pairwise comparisons between polyps and normal colon, adenomas and polyps, carcinoma and adenomas, as well as MC and AC revealed distinct proteomic changes associated with each transformation towards malignancy (Supplementary Figure S1A). Canonical pathways analysis
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