BackgroundAn accurate and robust gene signature is of the utmost importance in assisting oncologists to make a more accurate evaluation in clinical practice. In our study, we extracted key mRNAs significantly related to colorectal cancer (CRC) prognosis and we constructed an expression-based gene signature to predict CRC patients’ survival.MethodsmRNA expression profiles and clinicopathological data of colon adenocarcinoma (COAD) cases and rectum adenocarcinoma (READ) were collected from The Cancer Genome Atlas database to investigate gene expression alteration associated to the prognosis of CRC. Differentially expressed mRNAs (DEMs) were detected between COAD/READ and normal tissue samples. Relying on a univariate and multivariate Cox regression analyses, a mRNA panel signature was established and used for predicting the overall survival (OS) in CRC patients. Receiver operating characteristic curve was used to evaluate the prognosis performance of our model through calculating the AUC values corresponding to the 3-year and 5-year survival. To assess the performance of gene signature in the given cancer subgroups (CRC entire cohort, COAD cohort, and READ cohort), a stratified analysis was carried out according to clinical factors.ResultsA total of 5341 and 5594 DEMs were collected from COAD vs. normal tissue samples, and READ vs. normal samples respectively. A univariate regression analysis for the common DEMs between COAD and READ cohorts resulted in 14 common mRNAs related to OS. The multivariate Cox regression analysis revealed that 6 of these mRNAs (EPHA6, TIMP1, IRX6, ART5, HIST3H2BB, and FOXD1) had significant prognostic value allowing the discrimination between high- and low-risk patients, implying poor and good outcomes, respectively. The stratified analysis identified 6-gene signature as an independent prognostic signature in predicting CRC patients’ survival.ConclusionsThe 6-gene signature could act as an independent biomarker for survival prediction of CRC patients.Electronic supplementary materialThe online version of this article (10.1186/s12935-018-0724-7) contains supplementary material, which is available to authorized users.
Colorectal cancer (CRC) is a life-threatening disease with a poor prognosis. Therefore, it is crucial to identify molecular prognostic biomarkers for CRC. The present study aimed to identify potential key genes that could be used to predict the prognosis of patients with CRC. Three CRC microarray datasets (GSE20916, GSE73360 and GSE44861) were downloaded from the Gene Expression Omnibus (GEO) database, and one dataset was obtained from The Cancer Genome Atlas (TCGA) database. The three GEO datasets were analyzed to detect differentially expressed genes (DEGs) using the BRB-ArrayTools software. Functional and pathway enrichment analyses of these DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery tool. A protein-protein interaction (PPI) network of DEGs was constructed, hub genes were extracted, and modules of the PPI network were analyzed. To investigate the prognostic values of the hub genes in CRC, data from the CRC datasets of TCGA were used to perform the survival analyses based on the sample splitting method and Cox regression model. Correlation among the hub genes was evaluated using Spearman's correlation analysis. In the three GEO datasets, a total of 105 common DEGs were identified, including 51 down- and 54 up-regulated genes in CRC compared with normal colorectal tissues. A PPI network consisting of 100 DEGs and 551 edges was constructed, and 44 nodes were identified as hub genes. Among these 44 genes, the four hub genes TIMP metallopeptidase inhibitor 1 (TIMP1), solute carrier family 4 member 4 (SLC4A4), aldo-keto reductase family 1 member B10 (AKR1B10) and ATP binding cassette subfamily E member 1 (ABCE1) were associated with overall survival (OS) in patients with CRC. Three significant modules were extracted from the PPI network. The hub gene TIMP1 was present in Module 1, ABCE1 was involved in Module 2 and SLC4A4 was identified in Module 3. Univariate analysis revealed that TIMP1, SLC4A4, AKR1B10 and ABCE1 were associated with the OS of patients with CRC. Multivariate analysis demonstrated that SLC4A4 may be an independent prognostic factor associated with OS. Furthermore, the results from correlation analysis revealed that there was no correlation between TIMP1, SLC4A4 and ABCE1, whereas AKR1B10 was positively correlated with SLC4A4. In conclusion, the four key genes TIMP1, SLC4A4, AKR1B10 and ABCE1 associated with the OS of patients with CRC were identified by integrated bioinformatics analysis. These key genes may be used as prognostic biomarkers to predict the survival of patients with CRC, and may therefore represent novel therapeutic targets for CRC.
Renal cell carcinoma (RCC) is the most common adult renal epithelial cancer susceptible to metastasis and patients with irresectable RCC always have a poor prognosis. Long noncoding RNAs (lncRNAs) have recently been documented as having critical roles in the etiology of RCC. Nevertheless, the prognostic significance of lncRNA‐based signature for outcome prediction in patients with RCC has not been well investigated. Therefore, it is essential to identify a lncRNA‐based signature for predicting RCC prognosis. In the current study, we comprehensively analyzed the RNA sequencing data of the three main pathological subtypes of RCC (kidney renal clear cell carcinoma [KIRC], kidney renal papillary cell carcinoma [KIRP], and kidney chromophobe carcinoma [KICH]) from The Cancer Genome Atlas (TCGA) database, and identified a 6‐lncRNA prognostic signature with the help of a step‐wise multivariate Cox regression model. The 6‐lncRNA signature stratified the patients into low‐ and high‐risk groups with significantly different prognosis. Multivariate Cox regression analysis showed that predictive value of the 6‐lncRNA signature was independent of other clinical or pathological factors in the entire cohort and in each cohort of RCC subtypes. In addition, the three independent prognostic clinical factors (including age, pathologic stage III, and stage IV) was also stratified into low‐ and high‐risk groups basis on the risk score, and the stratification analyses demonstrated that the high‐risk score was a poor prognostic factor. In conclusion, these findings indicate that the 6‐lncRNA signature is a novel prognostic biomarker for all three subtypes of RCC, and can increase the accuracy of predicting overall survival.
Background: Radiotherapy is an important locoregional treatment, and its effect on triple-negative breast cancer (TNBC) needs to be enhanced. The aim of the present study was to investigate the potential effects of XRCC4 on radiosensitivity of TNBC. Methods: The RNAi technique was implemented to establish the TNBC stable cell line with XRCC4 knockdown. MTT assay was used to detect the effect of XRCC4 knockdown on cell proliferation. Western blot and immunohistochemistry assays were employed to identify protein expression. Colony assay was performed to detect the effect of XRCC4 knockdown on the colony formation ability of TNBC cells with radiation treatment. Comet assay was conducted to evaluate the influence of XRCC4 silencing on DNA repair activity in ionizing radiation. In addition, we performed a survival analysis based on data in TCGA database. Results: XRCC4 knockdown by lentivirus-mediated shRNA had no significant effect on proliferation of TNBC cells. Knockdown of XRCC4 could substantially increase the sensitivity of TNBC cells to ionizing radiation. The DNA damage level was detected to be increased in the XRCC4 knockdown group, indicating there was a significant repair delay in the XRCC4-deleted cells. Clinical sample analysis exhibited that there were various XRCC4 expression in different patients with TNBC. Moreover, survival analysis showed that high expression of XRCC4 was significantly associated with poor progression-free survival after radiotherapy in TNBC patients. Conclusion: Our findings suggest that XRCC4 knockdown sensitizes TNBC cells to ionizing radiation, and could be considered as a novel predictor of radiosensitivity and a promising target for TNBC.
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