Recurrence‐associated gene signature optimizes recurrence‐free survival prediction of colorectal cancer

Abstract

High throughput gene expression profiling has showed great promise in providing insight into molecular mechanisms. Metastasis‐related mRNAs may potentially enrich genes with the ability to predict cancer recurrence, therefore we attempted to build a recurrence‐associated gene signature to improve prognostic prediction of colorectal cancer (CRC). We identified 2848 differentially expressed mRNAs by analyzing CRC tissues with or without metastasis. For the selection of prognostic genes, a LASSO Cox regression model (least absolute shrinkage and selection operator method) was employed. Using this method, a 13‐mRNA signature was identified and then validated in two independent Gene Expression Omnibus cohorts. This classifier could successfully discriminate the high‐risk patients in discovery cohort [hazard ratio (HR) = 5.27, 95% confidence interval (CI) 2.30–12.08, P < 0.0001). Analysis in two independent cohorts yielded consistent results (GSE14333: HR = 4.55, 95% CI 2.18–9.508, P < 0.0001; GSE33113: HR = 3.26, 95% CI 2.16–9.16, P = 0.0176). Further analysis revealed that the prognostic value of this signature was independent of tumor stage, postoperative chemotherapy and somatic mutation. Receiver operating characteristic (ROC) analysis showed that the area under ROC curve of this signature was 0.8861 and 0.8157 in the discovery and validation cohort, respectively. A nomogram was constructed for clinicians, and did well in the calibration plots. Furthermore, this 13‐mRNA signature outperformed other known gene signatures, including oncotypeDX colon cancer assay. Single‐sample gene‐set enrichment analysis revealed that a group of pathways related to drug resistance, cancer metastasis and stemness were significantly enriched in the high‐risk patients. In conclusion, this 13‐mRNA signature may be a useful tool for prognostic evaluation and will facilitate personalized management of CRC patients.

Recurrence-Associated Multi-RNA Signature to Predict Disease-Free Survival for Ovarian Cancer Patients

“…is theory was validated in our study and previous studies: extensive evidence had suggested that miR-186 was a tumor suppressor gene, with a downregulated expression level in cancer tissues, cells [30,31], and blood of recurrent oral squamous cell carcinoma patients compared with controls [32], including OvCa [33]. Overexpression of miR-186 into cancer cells significantly inhibited proliferation, invasion, metastasis [29,34,35], and induced mesenchymal-to-epithelial transition, G1 cell cycle arrest, and cell apoptosis [33]. STEAP2 (Seven Transmembrane Epithelial Antigen of the Prostate 2) is a gene primarily expressed in the prostate and the ovary is the only other area with a significant expression [36].…”

“…us, there is a strong need to explore more effective prognostic biomarkers to independently or jointly identify the recurrence risk and prognosis of patients. Recently, several studies on other cancers had demonstrated that the risk score established by molecular signature had a similar or even higher accuracy for prognostic prediction than clinical features and the AUC (and/or C-index) was the largest for the combination of them [11,28,29]. Hereby, we also calculated the AUC (and/or C-index) of our multi-RNA-based risk score and independent pathologic stage factor.…”

OmixLitMiner: A Bioinformatics Tool for Prioritizing Biological Leads from ‘Omics Data Using Literature Retrieval and Data Mining

“…OmixLitMiner is also compatible with genomics data. We demonstrated this with an analysis of a publication from Tian et al [15], who detected a 13 panel gene signature for colorectal cancer recurrence. The input as well as output files from OmixLitMiner are shown in Supplementary Files S9-S12.…”

“…The presented results were obtained on 13.03.2019 and are expected to change as the PubMed library grows. We also included OmixLitMiner search results from a cancer genomics publication [15] to demonstrate the tool's compatibility with this data type.…”

A combination of glioma in vivo imaging and in vivo drug delivery by metal–organic framework based composite nanoparticles

“…[8][9][10][11] On account that the ZIFs have the properties of porosity and large specific surface area, they have attracted researchers' attention in loading and releasing drugs. 7,[12][13][14] Compared to traditional drug carriers, such as liposomes, protein nanoparticles, polymer nanoparticles, metal nanoparticles and dendrimers, [15][16][17][18] nano-ZIFs improve the bioavailability of the drug, prolong the circulation time of the drug in vivo, reduce the side effects caused by the high concentration of the drug, and exhibit better targeting ability. [19][20][21] In addition, imidazole is a component of human amino acids, while Zn 2+ is a trace element of the human body.…”