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 was employed. Using this method, a 13-mRNA signature was identified and then validated in two independent Gene Expression Omnibus (GEO) cohorts. This classifier could successfully discriminate the high-risk patients in discovery cohort (HR = 5.27, 95%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 receiver operating characteristic curve (AUC) of this signature was 0.8861 and 0.8157 in the discovery and validation cohort, respectively. A nomogram was constructed for clinicians, which 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 (ssGSEA) 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.
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 was employed. Using this method, a 13-mRNA signature was identified and then validated in two independent Gene Expression Omnibus (GEO) cohorts. This classifier could successfully discriminate the high-risk patients in discovery cohort (HR = 5.27, 95%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 receiver operating characteristic curve (AUC) of this signature was 0.8861 and 0.8157 in the discovery and validation cohort, respectively. A nomogram was constructed for clinicians, which 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 (ssGSEA) 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.
Abstract Esophageal cancer is one of the malignant tumors in the digestive system. Because the early symptoms of esophageal cancer are occult and lack effective screening of specific molecular markers of esophageal cancer, most patients are in the middle or advanced stage at the time of treatment, and the 5-year survival rate is low. This study aimed to find molecular biomarkers of clinical value in the development and diagnosis of esophageal cancer. The factors affecting esophageal cancer were identified by clinical factor analysis and tissue transcriptome sequencing of 180 cases of esophageal cancer in Jiangsu, China. The results of the Chi-square test and correlation analysis demonstrated that: a). relative expression of KRT17 was higher in esophageal cancer with diabetes ( P = 0.036); b). expression of KRT17 correlated with blood glucose levels ( r = 0.186, P = 0.013) and tumor size ( r = -0.197, P = 0.009) in esophageal cancer patients; c). and expression of COL1A1 correlated with age ( r = -0.148, P = 0.047) and blood glucose levels ( r = 0.212, P = 0.004) in esophageal cancer patients; d). Experimental results of QRT-RCR: KRT17 and COL1A1 genes were highly expressed in esophageal cancer, respectively ( P < 0.05); when the two genes were used as a combination test, the positive detection rate of esophageal cancer was 90.6%, ROC curve, specificity, and sensitivity had greater power, and KRT17 and COL1A1 genes had the potential to be biomarkers for the diagnosis of esophageal cancer.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Gene expression profiling has been used to classify molecular subtypes in colorectal cancer (CRC). Given that tumour transcriptome signals not only derive from cancer cells but also from tumour microenvironment. Recent studies have shown that noncancerous components might affect the classification of CRC subtypes. We hypothesised that using stroma-specific gene signature would be more effective to identify CRC subtypes with clinical relevance.
Methods
To this end, we analysed gene expression profiles from 1821 CRCs. We firstly constructed a signature where genes were both stroma-specifically expressed and associated with drug response. Further, we identified CRC stroma-specific subtypes (CRSS) using K-means clustering based on the signature and verified the classification in two datasets. We also used the nearest template prediction algorithm to predict drug response.
Results
The CRSS subtypes were associated with distinct clinicopathological, molecular and phenotypic characteristics and specific enrichments of gene signatures and signalling pathways (table 1): (i) CRSS-A: non-serrated adenomas, colon crypt top derived, glycolytic, epithelial, KRAS-mutant, sensitive to Cetuximab, enriched with NK cells; (ii) CRSS-B: non-serrated adenomas, colon crypt base derived, DNA replication activity, epithelial, BRAF wild-type, TP53 mutant, chromosomal stability, distal CRC, sensitive to Cetuximab; (iii) CRSS-C: serrated adenomas, colon crypt top derived, interleukin-6 pathway activity, epithelial, microsatellite instability (MSI), BRAF mutant, hypermutation, chromosomal instability, CpG island methylator phenotype, proximal CRC, sensitive to Gefitinib, good prognosis, enriched with cytotoxic lymphocytes and monocytic lineage; (iv) CRSS-D: non-serrated adenomas, colon crypt top derived, interleukin-2 pathway activity, epithelial-mesenchymal transition (EMT), chromosomal stability, sensitive to FOLFIRI and FOLFOX chemotherapy regimens; (v) CRSS-E: serrated adenomas, colon crypt base derived, EMT, immune pathways activation, poor prognosis, sensitive to FOLFIRI and FOLFOX, enriched with endothelial cells and fibroblasts; (vi) CRSS-F: serrated adenomas, colon crypt base derived, EMT, IGF1 pathway activity, poor prognosis, sensitive to FOLFIRI and FOLFOX, enriched with endothelial cells, fibroblasts and monocytic lineage.
Conclusions
We classified CRC into six molecular subtypes (CRSS). The identification of CRSS subtypes is critical, as it provides possibilities to identify robust prognostic models and provide more precise therapeutic options for each CRC subtype.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Summary This metadata record provides details of the data supporting the claims of the related manuscript “A tumor microenvironment specific gene expression signature predicts chemotherapy resistance in colorectal cancer patients”. The related study aimed to determine whether used tumor microenvironment (TME) specific gene signature to identify colorectal cancer (CRC) subtypes with distinctive clinical relevance was possible. Data access The data analysed during the related study were downloaded from public databases including Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) and The Cancer Genome Atlas (TCGA; TCGA CRC datasets available from the Synapse repository at: https://www.synapse.org/#!Synapse:syn2623706/files/). For a list of accession IDs for the analysed data, see Supplementary Table S1 of the manuscript, also included as part of this metadata record. The Renji RNA-seq data is available from GEO: https://identifiers.org/geo:GSE158559. The output data of the related study are included with this data record, and are as follows:- Table S1 to S10 - supplementary tables 1 to 10 for the related manuscript- Cetuximab_GSE5851.PRJEB34338.combined.Rdata - two combined CRC Cetuximab treated gene expression matrix- combined_five_GEObatch_GSE14333_GSE17536_GSE17537_GSE33113_GSE37892.Rdata - five combined CRC gene expression matrix- FOLFOX_GSE19860_GSE28702_GSE69675.Rdata - three combined CRC FOLFOX treated gene expression matrix- FOLFOX_GSE104645_GSE72970.Rdata - two combined CRC FOLFOX or FOLFIRI treated gene expression matrix- GSE39395.expMatrix.Rdata - GSE39395 gene expression matrix- GSE39396.expMatrix.Rdata - GSE39396 gene expression matrix- GSE39582_after_ComBat.Rdata - GSE39582 gene expression matrix- GSE62080_exp_pdata.Rdata - GSE62080 gene expression matrix - GSE72056.melanoma.sfm.signature.rds - scRNA melanoma processed data- GSE75688.BRCA.sfm.signature.rds - scRNA breast cancer processed data- GSE81861.sfm.signature.rds - scRNA CRC processed data- GSE103322.head-neck.sfm.signature.rds - scRNA head and neck processed data- TCGA.CRC.expMatrix.Rdata - TCGA CRC gene expression matrix- TCGA.CRC.microbiome.abundance.Rdata - TCGA CRC gut microbiome abundance
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