L1TD1 - a prognostic marker for colon cancer (original) (raw)
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World Journal of Surgical Oncology, 2021
Background Colon cancer is a worldwide leading cause of cancer-related mortality, and the prognosis of colon cancer is still needed to be improved. This study aimed to construct a prognostic model for predicting the prognosis of colon cancer. Methods The gene expression profile data of colon cancer were obtained from the TCGA, GSE44861, and GSE44076 datasets. The WGCNA module genes and common differentially expressed genes (DEGs) were used to screen out the prognosis-associated DEGs, which were used to construct a prognostic model. The performance of the prognostic model was assessed and validated in the TCGA training and microarray validation sets (GSE38832 and GSE17538). At last, the model and prognosis-associated clinical factors were used for the construction of the nomogram. Results Five colon cancer-related WGCNA modules (including 1160 genes) and 1153 DEGs between tumor and normal tissues were identified, inclusive of 556 overlapping DEGs. Stepwise Cox regression analyses ide...
Identification of upstream regulators for prognostic expression signature genes in colorectal cancer
BMC Systems Biology, 2013
Background: Gene expression signatures have been commonly used as diagnostic and prognostic markers for cancer subtyping. However, expression signatures frequently include many passengers, which are not directly related to cancer progression. Their upstream regulators such as transcription factors (TFs) may take a more critical role as drivers or master regulators to provide better clues on the underlying regulatory mechanisms and therapeutic applications. Results: In order to identify prognostic master regulators, we took the known 85 prognostic signature genes for colorectal cancer and inferred their upstream TFs. To this end, a global transcriptional regulatory network was constructed with total >200,000 TF-target links using the ARACNE algorithm. We selected the top 10 TFs as candidate master regulators to show the highest coverage of the signature genes among the total 846 TF-target sub-networks or regulons. The selected TFs showed a comparable or slightly better prognostic performance than the original 85 signature genes in spite of greatly reduced number of marker genes from 85 to 10. Notably, these TFs were selected solely from inferred regulatory links using gene expression profiles and included many TFs regulating tumorigenic processes such as proliferation, metastasis, and differentiation. Conclusions: Our network approach leads to the identification of the upstream transcription factors for prognostic signature genes to provide leads to their regulatory mechanisms. We demonstrate that our approach could identify upstream biomarkers for a given set of signature genes with markedly smaller size and comparable performances. The utility of our method may be expandable to other types of signatures such as diagnosis and drug response.
arXiv (Cornell University), 2023
Colon cancer is a prevalent gastrointestinal malignancy arising in the colon. Ulcerative colitis(UC) is one of the risk factors of colorectal cancer. The detection of under-expressed biomarkers and molecular mechanisms in UC and colon cancer can lead to effective management of colitis-associated cancer. A total of two mRNA expression datasets (GSE87473 and GSE44076) were downloaded from the Gene Expression Omnibus (GEO) database. GSE87473 contains 21 healthy samples, 27 extensive ulcerative colitis samples and 60 limited ulcerative colitis samples. GSE44076 contains 98 colon cancer samples and 98 healthy samples. GEO2R was used to screen differentially expressed genes (DEGs) between extensive ulcerative colitis samples and healthy samples, limited ulcerative colitis samples and healthy samples, and colon cancer samples and healthy samples. The inclusion criteria for DEGs included an adjusted p-value <0.05 and a log(2) fold change <-2. Venn diagram of DEGs was depicted for every three groups (extensive ulcerative colitis, limited ulcerative colitis and colon cancer). Protein-protein interaction (PPI) network of DEGs in every three groups was constructed using STRING online database. The DEGs of each group were imported to Cytoscape software separately and the hub genes were screened. Then, the Enrichr web server was used to perform KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses and adjusted p-value<0.05 was considered statistically significant. Furthermore, Kaplan-Meier curve in GEPIA (http://gepia.cancer-pku.cn/) was used for analyzing the overall survival (OS) of hub genes. In extensive ulcerative colitis, limited ulcerative colitis and colon cancer groups, 95,69 and 635 underexpressed genes with adjusted p-value<0.05 and log(2) fold change<-2 were detected respectively. Using Cytoscape software, the genes with degree> 15 including CLCA1, SLC26A3, SI, KIT, HPGDS, NR1H4, ADIPOQ, PPARGC1A, GCG, MS4A12, GUCA2A and FABP1 were screened as hub underexpressed genes in colon cancer. In extensive ulcerative colitis, the genes with degree>5 including ABCB1, ABCG2, UGT1A6, CYP2B6 and AQP8 were identified as hub genes. Moreover, the genes including NR1H4, CYP2B6, ABCB1, ABCG2, UGT2A3 and PLA2G12B were detected as hub genes with degree>5 in limited ulcerative colitis. According to inclusion criteria and venn diagram, the downregulated gene NR1H4 was common gene in limited ulcerative colitis and colon cancer. The genes MS4A12 and GUCA2A were common genes in extensive ulcerative colitis and colon cancer. The genes CLCA1, SLC26A3, SI, KIT, HPGDS, ADIPOQ, PPARGC1A, PPARGC1A, GCG and FABP1 were distinctive genes in colon cancer. The genes UGT1A6 and CYP2B6 were common genes in extensive ulcerative colitis and limited ulcerative colitis, and the genes ABCB1, ABCG2, AQP8, and UGT2A3 were common genes in three groups. According to KEGG enrichment analysis, hub under-expressed genes in colon cancer were enriched in the pathways including Pancreatic secretion, Mineral absorption, Drug metabolism, Metabolism of xenobiotics by cytochrome P450, Arachidonic acid metabolism, Bile secretion, PPAR signaling pathway, Adipocytokine signaling pathway and cAMP signaling pathway. The hub genes in extensive and limited ulcerative colitis were enriched in Bile secretion, ABC transporters, Steroid hormone biosynthesis, Retinol metabolism, Metabolism of xenobiotics by cytochrome P450, Drug metabolism, Chemical carcinogenesis and Fat digestion and absorption. According to survival analysis, CLCA1 (Calcium-activated chloride channel regulator 1), PPARGC1A (Peroxisome proliferatoractivated receptor gamma coactivator 1-alph) and AQP8 (Aquaporin-8) were with poor overall survival. The current in silico study showed that downregulation of CLCA1, PPARGC1A and AQP8 genes may increase cancer cell invasion and metastasis ability. The recent researches showed that CLCA1 overexpression inhibited colorectal cancer aggressiveness, and overexpression of AQP8 reduced cell proliferation, migration and invasion in colon cancer. The role of downregulation of PPARGC1A gene in poor survival of patients with colon cancer has not been revealed yet.
Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics, 2007
In order to discover potential markers of prognosis in colorectal cancer (CRC) we have determined gene expression profiles, using cDNA microarrays in CRC samples obtained from 19 patients in Dukes stages C and D, with favorable clinical course (Dukes C patients, survival >5 years after surgery, group A, n = 7) or unfavorable clinical course (Dukes stage C and D patients, survival <5 years after surgery, group B, n = 12). Gene expression was measured in RNA from each tumor, using a pool of equal amounts of RNA from all tumors as a reference. To identify and rank differentially expressed genes we used three different analytical methods: (i) Significance Analysis of Microarrays (SAM), (ii) Cox's Proportional Hazard Model, and (iii) Trend Filter (a mathematical method for the assessment of numerical trends). The level of expression of a gene in an individual tumor was regarded as of interest when that gene was identified as differentially expressed by at least two of these three methods. By these stringent criteria we identified eight genes (ITGB2, MRPS11, NPR1, TXNL2, PHF10, PRSS8, KCNK3, JAK3) that were correlated with prolonged survival after surgery. Pathway analysis showed that patients with favorable prognosis had several activated metabolic pathways (carbon metabolism, transcription, amino acid and nitrogen metabolism, signaling and fibroblast growth factor receptor pathways). To further validate individual gene expression findings, the RNA level of each gene identified as a marker with microarrays was measured by real-time RT-PCR in CRC samples from an independent group of 55 patients. In this set of patients the Cox Proportional Hazard Model analysis demonstrated a significant association between increased patient survival and low expression of ITGB2 (p = 0.011) and NPR1 (p = 0.023) genes.
Prognosis of stage II colon cancer by non-neoplastic mucosa gene expression profiling
Oncogene, 2007
We have assessed the possibility to build a prognosis predictor (PP), based on non-neoplastic mucosa microarray gene expression measures, for stage II colon cancer patients. Non-neoplastic colonic mucosa mRNA samples from 24 patients (10 with a metachronous metastasis, 14 with no recurrence) were profiled using the Affymetrix HGU133A GeneChip. Patients were repeatedly and randomly divided into 1000 training sets (TSs) of size 16 and validation sets (VS) of size 8. For each TS/VS split, a 70-gene PP, identified on the TS by selecting the 70 most differentially expressed genes and applying diagonal linear discriminant analysis, was used to predict the prognoses of VS patients. Mean prognosis prediction performances of the 70-gene PP were 81.8% for accuracy, 73.0% for sensitivity and 87.1% for specificity. Informative genes suggested branching signal-transduction pathways with possible extensive networks between individual pathways. They also included genes coding for proteins involved in immune surveillance. In conclusion, our study suggests that one can build an accurate PP for stage II colon cancer patients, based on non-neoplastic mucosa microarray gene expression measures.
Prognostic and predictive potential molecular biomarkers in colon cancer
Chirurgia (Bucharest, Romania : 1990)
An important objective in nowadays research is the discovery of new biomarkers that can detect colon tumours in early stages and indicate with accuracy the status of the disease. The aim of our study was to identify potential biomarkers for colon cancer onset and progression. We assessed gene expression profiles of a list of 10 candidate genes (MMP-1, MMP-3, MMP-7, DEFA 1, DEFA-5, DEFA-6, IL-8, CXCL-1, SPP-1, CTHRC-1) by quantitative real time PCR in triplets of colonic mucosa (normal, adenoma, tumoral tissue) collected from the same patient during surgery for a group of 20 patients. Additionally we performed immunohistochemistry for DEFA1-3 and SPP1. We remarked that DEFA5 and DEFA6 are key factors in adenoma formation (p<0.05). MMP7 is important in the transition from a benign to a malignant status (p <0.01) and further in metastasis being a prognostic indicator for tumor transformation and for the metastatic potential of cancer cells. IL8, irrespective of tumor stage, has a...
Frontiers in Cell and Developmental Biology, 2021
Various factors affect the prognosis of patients with colon cancer. Complicated factors are found to be conducive to accurate assessment of prognosis. In this study, we developed a series of prognostic prediction models for survival time of colon cancer patients after surgery. Analysis of nine clinical characteristics showed that the most important factor was the positive lymph node ratio (LNR). High LNR was the most important clinical factor affecting 1- and 3-year survival; M0&age < 70 was the most important feature for 5 years. The performance of the model was improved through the integration of clinical characteristics and four types of molecule features (mRNA, lncRNA, miRNA, DNA methylation). The model provides guidance for clinical practice. According to the high-risk molecular features combined with age ≥ 70&T3, poorly differentiated or undifferentiated, M0&well differentiated, M0&T2, LNR high, T4&poorly differentiated, or undifferentiated, the survival time may be less th...
A 19-Gene expression signature as a predictor of survival in colorectal cancer
BMC Medical Genomics, 2016
Background: Histopathological assessment has a low potential to predict clinical outcome in patients with the same stage of colorectal cancer. More specific and sensitive biomarkers to determine patients' survival are needed. We aimed to determine gene expression signatures as reliable prognostic marker that could predict survival of colorectal cancer patients with Dukes' B and C. Methods: We examined microarray gene expression profiles of 78 archived tissues of patients with Dukes' B and C using the Illumina DASL assay. The gene expression data were analyzed using the GeneSpring software and R programming. Results: The outliers were detected and replaced with randomly chosen genes from the 90 % confidence interval of the robust mean for each group. We performed three statistical methods (SAM, LIMMA and t-test) to identify significant genes. There were 19 significant common genes identified from microarray data that have been permutated 100 times namely NOTCH2,
Clinical value of prognosis gene expression signatures in colorectal cancer: a systematic review
PloS one, 2012
The traditional staging system is inadequate to identify those patients with stage II colorectal cancer (CRC) at high risk of recurrence or with stage III CRC at low risk. A number of gene expression signatures to predict CRC prognosis have been proposed, but none is routinely used in the clinic. The aim of this work was to assess the prediction ability and potential clinical usefulness of these signatures in a series of independent datasets. A literature review identified 31 gene expression signatures that used gene expression data to predict prognosis in CRC tissue. The search was based on the PubMed database and was restricted to papers published from January 2004 to December 2011. Eleven CRC gene expression datasets with outcome information were identified and downloaded from public repositories. Random Forest classifier was used to build predictors from the gene lists. Matthews correlation coefficient was chosen as a measure of classification accuracy and its associated p-value...