Molecular classification of colorectal cancer using the gene expression profile of tumor samples.
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ABSTRACT:
SUBMITTER: Rashid M
PROVIDER: S-EPMC6879775 | biostudies-literature | 2019 Sep
REPOSITORIES: biostudies-literature
Publications
Molecular classification of colorectal cancer using the gene expression profile of tumor samples.
Rashid Mamoon M Vishwakarma Ramesh K RK Deeb Ahmad M AM Hussein Mohamed A MA Aziz Mohammad A MA
Experimental biology and medicine (Maywood, N.J.) 20190515 12
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Project description:BackgroundCombining clinical and molecular data types may potentially improve prediction accuracy of a classifier. However, currently there is a shortage of effective and efficient statistical and bioinformatic tools for true integrative data analysis. Existing integrative classifiers have two main disadvantages: First, coarse combination may lead to subtle contributions of one data type to be overshadowed by more obvious contributions of the other. Second, the need to measure both data types for all patients may be both unpractical and (cost) inefficient.ResultsWe introduce a novel classification method, a stepwise classifier, which takes advantage of the distinct classification power of clinical data and high-dimensional molecular data. We apply classification algorithms to two data types independently, starting with the traditional clinical risk factors. We only turn to relatively expensive molecular data when the uncertainty of prediction result from clinical data exceeds a predefined limit. Experimental results show that our approach is adaptive: the proportion of samples that needs to be re-classified using molecular data depends on how much we expect the predictive accuracy to increase when re-classifying those samples.ConclusionsOur method renders a more cost-efficient classifier that is at least as good, and sometimes better, than one based on clinical or molecular data alone. Hence our approach is not just a classifier that minimizes a particular loss function. Instead, it aims to be cost-efficient by avoiding molecular tests for a potentially large subgroup of individuals; moreover, for these individuals a test result would be quickly available, which may lead to reduced waiting times (for diagnosis) and hence lower the patients distress. Stepwise classification is implemented in R-package stepwiseCM and available at the Bioconductor website.
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Project description:This study aimed to explore the underlying molecular mechanisms of colorectal cancer (CRC) using bioinformatics analysis. Using GSE4107 datasets downloaded from the Gene Expression Omnibus, the differentially expressed genes (DEGs) were screened by comparing the RNA expression from the colonic mucosa between 12 CRC patients and ten healthy controls using a paired t-test. The Gene Ontology (GO) functional and pathway enrichment analyses of DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) software followed by the construction of a protein-protein interaction (PPI) network. In addition, hub gene identification and GO functional and pathway enrichment analyses of the modules were performed. A total of 612 up- and 639 downregulated genes were identified. The upregulated DEGs were mainly involved in the regulation of cell growth, migration, and the MAPK signaling pathway. The downregulated DEGs were significantly associated with oxidative phosphorylation, Alzheimer's disease, and Parkinson's disease. Moreover, FOS, FN1, PPP1CC, and CYP2B6 were selected as hub genes in the PPI networks. Two modules (up-A and up-B) in the upregulated PPI network and three modules (d-A, d-B, and d-C) in the downregulated PPI were identified with the threshold of Molecular Complex Detection (MCODE) Molecular Complex Detection (MCODE) score ≥4 and nodes ≥6. The genes in module up-A were significantly enriched in neuroactive ligand-receptor interactions and the calcium signaling pathway. The genes in module d-A were enriched in four pathways, including oxidative phosphorylation and Parkinson's disease. DEGs, such as FOS, FN1, PPP1CC, and CYP2B6, may be used as potential targets for CRC diagnosis and treatment.
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Project description:Analysis of 4 pairs of colorectal cancer tissue and paraneoplastic tissue. The fresh frozen samples were obtained during surgical procedures. Results provide insight into molecular mechanisms underlying malignancy.
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Project description:Cancer is a serious public health concern worldwide and is the leading cause of death. Blood cancer is one of the most dangerous types of cancer. Leukemia is a type of cancer that affects the blood cell and bone marrow. Acute leukemia is a chronic condition that is fatal if left untreated. A timely, reliable, and accurate diagnosis of leukemia at an early stage is critical to treating and preserving patients' lives. There are four types of leukemia, namely acute lymphocytic leukemia, acute myelogenous leukemia, chronic lymphocytic in extracting, and chronic myelogenous leukemia. Recognizing these cancerous development cells is often done via manual analysis of microscopic images. This requires an extraordinarily skilled pathologist. Leukemia symptoms might include lethargy, a lack of energy, a pale complexion, recurrent infections, and easy bleeding or bruising. One of the challenges in this area is identifying subtypes of leukemia for specialized treatment. This Study is carried out to increase the precision of diagnosis to assist in the development of personalized plans for treatment, and improve general leukemia-related healthcare practises. In this research, we used leukemia gene expression data from Curated Microarray Database (CuMiDa). Microarrays are ideal for studying cancer, however, categorizing the expression pattern of microarray information can be challenging. This proposed study uses feature selection methods and machine learning techniques to predict and classify subtypes of leukemia in gene expression data CuMiDa (GSE9476). This research work utilized linear programming (LP) as a machine-learning technique for classification. Linear programming model classifies and predicts the subtypes of leukemia Bone_Marrow_CD34, Bone Marrow, AML, PB, and PBSC CD34. Before using the LP model, we selected 25 features from the given dataset of 22283 features. These 25 significant features were the most distinguishing for classification. The classification accuracy of this work is 98.44%.
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Project description:BackgroundColorectal cancer develops through two main genetic instability pathways characterized by distinct pathologic features and clinical outcome.ResultsWe investigated colon cancer samples (23 characterized by microsatellite stability, MSS, and 16 by high microsatellite instability, MSI-H) for genome-wide expression of microRNA (miRNA) and mRNA. Based on combined miRNA and mRNA gene expression, a molecular signature consisting of twenty seven differentially expressed genes, inclusive of 8 miRNAs, could correctly distinguish MSI-H versus MSS colon cancer samples. Among the differentially expressed miRNAs, various members of the oncogenic miR-17-92 family were significantly up-regulated in MSS cancers. The majority of protein coding genes were also up-regulated in MSS cancers. Their functional classification revealed that they were most frequently associated with cell cycle, DNA replication, recombination, repair, gastrointestinal disease and immune response.ConclusionThis is the first report that indicates the existence of differences in miRNA expression between MSS versus MSI-H colorectal cancers. In addition, the work suggests that the combination of mRNA/miRNA expression signatures may represent a general approach for improving bio-molecular classification of human cancer.
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Project description:Ferroptosis is a non-apoptotic form of cell death recognized in recent years. Nonetheless, the potential role of ferroptosis-associated genes in immune regulation and tumor microenvironment formation remains unknown. In this study, we characterized the ferroptosis-associated patterns of colorectal cancer through integrative analyses of multiple datasets with transcriptomics, genomics, and single-cell transcriptome profiling. Three distinct ferroptosis-associated clusters (FAC1, FAC2 and FAC3) were identified from 1251 CRC bulk samples, which were associated with different clinical outcomes and biological pathways. The TME characterization revealed that the three patterns were highly consistent with known immune profiles: immune-desert (FAC1), immune-inflamed (FAC2) and immune-excluded (FAC3), respectively. Ferroptosis-associated immune and stromal-activated genes were obtained and characterized by corresponding function in CRC tumorigenesis. Further single-cell analyses identified the ferroptosis-associated immune responding tumor cells and ferroptosis-associated stromal cells infiltration pattern. Based on the Fersig score, which was extracted from the ferroptosis phenotype-related signature, patients with lower Fersig score were characterized by prolonged survival time and effective immune responses. Collectively, we uncovered the ferroptosis-associated patterns associated with TME diversity and immune response phenotype. The Fersig we constructed could be the potential therapeutic target genes to improve the efficacy of patients' immunotherapy. The Fersig scoring scheme could enhance the understanding of TME infiltration associated with ferroptosis and prediction of immunotherapy efficacy.
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Project description:High Gleason score is currently the best prognostic indicator for poor prognosis in prostate cancer. However, a significant number of patients with low Gleason scores develop aggressive disease as well. In an effort to understand molecular signatures associated with poor outcome in prostate cancer, we analyzed a microarray dataset characterizing 281 prostate cancers from a Swedish watchful-waiting cohort. Patients were classified on the basis of their mRNA microarray signature profiles indicating embryonic stem cell expression patterns (stemness), inactivation of the tumor suppressors p53 and PTEN, activation of several oncogenic pathways, and the TMPRSS2-ERG fusion. Unsupervised clustering identified a subset of tumors manifesting stem-like signatures together with p53 and PTEN inactivation, which had very poor survival outcome, a second group with intermediate survival outcome, characterized by the TMPRSS2-ERG fusion, and three groups with benign outcome. The stratification was validated on a second independent dataset of 150 tumor and metastatic samples from a clinical cohort at Memorial Sloan-Kettering Cancer Center. This classification is independent of Gleason score and therefore provides useful unique molecular profiles for prostate cancer prognosis, helping to predict poor outcome in patients with low or average Gleason scores.
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Project description:The high heterogeneity of colorectal cancer (CRC) is the main clinical challenge for individualized therapies. Molecular classification will contribute to drug discovery and personalized management optimizing. Here, we aimed to characterize the molecular features of CRC by a classification system based on metabolic gene expression profiles. 435 CRC samples from the Genomic Data Commons data portal were chosen as training set while 566 sample in GSE39582 were selected as testing set. Then, a non-negative matrix factorization clustering was performed, and three subclasses of CRC (C1, C2, and C3) were identified in both training set and testing set. Results showed that subclass C1 displayed high metabolic activity and good prognosis. Subclass C2 was associated with low metabolic activities and displayed high immune signatures as well as high expression of immune checkpoint genes. C2 had the worst prognosis among the three subtypes. Subclass C3 displayed intermediate metabolic activity, high gene mutation numbers and good prognosis. Finally, a 27-gene metabolism-related signature was identified for prognosis prediction. Our works deepened the understanding of metabolic hallmarks of CRC, and provided valuable information for "multi-molecular" based personalized therapies.
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Project description:BackgroundIt is unclear whether the transcriptional subtypes of high grade serous ovarian cancer (HGSOC) apply to high grade clear cell (HGCCOC) or high grade endometrioid ovarian cancer (HGEOC). We aim to delineate transcriptional profiles of HGCCOCs and HGEOCs.MethodsWe used Agilent microarrays to determine gene expression profiles of 276 well annotated ovarian cancers (OCs) including 37 HGCCOCs and 66 HGEOCs. We excluded low grade OCs as these are known to be distinct molecular entities. We applied the prespecified TCGA and CLOVAR gene signatures using consensus non-negative matrix factorization (NMF).ResultsWe confirm the presence of four TCGA transcriptional subtypes and their significant prognostic relevance (p<0.001) across all three histological subtypes (HGSOC, HGCCOC and HGEOCs). However, we also demonstrate that 22/37 (59%) HGCCOCs and 30/67 (45%) HGEOCs form 2 additional separate clusters with distinct gene signatures. Importantly, of the HGCCOC and HGEOCs that clustered separately 62% and 65% were early stage (FIGO I/II), respectively. These finding were confirmed using the reduced CLOVAR gene set for classification where most early stage HGCCOCs and HGEOCs formed a distinct cluster of their own. When restricting the analysis to the four TCGA signatures (ssGSEA or NMF with CLOVAR genes) most early stage HGCCOCs and HGEOC were assigned to the differentiated subtype.ConclusionsUsing transcriptional profiling the current study suggests that HGCCOCs and HGEOCs of advanced stage group together with HGSOCs. However, HGCCOCs and HGEOCs of early disease stages may have distinct transcriptional signatures similar to those seen in their low grade counterparts.
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Project description:BackgroundDetailed molecular evaluation of cytology and limited tissue samples is increasingly becoming the standard for cancer care. Reproducible and accurate diagnostic approaches with reduced demands on cellularity are an ongoing unmet need. This study evaluated the performance of a 92-gene assay for molecular diagnosis of tumor type/subtype in cytology and limited tissue samples.MethodsClinical validation of accuracy for the 92-gene assay in limited tissue samples such as cytology cell blocks, core biopsies and small excisions was conducted in a blinded multi-institutional study (N = 109, 48% metastatic, 53% grade II and III). Analytical success rate and diagnostic utility were evaluated in a consecutive series of 644 cytology cases submitted for clinical testing.ResultsThe 92-gene assay demonstrated 91% sensitivity (95% CI [0.84, 0.95]) for tumor classification, with high accuracy maintained irrespective of specimen type (100%, 92%, and 86% in FNA/cytology cell blocks, core biopsies, and small excisions, respectively; p = 0.26). The assay performed equally well for metastatic versus primary tumors (90% vs 93%, p = 0.73), and across histologic grades (100%, 90%, 89%, in grades I, II, and III, respectively; p = 0.75). In the clinical case series, a molecular diagnosis was reported in 87% of the 644 samples, identifying 23 different tumor types and allowing for additional mutational analysis in selected cases.ConclusionsThese findings demonstrate high accuracy and analytical success rate of the 92-gene assay, supporting its utility in the molecular diagnosis of cancer for specimens with limited tissue.
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