A transcriptome based molecular classification scheme for cholangiocarcinoma and subtype-derived prognostic biomarker.
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SUBMITTER: Fan Z
PROVIDER: S-EPMC10784309 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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A transcriptome based molecular classification scheme for cholangiocarcinoma and subtype-derived prognostic biomarker.
Fan Zhongqi Z Zou Xinchen X Wang Guangyi G Liu Yahui Y Jiang Yanfang Y Wang Haoyan H Zhang Ping P Wei Feng F Du Xiaohong X Wang Meng M Sun Xiaodong X Ji Bai B Hu Xintong X Chen Liguo L Zhou Peiwen P Wang Duo D Bai Jing J Xiao Xiao X Zuo Lijiao L Xia Xuefeng X Yi Xin X Lv Guoyue G
Nature communications 20240111 1
Previous studies on the molecular classification of cholangiocarcinoma (CCA) focused on certain anatomical sites, and disregarded tissue contamination biases in transcriptomic profiles. We aim to provide universal molecular classification scheme and prognostic biomarker of CCAs across anatomical locations. Comprehensive bioinformatics analysis is performed on transcriptomic data from 438 CCA cases across various anatomical locations. After excluding CCA tumors showing normal tissue expression pa ...[more]
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Project description:IntroductionSmall-duct (SD) and large-duct (LD) subtypes of cholangiocarcinoma have been investigated for their prognostic factors. This study aimed to evaluate the diagnostic value of liver magnetic resonance imaging (MRI) in differentiating SD and LD types of intrahepatic cholangiocarcinoma (iCCA) and its prognostic value in predicting survival outcomes.MethodsOne hundred forty patients with surgically confirmed iCCAs (93 SD type and 47 LD type) who had available preoperative gadoxetic acid-enhanced liver MR images were retrospectively included. MRI features suggestive of the LD type over the SD type were analyzed using multivariate logistic analyses. Postoperative recurrence-free survival (RFS) and overall survival (OS) for 107 patients with available survival data were compared according to MRI features.ResultsMRI features suggestive of the LD type included infiltrative contour (odds ratio [OR] 14.2, 95% confidence interval [CI]: 2.5-81.7, p = 0.003), diffuse biliary dilatation (OR 9.7, 95% CI: 1.2-76.9, p = 0.032), no arterial phase hyperenhancement (OR 17.8, 95% CI: 2.7-118.6, p = 0.003), and vascular invasion (OR 4.5, 95% CI: 1.3-15.4, p = 0.018). When two or more features were combined, sensitivity was 59.6% (28/47), and specificity was 95.7% (89/93) in discriminating the LD type. RFS/OS was significantly shorter in patients with two or more MRI features, compared to those with none or one (310 days vs. 529 days, p = 0.011/964 days vs. 2,023 days, p = 0.010).ConclusionsPreoperative liver MRI may help predict the pathological subtype of iCCAs as either the SD type or LD type, allowing preoperative identification of patients with poorer survival outcomes.
| S-EPMC9218635 | biostudies-literature
Project description:Transcriptomic profiling Background Cholangiocarcinoma accounts for 5-10% of primary hepatic cancers. The etiology is unclear and patients are often diagnosed without risk factors. Resection is the only curative treatment although patients frequently remain undiagnosed until advanced stage of disease. Methods To construct molecular classification of cholangiocarcinoma, we profiled the transcriptomes of 104 freshly-frozen tumors and 59 matched non-cancerous livers obtained from Australia, Europe and the United States. We also performed mutational analysis of KRAS, EGFR and BRAF, and used laser-capture microdissection to obtain independent gene expression profiles for epithelial and stromal compartments in a subset of tumors. The selected target genes were validated by western blotting and immunohistochemistry. Results Transcriptomic profiling classified cholangiocarcinoma into two distinct subclasses defined by survival (P<0.0007) and early recurrence (P<0.001). Applying leave-one-out cross-validation, we optimized the prognostic classifier to 238 genes which were positively enriched in the epithelial tumor compartment. A deregulated HER2 network was associated with the epithelial compartment which also showed a frequent overexpression of Ki67, EGFR, MET and pRPS6 whereas inflammatory cytokines were enriched in tumor stroma specifically in patients with poor prognosis. KRAS mutations were found in 24.6% of patients with poor disease outcome. Conclusion Our study presents new insights into pathogenesis of cholangiocarcinoma and stratification of the patients according to survival and recurrence. Identification of a subgroup of patients among the poor prognostic cohort characterized by KRAS mutations and oncogenic-addiction may provide a novel therapeutic opportunity for this treatment-refractory malignancy. Profiling of individual cholangiocarcinomas and non-cancerous matched surrounding livers using normal bile ducts as reference
2012-02-25 | E-GEOD-26566 | biostudies-arrayexpress
Project description:Transcriptomic profiling Background Cholangiocarcinoma accounts for 5-10% of primary hepatic cancers. The etiology is unclear and patients are often diagnosed without risk factors. Resection is the only curative treatment although patients frequently remain undiagnosed until advanced stage of disease. Methods To construct molecular classification of cholangiocarcinoma, we profiled the transcriptomes of 104 freshly-frozen tumors and 59 matched non-cancerous livers obtained from Australia, Europe and the United States. We also performed mutational analysis of KRAS, EGFR and BRAF, and used laser-capture microdissection to obtain independent gene expression profiles for epithelial and stromal compartments in a subset of tumors. The selected target genes were validated by western blotting and immunohistochemistry. Results Transcriptomic profiling classified cholangiocarcinoma into two distinct subclasses defined by survival (P<0.0007) and early recurrence (P<0.001). Applying leave-one-out cross-validation, we optimized the prognostic classifier to 238 genes which were positively enriched in the epithelial tumor compartment. A deregulated HER2 network was associated with the epithelial compartment which also showed a frequent overexpression of Ki67, EGFR, MET and pRPS6 whereas inflammatory cytokines were enriched in tumor stroma specifically in patients with poor prognosis. KRAS mutations were found in 24.6% of patients with poor disease outcome. Conclusion Our study presents new insights into pathogenesis of cholangiocarcinoma and stratification of the patients according to survival and recurrence. Identification of a subgroup of patients among the poor prognostic cohort characterized by KRAS mutations and oncogenic-addiction may provide a novel therapeutic opportunity for this treatment-refractory malignancy.
2012-02-25 | GSE26566 | GEO
Project description: Not available
| S-DIXA-D-1079 | biostudies-other
Project description:BackgroundDeep learning (DL) has shown promising results in molecular-based classification of glioma subtypes from MR images. DL requires a large number of training data for achieving good generalization performance. Since brain tumor datasets are usually small in size, combination of such datasets from different hospitals are needed. Data privacy issue from hospitals often poses a constraint on such a practice. Federated learning (FL) has gained much attention lately as it trains a central DL model without requiring data sharing from different hospitals.MethodWe propose a novel 3D FL scheme for glioma and its molecular subtype classification. In the scheme, a slice-based DL classifier, EtFedDyn, is exploited which is an extension of FedDyn, with the key differences on using focal loss cost function to tackle severe class imbalances in the datasets, and on multi-stream network to exploit MRIs in different modalities. By combining EtFedDyn with domain mapping as the pre-processing and 3D scan-based post-processing, the proposed scheme makes 3D brain scan-based classification on datasets from different dataset owners. To examine whether the FL scheme could replace the central learning (CL) one, we then compare the classification performance between the proposed FL and the corresponding CL schemes. Furthermore, detailed empirical-based analysis were also conducted to exam the effect of using domain mapping, 3D scan-based post-processing, different cost functions and different FL schemes.ResultsExperiments were done on two case studies: classification of glioma subtypes (IDH mutation and wild-type on TCGA and US datasets in case A) and glioma grades (high/low grade glioma HGG and LGG on MICCAI dataset in case B). The proposed FL scheme has obtained good performance on the test sets (85.46%, 75.56%) for IDH subtypes and (89.28%, 90.72%) for glioma LGG/HGG all averaged on five runs. Comparing with the corresponding CL scheme, the drop in test accuracy from the proposed FL scheme is small (-1.17%, -0.83%), indicating its good potential to replace the CL scheme. Furthermore, the empirically tests have shown that an increased classification test accuracy by applying: domain mapping (0.4%, 1.85%) in case A; focal loss function (1.66%, 3.25%) in case A and (1.19%, 1.85%) in case B; 3D post-processing (2.11%, 2.23%) in case A and (1.81%, 2.39%) in case B and EtFedDyn over FedAvg classifier (1.05%, 1.55%) in case A and (1.23%, 1.81%) in case B with fast convergence, which all contributed to the improvement of overall performance in the proposed FL scheme.ConclusionThe proposed FL scheme is shown to be effective in predicting glioma and its subtypes by using MR images from test sets, with great potential of replacing the conventional CL approaches for training deep networks. This could help hospitals to maintain their data privacy, while using a federated trained classifier with nearly similar performance as that from a centrally trained one. Further detailed experiments have shown that different parts in the proposed 3D FL scheme, such as domain mapping (make datasets more uniform) and post-processing (scan-based classification), are essential.
| S-EPMC10242007 | biostudies-literature
Project description:Cholangiocarcinoma is a common malignancy with increasing incidence worldwide. Most patients are diagnosed at the advanced stage with poor survival rate. Laminin subunit γ2 (LAMC2) is a heparin binding-associated gene involved in tumorigenesis and has been implicated in the prognosis of various types of cancers. However, it is unclear whether expression of LAMC2 is associated with the clinical outcome of patients with cholangiocarcinoma. In the present study, the role and prognostic value of LAMC2 expression in patients with cholangiocarcinoma was investigated. Clinical information and pathological characteristics were analyzed and the association between LAMC2 expression and clinical characteristics, pathological findings and patient outcomes, including metastasis-free and disease-specific survival, were investigated. Data from 182 patients with cholangiocarcinoma were evaluated. High LAMC2 expression was associated with higher tumor stage (P<0.001), large duct type (P=0.024) and poor histological grade (P=0.002). Kaplan-Meier analysis showed high LAMC2 expression was associated with lower overall (P=0.003), disease-specific (P=0.0025), local recurrence-free (P<0.0001) and metastasis-free survival (P<0.0001). Moreover, multivariate analysis demonstrated that increased LAMC2 expression was a significant predictive risk factor for overall [hazard ratio (HR) 1.713; P=0.034], disease-specific (HR 2.011; P=0.039), local recurrence-free (HR 2.721; P<0.001) and metastasis-free survival (HR 3.117; P<0.001). Gene enrichment analysis using Gene Ontology showed that terms associated with LAMC2 upregulation were 'regulation of platelet-derived growth factor receptor-βsignaling pathway' and 'platelet-derived growth factor receptor-β signaling pathway'. The present study indicated that LAMC2 was upregulated in cholangiocarcinoma tumor tissue and had an inverse association with overall, disease-specific, local recurrence-free and metastasis-free survival in patients with cholangiocarcinoma. These results suggested that LAMC2 may serve as a potential biomarker for cholangiocarcinoma.
| S-EPMC10655064 | biostudies-literature
Project description:AbstractCholangiocarcinoma (CCA) is one of the most common malignant tumors. Although gene-targeted therapies have significantly improved the outcome of many cancers, the results are still not satisfactory for patients with CCA. Owing to the lack of an effective biomarker for guiding clinical treatment and monitoring prognosis in patients with CCA, the purpose of this study was to identify a new biomarker that could help predict the outcome of patients with CCA using bioinformatics tools.Gene expression data were collected from three publicly available datasets, comprising 263 patients with CCA and 22 healthy controls. Differentially expressed genes were obtained using the limma package (FDR < 0.05, |Log2FC|>1), and the respective protein-protein interaction revealed five relevant genes in the STRING dataset (TOP2A, BUB1, RRM2, TYMS, and KIF4A). The immunohistochemistry and PCR were used to analyze the difference in KIF4A expression in CCA.Kinesin Family Member 4A (KIF4A) was the only gene significantly associated with overall patient survival (P .035), with higher KIF4A expression being associated with poor survival rates. Moreover, KIF4A was significantly correlated with the infiltration of activated memory T cells (P = .0198) and activated mast cells (P = .008) in the tumor microenvironment. Increase in KIF4A expression affected the infiltration degree of the immune cells, which may be involved in the regulation of immune tolerance by CCA cells. The results indicated that the expression of KIF4A in CCA was higher than that in paracancerous tissues.Taken together, these findings suggest that KIF4A could be a potential new biomarker in CCA for predicting the response of patients to targeted immunotherapies.
| S-EPMC8154480 | biostudies-literature
Project description:Genome-wide expression analysis of 182 extrahepatic cholangiocarcinoma and 38 non-tumoral bile duct samples as part of a integrated study of gene expression and targeted DNA-sequencing in patients with extrahepatic cholangiocarcinoma We used whole-genome transcriptome to conduct an unsupervised molecular classification of extrahepatic cholangiocarcinoma
2020-03-22 | GSE132305 | GEO
Project description:Molecular subtyping of cancer is a critical step towards more individualized therapy and provides important biological insights into cancer heterogeneity. Although gene expression signature-based classification has been widely demonstrated to be an effective approach in the last decade, the widespread implementation has long been limited by platform differences, batch effects, and the difficulty to classify individual patient samples. Here, we describe a novel supervised cancer classification framework, deep cancer subtype classification (DeepCC), based on deep learning of functional spectra quantifying activities of biological pathways. In two case studies about colorectal and breast cancer classification, DeepCC classifiers and DeepCC single sample predictors both achieved overall higher sensitivity, specificity, and accuracy compared with other widely used classification methods such as random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), and multinomial logistic regression algorithms. Simulation analysis based on random subsampling of genes demonstrated the robustness of DeepCC to missing data. Moreover, deep features learned by DeepCC captured biological characteristics associated with distinct molecular subtypes, enabling more compact within-subtype distribution and between-subtype separation of patient samples, and therefore greatly reduce the number of unclassifiable samples previously. In summary, DeepCC provides a novel cancer classification framework that is platform independent, robust to missing data, and can be used for single sample prediction facilitating clinical implementation of cancer molecular subtyping.
| S-EPMC6697729 | biostudies-literature
Project description:Consensus molecular subtyping is an RNA expression-based classification system for colorectal cancer (CRC). Genomic alterations accumulate during CRC pathogenesis, including the premalignant adenoma stage, leading to changes in RNA expression. Only a minority of adenomas progress to malignancies, a transition that is associated with specific DNA copy number aberrations or microsatellite instability (MSI). We aimed to investigate whether colorectal adenomas can already be stratified into consensus molecular subtype (CMS) classes, and whether specific CMS classes are related to the presence of specific DNA copy number aberrations associated with progression to malignancy. RNA sequencing was performed on 62 adenomas and 59 CRCs. MSI status was determined with polymerase chain reaction-based methodology. DNA copy number was assessed by low-coverage DNA sequencing (n = 30) or array-comparative genomic hybridisation (n = 32). Adenomas were classified into CMS classes together with CRCs from the study cohort and from The Cancer Genome Atlas (n = 556), by use of the established CMS classifier. As a result, 54 of 62 (87%) adenomas were classified according to the CMS. The CMS3 'metabolic subtype', which was least common among CRCs, was most prevalent among adenomas (n = 45; 73%). One of the two adenomas showing MSI was classified as CMS1 (2%), the 'MSI immune' subtype. Eight adenomas (13%) were classified as the 'canonical' CMS2. No adenomas were classified as the 'mesenchymal' CMS4, consistent with the fact that adenomas lack invasion-associated stroma. The distribution of the CMS classes among adenomas was confirmed in an independent series. CMS3 was enriched with adenomas at low risk of progressing to CRC, whereas relatively more high-risk adenomas were observed in CMS2. We conclude that adenomas can be stratified into the CMS classes. Considering that CMS1 and CMS2 expression signatures may mark adenomas at increased risk of progression, the distribution of the CMS classes among adenomas is consistent with the proportion of adenomas expected to progress to CRC. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
| S-EPMC6221003 | biostudies-literature