Dataset Information

Epigenome-wide development and validation of a prognostic methylation score in intrahepatic cholangiocarcinoma based on machine learning strategies.

ABSTRACT:

Background

Clinical parameter-based nomograms and staging systems provide limited information for the prediction of survival in intrahepatic cholangiocarcinoma (ICC) patients. In this study, we developed a methylation signature that precisely predicts overall survival (OS) after surgery.

Methods

An epigenome-wide study of DNA methylation based on whole-genome bisulfite sequencing (WGBS) was conducted for two independent cohorts (discovery cohort, n=164; validation cohort, n=170) from three hepatobiliary centers in China. By referring to differentially methylated regions (DMRs), we proposed the concept of prognostically methylated regions (PMRs), which were composed of consecutive prognostically methylated CpGs (PMCs). Using machine learning strategies (Random Forest and the least absolute shrinkage and selector regression), a prognostic methylation score (PMS) was constructed based on 14 PMRs in the discovery cohort and confirmed in the validation cohort.

Results

The C-indices of the PMS for predicting OS in the discovery and validation cohorts were 0.79 and 0.74, respectively. In the whole cohort, the PMS was an independent predictor of OS [hazard ratio (HR) =8.12; 95% confidence interval (CI): 5.48-12.04; P<0.001], and the C-index (0.78) of the PMS was significantly higher than that of the Johns Hopkins University School of Medicine (JHUSM) nomogram (0.69, P<0.001), the Eastern Hepatobiliary Surgery Hospital (EHBSH) nomogram (0.67, P<0.001), American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system (0.61, P<0.001), and MEGNA prognostic score (0.60, P<0.001). The patients in quartile 4 of PMS could benefit from adjuvant therapy (AT) (HR =0.54; 95% CI: 0.32-0.91; log-rank P=0.043), whereas those in the quartiles 1-3 could not. However, other nomograms and staging system failed to do so. Further analyses of potential mechanisms showed that the PMS was associated with tumor biological behaviors, pathway activation, and immune microenvironment.

Conclusions

The PMS could improve the prognostic accuracy and identify patients who would benefit from AT for ICC patients, and might facilitate decisions in treatment of ICC patients.

SUBMITTER: Chen X

PROVIDER: S-EPMC10432305 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Publications

Epigenome-wide development and validation of a prognostic methylation score in intrahepatic cholangiocarcinoma based on machine learning strategies.

Chen Xing X Dong Liangqing L Chen Lu L Wang Yuan Y Du Jinpeng J Ma Lijie L Yan Xiaokai X Huang Jiwei J Liao Mingheng M Chen Xiangzheng X Liu Dongming D Li Jin J Zhang Bo B Teng Wen W Yuan Kefei K Sun Deqiang D Gao Qiang Q Zeng Yong Y

Hepatobiliary surgery and nutrition 20220422 4

<h4>Background</h4>Clinical parameter-based nomograms and staging systems provide limited information for the prediction of survival in intrahepatic cholangiocarcinoma (ICC) patients. In this study, we developed a methylation signature that precisely predicts overall survival (OS) after surgery.<h4>Methods</h4>An epigenome-wide study of DNA methylation based on whole-genome bisulfite sequencing (WGBS) was conducted for two independent cohorts (discovery cohort, n=164; validation cohort, n=170) f ...[more]

PMID: 37601000

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Project description:ImportanceIn patients with intrahepatic cholangiocarcinoma (ICC), the oncologic benefit of surgery and perioperative outcomes for large multifocal tumors or tumors with contiguous organ involvement remain to be defined.ObjectivesTo develop and externally validate a simplified prognostic score for ICC and to determine perioperative outcomes for large multifocal ICCs or tumors with contiguous organ involvement.Design, setting, and participantsThis study of a contemporary cohort merged data from the California Cancer Registry (January 1, 2004, through December 31, 2011) and the Office of Statewide Health Planning and Development inpatient database. Clinicopathologic variables were compared between tumors that were intrahepatic, small (<7 cm), and solitary (ISS) and those that had extrahepatic extension and were large (≥7 cm) and multifocal (ELM). External validation of the prognostic model was performed using an independent data set from the National Cancer Institute's Surveillance, Epidemiology, and End Results database from January 1, 2004, through December 31, 2013.Main outcomes and measuresPatient overall survival after hepatectomy.ResultsA total of 275 patients (123 men [44.7%] and 152 women [55.3%]; median [interquartile range] age, 65 [55-72] years) met the inclusion criteria. No significant differences in overall complication rate (ISS, 48 [34.5%]; ELM, 37 [27.2%]; P = .19) and mortality rate (ISS, 10 [7.2%]; ELM, 6 [4.4%]; P = .32) were found. A multivariate Cox proportional hazards model demonstrated that multifocality, extrahepatic extension, grade, node positivity, and age greater than 60 years are independently associated with worse overall survival. These variables were used to develop the MEGNA prognostic score. The prognostic separation/discrimination index was improved with the MEGNA prognostic score (0.21; 95% CI, 0.11-0.33) compared with the staging systems of the American Joint Committee on Cancer sixth (0.17; 95% CI, 0.09-0.29) and seventh (0.18; 95% CI, 0.08-0.30) editions.Conclusions and relevanceThe MEGNA prognostic score allows more accurate and superior estimation of patient survival after hepatectomy compared with current staging systems.

Project description:Background: Previous studies have shown that cellular senescence is strongly associated with tumorigenesis and the tumor microenvironment. Accordingly, we developed a novel prognostic signature for intrahepatic cholangiocarcinoma (ICCA) based on senescence-associated long non-coding RNAs (SR-lncRNAs) and identified a lncRNA-miRNA-mRNA axis involving in ICCA. Methods: Based on the 197 senescence-associated genes (SRGs) from Genacards and their expression in Fu-ICCA cohort, we identified 20 lncRNAs as senescence-associated lncRNAs (SR-lncRNAs) through co-expression and cox-regression analysis. According to 20 SR-lncRNAs, patients with ICCA were classified into 2 molecular subtypes using unsupervised clustering machine learning approach and to explore the prognostic and functional heterogeneity between these two subtypes. Subsequently, we integrated 113 machine learning algorithms to develop senescence-related lncRNA signature, ultimately identifying 11 lncRNAs and constructing prognostic models and risk stratification. The correlation between the signature and the immune landscape, immunotherapy response as well as drug sensitivity are explored too. Results: We developed a novel senescence related signature. The predictive model and risk score calculated by the signature exhibited favorable prognostic predictive performance, which is a suitable independent risk factor for the prognosis of patients with ICCA based on Kaplan-Meier plotter, nomogram and receiving operating characteristic (ROC) curves. The results were validated using external datasets. Estimate, ssGSEA (single sample gene set enrichment analysis), IPS (immunophenotype score) and TIDE (tumor immune dysfunction and exclusion) algorithms revealed higher immune infiltration, higher immune scores, lower immune escape potential and better response to immunotherapy in the high-risk group. In addition, signature identifies eight chemotherapeutic agents, including cisplatin for patients with different risk levels, providing guidance for clinical treatment. Finally, we identified a set of lncRNA-miRNA-mRNA axes involved in ICCA through regulation of senescence. Conclusion: SR-lncRNAs signature can favorably predict the prognosis, risk stratification, immune landscape and immunotherapy response of patients with ICCA and consequently guide individualized treatment.

Project description:BackgroundImmune function, nutrition status, and inflammation influence tumor initiation and progression. This was a retrospective multicenter cohort study that investigated the prognostic value and clinical relevance of immune-, inflammatory-, and nutritional-related biomarkers to develop a novel prognostic immune-inflammatory-nutritional score (PIIN score) for patients with intrahepatic cholangiocarcinoma (ICC).MethodsThe clinical data of 571 patients (406 in the training set and 165 in the validation set) were collected from four large hepato-pancreatico-biliary centers of patients with ICC who underwent surgical resection between January 2011 and September 2017. Twelve blood biomarkers were collected to develop the PIIN score using the LASSO Cox regression model. The predictive value was further assessed using validation datasets. Afterward, nomograms combining the PIIN score and other clinicopathological parameters were developed and validated based on the calibration curve, time-dependent AUC curves, and decision curve analysis (DCA). The primary outcomes evaluated were overall survival (OS) and recurrence-free survival (RFS) from the day of primary resection of ICC.ResultsBased on the albumin-bilirubin (ALBI) grade, neutrophil- to- lymphocyte ratio (NLR), prognostic nutritional index (PNI), and systemic immune- inflammation index (SII) biomarkers, the PIIN score that classified patients into high-risk and low-risk groups could be calculated. Patients with high-risk scores had shorter OS (training set, p < 0.001; validation set, p = 0.003) and RFS (training set, p < 0.001; validation set, p = 0.002) than patients with low-risk scores. The high PIIN score was also associated with larger tumors (≥5 cm), lymph node metastasis (N1 stage), multiple tumors, and high tumor grade or TNM (tumor (T), nodes (N), and metastases (M)) stage. Furthermore, the high PIIN score was a significant independent prognostic factor of OS and RFS in both the training (p < 0.001) and validation (p = 0.003) cohorts, respectively. A PIIN-nomogram for individualized prognostic prediction was constructed by integrating the PIIN score with the clinicopathological variables that yielded better predictive performance than the TNM stage.ConclusionThe PIIN score, a novel immune-inflammatory-nutritional-related prognostic biomarker, predicts the prognosis in patients with resected ICC and can be a reliable tool for ICC prognosis prediction after surgery. Our study findings provide novel insights into the role of cancer-related immune disorders, inflammation, and malnutrition.

Project description:Background and objectivesCurrently, the prognostic performance of the staging systems proposed by the 8th edition of the American Joint Committee on Cancer (AJCC 8th) and the Liver Cancer Study Group of Japan (LCSGJ) in resectable intrahepatic cholangiocarcinoma (ICC) remains controversial. The aim of this study was to use machine learning techniques to modify existing ICC staging strategies based on clinical data and to demonstrate the accuracy and discrimination capacity in prognostic prediction.Patients and methodsThis is a retrospective study based on 1,390 patients who underwent surgical resection for ICC at Eastern Hepatobiliary Surgery Hospital from 2007 to 2015. External validation was performed for patients from 2015 to 2017. The ensemble of three machine learning algorithms was used to select the most important prognostic factors and stepwise Cox regression was employed to derive a modified scoring system. The discriminative ability and predictive accuracy were assessed using the Concordance Index (C-index) and Brier Score (BS). The results were externally validated through a cohort of 42 patients operated on from the same institution.ResultsSix independent prognosis factors were selected and incorporated in the modified scoring system, including carcinoembryonic antigen, carbohydrate antigen 19-9, alpha-fetoprotein, prealbumin, T and N of ICC staging category in 8th edition of AJCC. The proposed scoring system showed a more favorable discriminatory ability and model performance than the AJCC 8th and LCSGJ staging systems, with a higher C-index of 0.693 (95% CI, 0.663-0.723) in the internal validation cohort and 0.671 (95% CI, 0.602-0.740) in the external validation cohort, which was then confirmed with lower BS (0.103 in internal validation cohort and 0.169 in external validation cohort). Meanwhile, machine learning techniques for variable selection together with stepwise Cox regression for survival analysis shows a better prognostic accuracy than using stepwise Cox regression method only.ConclusionsThis study put forward a modified ICC scoring system based on prognosis factors selection incorporated with machine learning, for individualized prognosis evaluation in patients with ICC.

Dataset Information

Epigenome-wide development and validation of a prognostic methylation score in intrahepatic cholangiocarcinoma based on machine learning strategies.

Background

Methods

Results

Conclusions

Publications

Epigenome-wide development and validation of a prognostic methylation score in intrahepatic cholangiocarcinoma based on machine learning strategies.

Similar Datasets

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