المؤلفون: Xiang Zhou, Weiming Wang, Baofu Zhang, Xiaohu Chen, Pengyi Guo, Jungang Zhao, Guorong Chen, Yunfeng Shan, Zhengping Yu, Yi Wang, Gang Chen

المصدر: SSRN Electronic Journal.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::eeebfd5c540c03322a310cf1b96039cdTest
https://doi.org/10.2139/ssrn.3907469Test

المؤلفون: Kuvaneshan Ramen, Xiuling Wu, Xian Shen, Yunfeng Shan, Qiyu Zhang, Junyu Chen, Xing Jin, Zhengping Yu, Xiangwu Zheng, Huanhu Zhao, Qiqiang Zeng

المصدر: SSRN Electronic Journal.

مصطلحات موضوعية: Abdominal pain, biology, business.industry, medicine.medical_treatment, Nomogram, Institutional review board, Machine learning, computer.software_genre, Logistic regression, Helsinki declaration, Carcinoembryonic antigen, medicine, biology.protein, Artificial intelligence, Hepatectomy, medicine.symptom, business, computer, Intrahepatic Cholangiocarcinoma

الوصف: Background: Accurate diagnosis of intrahepatic cholangiocarcinoma (ICC) caused by intrahepatic lithiasis (IHL) is crucial for timely and effective surgical intervention. The aim of the present study was to develop a nomogram to identify ICC associated with IHL (IHL-ICC). Methods: The study included 2269 patients with IHL, who got pathological diagnosis after hepatectomy or diagnostic biopsy. Machine learning algorithms including Lasso regression and random forest were used to identify important features out of the available features. Univariate and multivariate logistic regression analyses were used to reconfirm the features and develop the nomogram. The nomogram was externally validated in two independent cohorts. Results: The seven potential predictors were revealed for IHL-ICC, including age, abdominal pain, vomiting, comprehensive imagological diagnosis, alkaline phosphatase, carcinoembryonic antigen (CEA), and cancer antigen (CA) 19-9. The optimal cutoff value was 2.05μg/L for serum CEA and 133.65 U/mL for serum CA 19-9. The accuracy of the nomogram in predicting ICC was 82.6%. The area under the curve (AUC) of nomogram in training cohort was 0.867. The AUC for the validation set was 0.881 from the Second Affiliated Hospital of Wenzhou Medical University, and 0.938 from the First Affiliated Hospital of FuJian Medical University. Conclusions: The nomogram holds promise as a novel and accurate tool to predict IHL-ICC, which can identify lesions in IHL timely for hepatectomy or avoid unnecessary surgical resection. Funding Statement: This study was funded by Zhejiang Provincial Top Key Discipline of Surgery Wenzhou Medical University (2008-255). Declaration of Interests: No conflict of interest exits in the submission of this manuscript. Ethics Approval Statement: All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Review Board (IRB) of the First Affiliated Hospital of Wenzhou Medical University and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::4f9b3f22be1dab79654af379b6ccc826Test
https://doi.org/10.2139/ssrn.3495609Test

المؤلفون: Qiandong Zhu, Junjian Li, Ziyan Chen, Qi-Qiang Zeng, Zhi-Yong Du, Long-Yun Ye, Xiaozai Xie, Gang Chen, Yi Wang, Sina Zhang, Zhen Yang, Xi-Wei Ding, Zhengping Yu

المصدر: SSRN Electronic Journal.

مصطلحات موضوعية: Oncology, medicine.medical_specialty, HULC, Proportional hazards model, business.industry, Ethics committee, Normal tissue, Biomarker (cell), Internal medicine, Cancer genome, medicine, Prognostic model, business, Survival analysis

الوصف: Background: There is increasing evidence that long non-coding RNA are involved in the occurrence and progression of various tumors as carcinogenic or tumor suppressor regulators. Aims: Our study purposed to identify a novel lncRNA signature panel and evaluate its predictive value on cholangiocarcinoma (CCA) prognosis. Methods: Differentially expressed lncRNA were identified in CCA patients from The Cancer Genome Atlas database. The "edgeR" package and Cox regression analysis was used to establish a lncRNA prognostic model, which allowed patients to be divided into high-risk and low-risk groups. Through Receiver operating curves analysis, 90 CCA patients from the First Affiliated Hospital of Wenzhou Medical University (WMU) was performed to investigate the specificity and sensitivity of the model. By using Gene Ontology function and Kyoto Encyclopedia Gene and Genome pathway enrichment analysis, we explored the potential function of prognosis lncRNA. Results: A total of 1192 differential expression of lncRNA were identified in CCA and normal tissues. Five lncRNAs (HULC; AL359715.5; AC006504.8; AC090114.2; AP00943.4) were screened to establish a predictive model, whose high predictive power was validated in 90 CCA patients from WMU. Further analysis showed the independent prognostic capability of 5-lncRNA in the model, which had high sensitivity and specificity. Moreover, functional enrichment analysis suggested prognostic lncRNAs were primarily associated with CCA-related various biological processes. Conclusions: Our data established a novel lncRNA prognostic model for CCA, provided prognostic more useful information that went beyond traditional clinical pathology to predict survival in CCA patients. In addition, it revealed new molecular mechanisms of CCA. Funding: This work was supported by the grants from the Natural Science Foundation of Zhejiang Province (LY17H160047), the Public Projects of Zhejiang Province (2016C37127, 2018C37114), the National Natural Science Foundation of China (81470868, 81772628, 81703310). Declaration of Interest: The authors declare no conflict of interests. Ethical Approval: This study was approved by WMU’s Ethics Committee.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::9f19e121d26b1f67dc4bd73110f3865bTest
https://doi.org/10.2139/ssrn.3255811Test