Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer
It is currently difficult for pathologists to diagnose pancreatic cancer (PC) using biopsy specimens because samples may have been from an incorrect site or contain an insufficient amount of tissue. Thus, there is a need to develop a platform-independent molecular classifier that accurately distingu...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020-09-01
|
| Series: | Frontiers in Molecular Biosciences |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fmolb.2020.569842/full |
| _version_ | 1819171231468879872 |
|---|---|
| author | Yu-Jie Zhou Xiao-Fan Lu Jia-Lin Meng Xin-Yuan Wang Xin-Jia Ruan Chang-Jie Yang Qi-Wen Wang Hui-Min Chen Yun-Jie Gao Fang-Rong Yan Xiao-Bo Li |
| author_facet | Yu-Jie Zhou Xiao-Fan Lu Jia-Lin Meng Xin-Yuan Wang Xin-Jia Ruan Chang-Jie Yang Qi-Wen Wang Hui-Min Chen Yun-Jie Gao Fang-Rong Yan Xiao-Bo Li |
| author_sort | Yu-Jie Zhou |
| collection | DOAJ |
| description | It is currently difficult for pathologists to diagnose pancreatic cancer (PC) using biopsy specimens because samples may have been from an incorrect site or contain an insufficient amount of tissue. Thus, there is a need to develop a platform-independent molecular classifier that accurately distinguishes benign pancreatic lesions from PC. Here, we developed a robust qualitative messenger RNA signature based on within-sample relative expression orderings (REOs) of genes to discriminate both PC tissues and cancer-adjacent normal tissues from non-PC pancreatitis and healthy pancreatic tissues. A signature comprising 12 gene pairs and 17 genes was built in the training datasets and validated in microarray and RNA-sequencing datasets from biopsy samples and surgically resected samples. Analysis of 1,007 PC tissues and 257 non-tumor samples from nine databases indicated that the geometric mean of sensitivity and specificity was 96.7%, and the area under receiver operating characteristic curve was 0.978 (95% confidence interval, 0.947–0.994). For 20 specimens obtained from endoscopic biopsy, the signature had a diagnostic accuracy of 100%. The REO-based signature described here can aid in the molecular diagnosis of PC and may facilitate objective differentiation between benign and malignant pancreatic lesions. |
| first_indexed | 2024-12-22T19:48:00Z |
| format | Article |
| id | doaj.art-4f01abf63f424836940c2b47858bc945 |
| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-12-22T19:48:00Z |
| publishDate | 2020-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-4f01abf63f424836940c2b47858bc9452022-12-21T18:14:38ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2020-09-01710.3389/fmolb.2020.569842569842Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic CancerYu-Jie Zhou0Xiao-Fan Lu1Jia-Lin Meng2Xin-Yuan Wang3Xin-Jia Ruan4Chang-Jie Yang5Qi-Wen Wang6Hui-Min Chen7Yun-Jie Gao8Fang-Rong Yan9Xiao-Bo Li10Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaState Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, ChinaDepartment of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, ChinaDivision of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaState Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, ChinaDepartment of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaDivision of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaDivision of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaDivision of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaState Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, ChinaDivision of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaIt is currently difficult for pathologists to diagnose pancreatic cancer (PC) using biopsy specimens because samples may have been from an incorrect site or contain an insufficient amount of tissue. Thus, there is a need to develop a platform-independent molecular classifier that accurately distinguishes benign pancreatic lesions from PC. Here, we developed a robust qualitative messenger RNA signature based on within-sample relative expression orderings (REOs) of genes to discriminate both PC tissues and cancer-adjacent normal tissues from non-PC pancreatitis and healthy pancreatic tissues. A signature comprising 12 gene pairs and 17 genes was built in the training datasets and validated in microarray and RNA-sequencing datasets from biopsy samples and surgically resected samples. Analysis of 1,007 PC tissues and 257 non-tumor samples from nine databases indicated that the geometric mean of sensitivity and specificity was 96.7%, and the area under receiver operating characteristic curve was 0.978 (95% confidence interval, 0.947–0.994). For 20 specimens obtained from endoscopic biopsy, the signature had a diagnostic accuracy of 100%. The REO-based signature described here can aid in the molecular diagnosis of PC and may facilitate objective differentiation between benign and malignant pancreatic lesions.https://www.frontiersin.org/article/10.3389/fmolb.2020.569842/fullmolecular signaturerelative expression orderingsearly diagnosispancreatic cancergene pairs |
| spellingShingle | Yu-Jie Zhou Xiao-Fan Lu Jia-Lin Meng Xin-Yuan Wang Xin-Jia Ruan Chang-Jie Yang Qi-Wen Wang Hui-Min Chen Yun-Jie Gao Fang-Rong Yan Xiao-Bo Li Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer Frontiers in Molecular Biosciences molecular signature relative expression orderings early diagnosis pancreatic cancer gene pairs |
| title | Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer |
| title_full | Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer |
| title_fullStr | Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer |
| title_full_unstemmed | Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer |
| title_short | Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer |
| title_sort | qualitative transcriptional signature for the pathological diagnosis of pancreatic cancer |
| topic | molecular signature relative expression orderings early diagnosis pancreatic cancer gene pairs |
| url | https://www.frontiersin.org/article/10.3389/fmolb.2020.569842/full |
| work_keys_str_mv | AT yujiezhou qualitativetranscriptionalsignatureforthepathologicaldiagnosisofpancreaticcancer AT xiaofanlu qualitativetranscriptionalsignatureforthepathologicaldiagnosisofpancreaticcancer AT jialinmeng qualitativetranscriptionalsignatureforthepathologicaldiagnosisofpancreaticcancer AT xinyuanwang qualitativetranscriptionalsignatureforthepathologicaldiagnosisofpancreaticcancer AT xinjiaruan qualitativetranscriptionalsignatureforthepathologicaldiagnosisofpancreaticcancer AT changjieyang qualitativetranscriptionalsignatureforthepathologicaldiagnosisofpancreaticcancer AT qiwenwang qualitativetranscriptionalsignatureforthepathologicaldiagnosisofpancreaticcancer AT huiminchen qualitativetranscriptionalsignatureforthepathologicaldiagnosisofpancreaticcancer AT yunjiegao qualitativetranscriptionalsignatureforthepathologicaldiagnosisofpancreaticcancer AT fangrongyan qualitativetranscriptionalsignatureforthepathologicaldiagnosisofpancreaticcancer AT xiaoboli qualitativetranscriptionalsignatureforthepathologicaldiagnosisofpancreaticcancer |