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Zeng N, Jian Z, Xu J, Peng T, Hong G, Xiao F. Identification of qualitative characteristics of immunosuppression in sepsis based on immune-related genes and immune infiltration features. Heliyon 2024; 10:e29007. [PMID: 38628767 PMCID: PMC11019180 DOI: 10.1016/j.heliyon.2024.e29007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 03/26/2024] [Accepted: 03/28/2024] [Indexed: 04/19/2024] Open
Abstract
Objective Sepsis is linked to high morbidity and mortality rates. Consequently, early diagnosis is crucial for proper treatment, reducing hospitalization, and mortality rates. Additionally, over one-fifth of sepsis patients still face a risk of death. Hence, early diagnosis, and effective treatment play pivotal roles in enhancing the prognosis of patients with sepsis. Method The study analyzed whole blood data obtained from patients with sepsis and control samples sourced from three datasets (GSE57065, GSE69528, and GSE28750). Commonly dysregulated immune-related genes (IRGs) among these three datasets were identified. The differential characteristics of these common IRGs in the sepsis and control samples were assessed using the REO-based algorithm. Based on these differential characteristics, samples from eight Gene Expression Omnibus (GEO) databases (GSE57065, GSE69528, GSE28750, GSE65682, GSE69063, GSE95233, GSE131761, and GSE154918), along with three ArrayExpress databases (E-MTAB-4421, E-MTAB-4451, and E-MTAB-7581), were categorized and scored. The effectiveness of these differential characteristics in distinguishing sepsis samples from control samples was evaluated using the AUC value derived from the receiver operating characteristic curve (ROC) curve. Furthermore, the expression of IRGs was validated in peripheral blood samples obtained from patients with sepsis through qRT-PCR. Results Among the three training datasets, a total of 84 common dysregulated immune-related genes (IRGs) were identified. Utilizing a within-sample relative expression ordering (REOs)-based algorithm to analyze these common IRGs, differential characteristics were observed in three reverse stable pairs (ELANE-RORA, IL18RAP-CD247, and IL1R1-CD28). In the eight GEO datasets, the expression of ELANE, IL18RAP, and IL1R1 demonstrated significant upregulation, while RORA, CD247, and CD28 expression exhibited notable downregulation during sepsis. These three pairs of immune-related marker genes displayed accuracies of 95.89% and 97.99% in distinguishing sepsis samples among the eight GEO datasets and the three independent ArrayExpress datasets, respectively. The area under the receiver operating characteristic curve ranged from 0.81 to 1.0. Additionally, among these three immune-related marker gene pairs, mRNA expression levels of ELANE and IL1R1 were upregulated, whereas the levels of CD247 and CD28 mRNA were downregulated in blood samples from patients with sepsis compared to normal controls. Conclusion These three immune-related marker gene pairs exhibit high predictive performance for blood samples from patients with sepsis. They hold potential as valuable auxiliary clinical blood screening tools for sepsis.
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Affiliation(s)
- Ni Zeng
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Zaijin Jian
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Junmei Xu
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Tian Peng
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Guiping Hong
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Feng Xiao
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
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Zhang ZM, Huang Y, Liu G, Yu W, Xie Q, Chen Z, Huang G, Wei J, Zhang H, Chen D, Du H. Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma. Sci Rep 2024; 14:5274. [PMID: 38438393 PMCID: PMC10912761 DOI: 10.1038/s41598-024-51265-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/03/2024] [Indexed: 03/06/2024] Open
Abstract
Hepatocellular carcinoma (HCC) remains a formidable malignancy that significantly impacts human health, and the early diagnosis of HCC holds paramount importance. Therefore, it is imperative to develop an efficacious signature for the early diagnosis of HCC. In this study, we aimed to develop early HCC predictors (eHCC-pred) using machine learning-based methods and compare their performance with existing methods. The enhancements and advancements of eHCC-pred encompassed the following: (i) utilization of a substantial number of samples, including an increased representation of cirrhosis tissues without HCC (CwoHCC) samples for model training and augmented numbers of HCC and CwoHCC samples for model validation; (ii) incorporation of two feature selection methods, namely minimum redundancy maximum relevance and maximum relevance maximum distance, along with the inclusion of eight machine learning-based methods; (iii) improvement in the accuracy of early HCC identification, elevating it from 78.15 to 97% using identical independent datasets; and (iv) establishment of a user-friendly web server. The eHCC-pred is freely accessible at http://www.dulab.com.cn/eHCC-pred/ . Our approach, eHCC-pred, is anticipated to be robustly employed at the individual level for facilitating early HCC diagnosis in clinical practice, surpassing currently available state-of-the-art techniques.
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Affiliation(s)
- Zi-Mei Zhang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Yuting Huang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Guanghao Liu
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
- Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, 350122, China
| | - Wenqi Yu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Qingsong Xie
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Zixi Chen
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Guanda Huang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Jinfen Wei
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Haibo Zhang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Dong Chen
- Fangrui Institute of Innovative Drugs, South China University of Technology, Guangzhou, China
| | - Hongli Du
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.
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Zheng L, Chen J, Ye W, Fan Q, Chen H, Yan H. An individualized stemness-related signature to predict prognosis and immunotherapy responses for gastric cancer using single-cell and bulk tissue transcriptomes. Cancer Med 2024; 13:e6908. [PMID: 38168907 PMCID: PMC10807574 DOI: 10.1002/cam4.6908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/01/2023] [Accepted: 12/22/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Currently, many stemness-related signatures have been developed for gastric cancer (GC) to predict prognosis and immunotherapy outcomes. However, due to batch effects, these signatures cannot accurately analyze patients one by one, rendering them impractical in real clinical scenarios. Therefore, we aimed to develop an individualized and clinically applicable signature based on GC stemness. METHODS Malignant epithelial cells from single-cell RNA-Seq data of GC were used to identify stemness-related signature genes based on the CytoTRACE score. Using two bulk tissue datasets as training data, the enrichment scores of the signature genes were applied to classify samples into two subtypes. Then, using the identified subtypes as criteria, we developed an individualized stemness-related signature based on the within-sample relative expression orderings of genes. RESULTS We identified 175 stemness-related signature genes, which exhibited significantly higher AUCell scores in poorly differentiated GCs compared to differentiated GCs. In training datasets, GC samples were classified into two subtypes with significantly different survival times and genomic characteristics. Utilizing the two subtypes, an individualized signature was constructed containing 47 gene pairs. In four independent testing datasets, GC samples classified as high risk exhibited significantly shorter survival times, higher infiltration of M2 macrophages, and lower immune responses compared to low-risk samples. Moreover, the potential therapeutic targets and corresponding drugs were identified for the high-risk group, such as CD248 targeted by ontuxizumab. CONCLUSIONS We developed an individualized stemness-related signature, which can accurately predict the prognosis and efficacy of immunotherapy for each GC sample.
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Affiliation(s)
- Linyong Zheng
- Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, School of Medical Technology and EngineeringFujian Medical UniversityFuzhouChina
| | - Jingyan Chen
- Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, School of Medical Technology and EngineeringFujian Medical UniversityFuzhouChina
| | - Wenhai Ye
- Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, School of Medical Technology and EngineeringFujian Medical UniversityFuzhouChina
| | - Qi Fan
- Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, School of Medical Technology and EngineeringFujian Medical UniversityFuzhouChina
| | - Haifeng Chen
- Department of Gastrointestinal SurgeryFuzhou Second HospitalFuzhouChina
| | - Haidan Yan
- Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, School of Medical Technology and EngineeringFujian Medical UniversityFuzhouChina
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical SciencesFujian Medical UniversityFuzhouChina
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Ma C, Zhang P, Du S, Li Y, Li S. Construction of Tongue Image-Based Machine Learning Model for Screening Patients with Gastric Precancerous Lesions. J Pers Med 2023; 13:jpm13020271. [PMID: 36836505 PMCID: PMC9968136 DOI: 10.3390/jpm13020271] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/25/2023] [Accepted: 01/29/2023] [Indexed: 02/05/2023] Open
Abstract
Screening patients with precancerous lesions of gastric cancer (PLGC) is important for gastric cancer prevention. The accuracy and convenience of PLGC screening could be improved with the use of machine learning methodologies to uncover and integrate valuable characteristics of noninvasive medical images related to PLGC. In this study, we therefore focused on tongue images and for the first time constructed a tongue image-based PLGC screening deep learning model (AITongue). The AITongue model uncovered potential associations between tongue image characteristics and PLGC, and integrated canonical risk factors, including age, sex, and Hp infection. Five-fold cross validation analysis on an independent cohort of 1995 patients revealed the AITongue model could screen PLGC individuals with an AUC of 0.75, 10.3% higher than that of the model with only including canonical risk factors. Of note, we investigated the value of the AITongue model in predicting PLGC risk by establishing a prospective PLGC follow-up cohort, reaching an AUC of 0.71. In addition, we developed a smartphone-based app screening system to enhance the application convenience of the AITongue model in the natural population from high-risk areas of gastric cancer in China. Collectively, our study has demonstrated the value of tongue image characteristics in PLGC screening and risk prediction.
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Affiliation(s)
- Changzheng Ma
- Institute of TCM-X/MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist/Department of Automation, Tsinghua University, Beijing 100084, China
| | - Peng Zhang
- Institute of TCM-X/MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist/Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shiyu Du
- Department of Gastroenterology, China-Japan Friendship Hospital, Chaoyang District, Beijing 100029, China
| | - Yan Li
- Department of Traditional Chinese Medicine, Yijishan Hospital of Wannan Medical College, Wuhu 241000, China
| | - Shao Li
- Institute of TCM-X/MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist/Department of Automation, Tsinghua University, Beijing 100084, China
- Correspondence:
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Li HH, Sun B, Tan C, Li R, Fu CX, Grimm R, Zhu H, Peng WJ. The Value of Whole-Tumor Histogram and Texture Analysis Using Intravoxel Incoherent Motion in Differentiating Pathologic Subtypes of Locally Advanced Gastric Cancer. Front Oncol 2022; 12:821586. [PMID: 35223503 PMCID: PMC8864172 DOI: 10.3389/fonc.2022.821586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 01/20/2022] [Indexed: 01/02/2023] Open
Abstract
Purpose To determine if whole-tumor histogram and texture analyses using intravoxel incoherent motion (IVIM) parameters values could differentiate the pathologic characteristics of locally advanced gastric cancer. Methods Eighty patients with histologically confirmed locally advanced gastric cancer who received surgery in our institution were retrospectively enrolled into our study between April 2017 and December 2018. Patients were excluded if they had lesions with the smallest diameter < 5 mm and severe image artifacts. MR scanning included IVIM sequences (9 b values, 0, 20, 40, 60, 100, 150,200, 500, and 800 s/mm2) used in all patients before treatment. Whole tumors were segmented by manually drawing the lesion contours on each slice of the diffusion-weighted imaging (DWI) images (with b=800). Histogram and texture metrics for IVIM parameters values and apparent diffusion coefficient (ADC) values were measured based on whole-tumor volume analyses. Then, all 24 extracted metrics were compared between well, moderately, and poorly differentiated tumors, and between different Lauren classifications, signet-ring cell carcinomas, and other poorly cohesive carcinomas using univariate analyses. Multivariate logistic analyses and multicollinear tests were used to identify independent influencing factors from the significant variables of the univariate analyses to distinguish tumor differentiation and Lauren classifications. ROC curve analyses were performed to evaluate the diagnostic performance of these independent influencing factors for determining tumor differentiation and Lauren classifications and identifying signet-ring cell carcinomas. The interobserver agreement was also conducted between the two observers for image quality evaluations and parameter metric measurements. Results For diagnosing tumor differentiation, the ADCmedian, pure diffusion coefficient median (Dslowmedian), and pure diffusion coefficient entropy (Dslowentropy) showed the greatest AUCs: 0.937, 0.948, and 0.850, respectively, and no differences were found between the three metrics, P>0.05). The 95th percentile perfusion factor (FP P95th) was the best metric to distinguish diffuse-type GCs vs. intestinal/mixed (AUC=0.896). The ROC curve to distinguish signet-ring cell carcinomas from other poorly cohesive carcinomas showed that the Dslowmedian had AUC of 0.738. For interobserver reliability, image quality evaluations showed excellent agreement (interclass correlation coefficient [ICC]=0.85); metrics measurements of all parameters indicated good to excellent agreement (ICC=0.65-0.89), except for the Dfast metric, which showed moderate agreement (ICC=0.41-0.60). Conclusions The whole-tumor histogram and texture analyses of the IVIM parameters based on the biexponential model provided a non-invasive method to discriminate pathologic tumor subtypes preoperatively in patients with locally advanced gastric cancer. The metric FP P95th derived from IVIM performed better in determining Lauren classifications than the mono-exponential model.
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Affiliation(s)
- Huan-Huan Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Bo Sun
- Department of Gastric Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Cong Tan
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Rong Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Cai-Xia Fu
- MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China
| | - Robert Grimm
- MR Applications Development, Siemens Healthcare, Erlangen, Germany
| | - Hui Zhu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Wei-Jun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
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Zhou YJ, Lu XF, Meng JL, Wang XY, Zhang QW, Chen JN, Wang QW, Yan FR, Li XB. Neo-adjuvant radiation therapy provides a survival advantage in T3-T4 nodal positive gastric and gastroesophageal junction adenocarcinoma: a SEER database analysis. BMC Cancer 2021; 21:771. [PMID: 34217249 PMCID: PMC8254219 DOI: 10.1186/s12885-021-08534-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 06/21/2021] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Due to negative results in clinical trials of postoperative chemoradiation for gastric cancer, at present, there is a tendency to move chemoradiation therapy forward in gastric and gastroesophageal junction (GEJ) adenocarcinoma. Several randomized controlled trials (RCTs) are currently recruiting subjects to investigate the effect of neo-adjuvant radiotherapy (NRT) in gastric and GEJ cancer. Large retrospective studies may be beneficial in clarifying the potential benefit of NRT, providing implications for RCTs. METHODS We retrieved the clinicopathological and treatment data of gastric and GEJ adenocarcinoma patients who underwent surgical resection and chemotherapy between 2004 and 2015 from Surveillance, Epidemiology, and End Results (SEER) database. We compared survival between NRT and non-NRT patients among four clinical subgroups (T1-2N-, T1-2N+, T3-4N-, and T3-4N+). RESULTS Overall, 5272 patients were identified, among which 1984 patients received NRT. After adjusting confounding variables, significantly improved survival between patients with and without NRT was only observed in T3-4N+ subgroup [hazard ratio (HR) 0.79, 95% confidence interval (CI): 0.66-0.95; P = 0.01]. Besides, Kaplan-Meier plots showed significant cause-specific survival advantage of NRT in intestinal type (P < 0.001), but not in diffuse type (P = 0.11) for T3-4N+ patients. In the multivariate competing risk model, NRT still showed survival advantage only in T3-4 N+ patients (subdistribution HR: 0.77; 95% CI: 0.64-0.93; P = 0.006), but not in other subgroups. CONCLUSIONS NRT might benefit resectable gastric and GEJ cancer patients of T3-4 stages with positive lymph nodes, particularly for intestinal-type. Nevertheless, these results should be interpreted with caution, and more data from ongoing RCTs are warranted.
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Affiliation(s)
- Yu-Jie Zhou
- Division 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, 160 Pujian Road, Shanghai, 200127, China
| | - Xiao-Fan Lu
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Jia-Lin Meng
- Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xin-Yuan Wang
- Division 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, 160 Pujian Road, Shanghai, 200127, China
| | - Qing-Wei Zhang
- Division 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, 160 Pujian Road, Shanghai, 200127, China
| | - Jin-Nan Chen
- Division 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, 160 Pujian Road, Shanghai, 200127, China
| | - Qi-Wen Wang
- Division 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, 160 Pujian Road, Shanghai, 200127, China
| | - Fang-Rong Yan
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Xiao-Bo Li
- Division 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, 160 Pujian Road, Shanghai, 200127, China.
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Xia J, Zhang H, Guan Q, Wang S, Li Y, Xie J, Li M, Huang H, Yan H, Chen T. Qualitative diagnostic signature for pancreatic ductal adenocarcinoma based on the within-sample relative expression orderings. J Gastroenterol Hepatol 2021; 36:1714-1720. [PMID: 33150986 DOI: 10.1111/jgh.15326] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 10/18/2020] [Accepted: 10/24/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) accounts for about 90% of pancreatic cancer, which is one of the most aggressive malignant neoplasms with a 9.3% five-year survival rate. The pathological biopsy is the current golden standard for confirming suspicious lesions of PDAC, but it is not entirely reliable because of the insufficient sampling amount and inaccurate sampling location. Therefore, developing a robust signature to aid the accurate diagnosis of PDAC is critical. METHODS Based on the within-sample relative expression orderings of gene pairs, we identified a qualitative signature to discriminate both PDAC and adjacent samples from both chronic pancreatitis and normal samples in the training datasets and validated it in other independent datasets produced by different laboratories with different measuring platforms. RESULTS A six-gene-pair signature was identified in the training data and validated in eight independent datasets. For surgical samples, 96.63% of 356 PDAC tissues, 100% of 11 pancreatitis tissues of non-cancer patients, and 23 of 24 normal pancreatic tissues were correctly classified. Especially, 59 of 60 cancer-adjacent normal tissues of PDAC patients were correctly identified as PDAC. For biopsy samples, all of 11 PDAC biopsy tissues were correctly classified as PDAC. CONCLUSION The signature can distinguish both PDAC and PDAC-adjacent normal tissues from both chronic pancreatitis and normal tissues of non-cancer patients even when the sampling locations are inaccurate, which can aid the diagnosis of PDAC.
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Affiliation(s)
- Jie Xia
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Huarong Zhang
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Qingzhou Guan
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Shanshan Wang
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Yawei Li
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Jiajing Xie
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Meifeng Li
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Haiyan Huang
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Haidan Yan
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Ting Chen
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
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Yu Z, Xie Q, Zhao Y, Duan L, Qiu P, Fan H. NGS plus bacterial culture: A more accurate method for diagnosing forensic-related nosocomial infections. Leg Med (Tokyo) 2021; 52:101910. [PMID: 34052680 DOI: 10.1016/j.legalmed.2021.101910] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/19/2021] [Accepted: 05/20/2021] [Indexed: 11/30/2022]
Abstract
Traditional autopsy and microscopic examination of pathological sections are the "gold standard" for the cause of death diagnosis. However, in some special cases, such as the deaths caused by bacterial infections, pathological sections are not always sufficient to provide convincing evidences for determining the causes of death. In recent years, with the development of Next Generation Sequencing (NGS), clinical medicine has already introduced it into the diagnosis of difficult diseases, which is rare in forensic pathological diagnoses. Here, we applied an NGS-based method combined with bacterial culture to examine a special case in which the deceased was suspected of having suffered from nosocomial infections. Results of the NGS and bacterial culture showed that Enterococcus and Acinetobacter baumannii, which are the most common bacteria causing nosocomial infections, were abundant in blood and hydropericardium of the deceased. Combining medical records and the results of the dissections, we proved that the death was actually caused by MODS which was the adverse consequence of nosocomial infections. In this case, the combination of NGS and bacterial culture was used to identify the pathogen which had caused the death. The results of NGS not only shorten the period of diagnosis, but also greatly increase the credibility of traditional anatomy and results of bacterial culture, which is expected to be further applied for forensic practices in the near future.
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Affiliation(s)
- Zhonghao Yu
- School of Forensic Medicine, Southern Medical University, Guangzhou, 510515 Guangdong, China
| | - Qiqian Xie
- School of Forensic Medicine, Southern Medical University, Guangzhou, 510515 Guangdong, China
| | - Yifeng Zhao
- Nanjing Zhenghong Judicial Identification Institute, Nanjing, 211800 Jiangsu, China
| | - Lizhong Duan
- Beijing Municipal Public Security Bureau, 102600 Beijing, China
| | - Pingming Qiu
- School of Forensic Medicine, Southern Medical University, Guangzhou, 510515 Guangdong, China.
| | - Haoliang Fan
- School of Forensic Medicine, Southern Medical University, Guangzhou, 510515 Guangdong, China; School of Basic Medicine and Life Science, Hainan Medical University, Haikou, 571199 Hainan, China.
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Zhou YJ, Lu XF, Meng JL, Wang XY, Ruan XJ, Yang CJ, Wang QW, Chen HM, Gao YJ, Yan FR, Li XB. Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer. Front Mol Biosci 2020; 7:569842. [PMID: 33173782 PMCID: PMC7538791 DOI: 10.3389/fmolb.2020.569842] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 08/26/2020] [Indexed: 12/14/2022] Open
Abstract
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.
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Affiliation(s)
- Yu-Jie Zhou
- Division 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, China
| | - Xiao-Fan Lu
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Jia-Lin Meng
- Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xin-Yuan Wang
- Division 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, China
| | - Xin-Jia Ruan
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Chang-Jie Yang
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qi-Wen Wang
- Division 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, China
| | - Hui-Min Chen
- Division 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, China
| | - Yun-Jie Gao
- Division 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, China
| | - Fang-Rong Yan
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Xiao-Bo Li
- Division 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, China
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10
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Zhang ZM, Wang JS, Zulfiqar H, Lv H, Dao FY, Lin H. Early Diagnosis of Pancreatic Ductal Adenocarcinoma by Combining Relative Expression Orderings With Machine-Learning Method. Front Cell Dev Biol 2020; 8:582864. [PMID: 33178697 PMCID: PMC7593596 DOI: 10.3389/fcell.2020.582864] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 09/15/2020] [Indexed: 12/16/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal cancer deeply affecting human health. Diagnosing early-stage PDAC is the key point to PDAC patients' survival. However, the biomarkers for diagnosing early PDAC are inexact in most cases. Therefore, it is highly desirable to identify an effective PDAC diagnostic biomarker. In the current work, we designed a novel computational approach based on within-sample relative expression orderings (REOs). A feature selection technique called minimum redundancy maximum relevance was used to pick out optimal REOs. We then compared the performances of different classification algorithms for discriminating PDAC and its adjacent normal tissues from non-PDAC tissues. The support vector machine algorithm is the best one for identifying early PDAC diagnostic biomarker. At first, a signature composed of nine gene pairs was acquired from microarray gene expression data sets. These gene pairs could produce satisfactory classification accuracy up to 97.53% in fivefold cross-validation. Subsequently, two types of data from diverse platforms, namely, microarray and RNA-Seq, were used to validate this signature. For microarray data, all (100.00%) of 115 PDAC tissues and all (100.00%) of 31 PDAC adjacent normal tissues were correctly recognized as PDAC. In addition, 88.24% of 17 non-PDAC (normal or pancreatitis) tissues were correctly classified. For the RNA-Seq data, all (100.00%) of 177 PDAC tissues and all (100.00%) of 4 PDAC adjacent normal tissues were correctly recognized as PDAC. Validation results demonstrated that the signature had a good cross-platform effect for early detection of PDAC. This work developed a new robust signature that might be a promising biomarker for early PDAC diagnosis.
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Affiliation(s)
- Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jia-Shu Wang
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hasan Zulfiqar
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lv
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
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11
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Zhang ZM, Tan JX, Wang F, Dao FY, Zhang ZY, Lin H. Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method. Front Bioeng Biotechnol 2020; 8:254. [PMID: 32292778 PMCID: PMC7122481 DOI: 10.3389/fbioe.2020.00254] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 03/12/2020] [Indexed: 12/18/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is a serious cancer which ranked the fourth in cancer-related death worldwide. Hence, more accurate diagnostic models are urgently needed to aid the early HCC diagnosis under clinical scenarios and thus improve HCC treatment and survival. Several conventional methods have been used for discriminating HCC from cirrhosis tissues in patients without HCC (CwoHCC). However, the recognition successful rates are still far from satisfactory. In this study, we applied a computational approach that based on machine learning method to a set of microarray data generated from 1091 HCC samples and 242 CwoHCC samples. The within-sample relative expression orderings (REOs) method was used to extract numerical descriptors from gene expression profiles datasets. After removing the unrelated features by using maximum redundancy minimum relevance (mRMR) with incremental feature selection, we achieved “11-gene-pair” which could produce outstanding results. We further investigated the discriminate capability of the “11-gene-pair” for HCC recognition on several independent datasets. The wonderful results were obtained, demonstrating that the selected gene pairs can be signature for HCC. The proposed computational model can discriminate HCC and adjacent non-cancerous tissues from CwoHCC even for minimum biopsy specimens and inaccurately sampled specimens, which can be practical and effective for aiding the early HCC diagnosis at individual level.
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Affiliation(s)
- Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiu-Xin Tan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fang Wang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhao-Yue Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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