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      Using ESTIMATE algorithm to establish an 8-mRNA signature prognosis prediction system and identify immunocyte infiltration-related genes in Pancreatic adenocarcinoma

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          Abstract

          Objective: The tumour microenvironment is one of the significant factors driving the carcinogenesis of Pancreatic adenocarcinoma (PAAD). However, the underlying mechanism of how the tumour microenvironment impacts the prognosis of PAAD is not completely clear.

          Results: The transcriptome and clinical data of 182 PAAD program cases were downloaded from the TCGA database. Three hundred thirty-three differentially expressed genes (DEGs) between high and low stromal groups and 314 DEGs between high and low immune score groups were identified using ESTIMATE score. Based on the 203 genes differentially expressed simultaneously in two score-related comparisons, we established an 8-mRNA signature to evaluate the prognosis of PAAD patients. Kaplan-Meier curves showed significantly worse survival for patients with high-risk scores in both the training and validation groups. The risk score was an independent prognostic factor and had a high predictive value for the prognosis of patients with PAAD. By searching the TCGA database, we showed that CA9, CXCL9, and GIMAP7 from the 8-mRNA signature were associated with the infiltration levels of immunocytes by regulating FOXO1 expression in PAAD.

          Conclusions: Unlike traditional methods of screening for differential genes in cancer and healthy tissues, we constructed a novel 8-mRNA signature to predict the prognosis of PAAD patients by applying ESTIMATE scoring to RNA-seq-based transcriptome data. Most importantly, we identified CA9, CXCL9, and GIMAP7 from the above eight genes as regulators of immunocyte infiltration by adjusting the expression of FOXO1 in PAAD. Thus, CA9, CXCL9, and GIMAP7 might be the ideal targets of immune therapy of PAAD.

          Methods: ESTIMATE scoring was used to determine the stromal and immune scores of transcriptome datasets downloaded from the TCGA database. An mRNA-based prognostic signature was built for the training cohort via the LASSO Cox regression model. The signature was verified using a validation cohort. Kaplan-Meier curves and log-rank analysis were used to identify survival differences. Western blot analysis and RT-qPCR analysis were carried out to analyze the expression of specific proteins and mRNAs. IHC was performed to assess the protein levels of Forkhead box-O 1 (FOXO1), Carbonic anhydrase 9 (CA9), C-X-C motif chemokine ligand 9 (CXCL9), and GTPase, IMAP family member 7 (GIMAP7) in the tissue microarray of PAAD.

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          Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.

          Purpose To develop a radiomics signature to estimate disease-free survival (DFS) in patients with early-stage (stage I-II) non-small cell lung cancer (NSCLC) and assess its incremental value to the traditional staging system and clinical-pathologic risk factors for individual DFS estimation. Materials and Methods Ethical approval by the institutional review board was obtained for this retrospective analysis, and the need to obtain informed consent was waived. This study consisted of 282 consecutive patients with stage IA-IIB NSCLC. A radiomics signature was generated by using the least absolute shrinkage and selection operator, or LASSO, Cox regression model. Association between the radiomics signature and DFS was explored. Further validation of the radiomics signature as an independent biomarker was performed by using multivariate Cox regression. A radiomics nomogram with the radiomics signature incorporated was constructed to demonstrate the incremental value of the radiomics signature to the traditional staging system and other clinical-pathologic risk factors for individualized DFS estimation, which was then assessed with respect to calibration, discrimination, reclassification, and clinical usefulness. Results The radiomics signature was significantly associated with DFS, independent of clinical-pathologic risk factors. Incorporating the radiomics signature into the radiomics-based nomogram resulted in better performance (P < .0001) for the estimation of DFS (C-index: 0.72; 95% confidence interval [CI]: 0.71, 0.73) than with the clinical-pathologic nomogram (C-index: 0.691; 95% CI: 0.68, 0.70), as well as a better calibration and improved accuracy of the classification of survival outcomes (net reclassification improvement: 0.182; 95% CI: 0.02, 0.31; P = .02). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinical-pathologic nomogram. Conclusion The radiomics signature is an independent biomarker for the estimation of DFS in patients with early-stage NSCLC. Combination of the radiomics signature, traditional staging system, and other clinical-pathologic risk factors performed better for individualized DFS estimation in patients with early-stage NSCLC, which might enable a step forward precise medicine. (©) RSNA, 2016 Online supplemental material is available for this article.
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            A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme

            Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.
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              Epidemiology of pancreatic cancer: an overview.

              Pancreatic cancer, although infrequent, has an exceptionally high mortality rate, making it one of the four or five most common causes of cancer mortality in developed countries. The incidence of pancreatic cancer varies greatly across regions, which suggests roles for lifestyle factors, such as diet, or environmental factors, such as vitamin D exposure. Smoking is the most common known risk factor, and is the cause of 20-25% of all pancreatic tumors. Alcohol does not seem to be a risk factor, unless it leads to chronic pancreatitis, which is a probable risk factor. Long-standing diabetes increases the risk of pancreatic cancer, but can also be an early manifestation of pancreatic tumors. 5-10% of patients with pancreatic cancer have an underlying germline disorder, while the remaining percentage of cancer cases is thought to be caused by somatic mutations. Some individual studies suggest that mutations in various polymorphic genes can lead to small increases in the risk of pancreatic cancer, but these findings need to be replicated. Rising prevalence of smoking in developing countries, improved diagnosis and increasing population longevity are all likely to increase the global burden of pancreatic cancer in the coming decades.
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                Author and article information

                Journal
                Aging (Albany NY)
                Aging (Albany NY)
                Aging
                Aging (Albany NY)
                Impact Journals
                1945-4589
                31 March 2020
                17 March 2020
                : 12
                : 6
                : 5048-5070
                Affiliations
                [1 ]Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
                [2 ]Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
                [3 ]Department of Otorhinolaryngology-Head And Neck Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
                [4 ]Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
                Author notes
                [*]

                Equal Contribution

                Correspondence to: Heshui Wu; email: heshuiwu@hust.edu.cn
                Correspondence to: Xin Jin; email: jinxinunion@hust.edu.cn
                Article
                102931 102931
                10.18632/aging.102931
                7138590
                32181755
                a9aad6c4-1fce-422a-8060-eec1434509bb
                Copyright © 2020 Meng et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 August 2019
                : 09 March 2020
                Categories
                Research Paper

                Cell biology
                pancreatic cancer,tumor microenvironment,immunocytes infiltration,foxo1
                Cell biology
                pancreatic cancer, tumor microenvironment, immunocytes infiltration, foxo1

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