1
|
Kulkarni C, Liu D, Fardeen T, Dickson ER, Jang H, Sinha SR, Gubatan J. Artificial intelligence and machine learning technologies in ulcerative colitis. Therap Adv Gastroenterol 2024; 17:17562848241272001. [PMID: 39247718 PMCID: PMC11378191 DOI: 10.1177/17562848241272001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 06/17/2024] [Indexed: 09/10/2024] Open
Abstract
Interest in artificial intelligence (AI) applications for ulcerative colitis (UC) has grown tremendously in recent years. In the past 5 years, there have been over 80 studies focused on machine learning (ML) tools to address a wide range of clinical problems in UC, including diagnosis, prognosis, identification of new UC biomarkers, monitoring of disease activity, and prediction of complications. AI classifiers such as random forest, support vector machines, neural networks, and logistic regression models have been used to model UC clinical outcomes using molecular (transcriptomic) and clinical (electronic health record and laboratory) datasets with relatively high performance (accuracy, sensitivity, and specificity). Application of ML algorithms such as computer vision, guided image filtering, and convolutional neural networks have also been utilized to analyze large and high-dimensional imaging datasets such as endoscopic, histologic, and radiological images for UC diagnosis and prediction of complications (post-surgical complications, colorectal cancer). Incorporation of these ML tools to guide and optimize UC clinical practice is promising but will require large, high-quality validation studies that overcome the risk of bias as well as consider cost-effectiveness compared to standard of care.
Collapse
Affiliation(s)
- Chiraag Kulkarni
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Derek Liu
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Touran Fardeen
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Eliza Rose Dickson
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Hyunsu Jang
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Sidhartha R Sinha
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, 300 Pasteur Drive, M211, Stanford, CA 94305, USA
| | - John Gubatan
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, 300 Pasteur Drive, M211, Stanford, CA 94305, USA
| |
Collapse
|
2
|
Gonzalez CG, Stevens TW, Verstockt B, Gonzalez DJ, D'Haens G, Dulai PS. Crohn's Patient Serum Proteomics Reveals Response Signature for Infliximab but not Vedolizumab. Inflamm Bowel Dis 2024; 30:1536-1545. [PMID: 38367209 DOI: 10.1093/ibd/izae016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Indexed: 02/19/2024]
Abstract
BACKGROUND Crohn's disease is a chronic inflammatory bowel disease that affects the gastrointestinal tract. Common biologic families used to treat Crohn's are tumor necrosis factor (TNF)-α blockers (infliximab and adalimumab) and immune cell adhesion blockers (vedolizumab). Given their differing mechanisms of action, the ability to monitor response and predict treatment efficacy via easy-to-obtain blood draws remains an unmet need. METHODS To investigate these gaps in knowledge, we leveraged 2 prospective cohorts (LOVE-CD, TAILORIX) and profiled their serum using high-dimensional isobaric-labeled proteomics before treatment and 6 weeks after treatment initiation with either vedolizumab or infliximab. RESULTS The proportion of patients endoscopically responding to treatment was comparable among infliximab and vedolizumab cohorts; however, the impact of vedolizumab on patient sera was negligible. In contrast, infliximab treatment induced a robust response including increased blood-gas regulatory response proteins, and concomitant decreases in inflammation-related proteins. Further analysis comparing infliximab responders and nonresponders revealed a lingering innate immune enrichments in nonresponders and a unique protease regulation signature related to clotting cascades in responders. Lastly, using samples prior to infliximab treatment, we highlight serum protein biomarkers that potentially predict a positive response to infliximab treatment. CONCLUSIONS These results will positively impact the determination of appropriate patient treatment and inform the selection of clinical trial outcome metrics.
Collapse
Affiliation(s)
- Carlos G Gonzalez
- Department of Pharmacology, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Toer W Stevens
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Bram Verstockt
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, KU Leuven, Leuven, Belgium
- Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
| | - David J Gonzalez
- Department of Pharmacology, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy, University of California San Diego, La Jolla, CA, USA
| | - Geert D'Haens
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Parambir S Dulai
- Department of Medicine, Division of Gastroenterology and Hepatology, Feinberg School of Medicine Northwestern University, Chicago, ILUSA
| |
Collapse
|
3
|
Lyu X, Zhang Z, Liu X, Geng L, Zhang M, Feng B. Prediction and Verification of Potential Therapeutic Targets for Non-Responders to Infliximab in Ulcerative Colitis. J Inflamm Res 2023; 16:2063-2078. [PMID: 37215377 PMCID: PMC10198282 DOI: 10.2147/jir.s409290] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 05/09/2023] [Indexed: 05/24/2023] Open
Abstract
Background Infliximab (IFX) has been widely used in ulcerative colitis (UC) patients. However, the subsequent effective treatment of IFX non-response in UC patients remains a challenge. This study aims to predict potential therapeutic targets for non-responders by performing a bioinformatic analysis of the data in the Gene Expression Omnibus (GEO) database and validation by biopsies. Methods Colonic mucosal biopsies expression profiles of IFX-treated UC patients (GSE73661, GSE16879) were utilized to predict potential therapeutic targets. Bioinformatics analyses were used to explore potential biological mechanisms. CytoHubba was performed to screen hub genes. We used a validation dataset and colonic mucosal biopsies of UC patients to validate hub genes. Results A total of 147 DEGs were identified (119 upregulated genes and 28 downregulated genes). GSEA showed that DEGs in GSE73661 were enriched in the pathways of the cytokine-cytokine receptor, the chemokine, and the adhesion molecules system. Based on the PPI network analysis, we identified four hub genes (and the transcription factor NF-κB). Then, we validate the expression of hub genes by reverse transcription-polymerase chain reaction (RT-PCR). We found higher expression of IL-6, IL1B, CXCL8, and CCL2 in non-responders compared to responders. Conclusion In summary, four potential targets (IL-6, IL1B, CXCL8, and CCL2) were finally identified by performing a bioinformatics analysis of the datasets in the GEO database. Their expression was confirmed in colonic mucosal biopsies of patients with UC. These results can help to further explore the mechanism of non-responders to IFX in UC and to provide potential targets for their subsequent treatment.
Collapse
Affiliation(s)
- Xue Lyu
- Department of Gastroenterology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, 450014, People’s Republic of China
| | - Zhe Zhang
- Department of Gastroenterology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, 450014, People’s Republic of China
| | - Xia Liu
- Department of Gastroenterology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, 450014, People’s Republic of China
| | - Li Geng
- Department of Gastroenterology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, 450014, People’s Republic of China
| | - Muhan Zhang
- Department of Gastroenterology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, 450014, People’s Republic of China
| | - Baisui Feng
- Department of Gastroenterology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, 450014, People’s Republic of China
| |
Collapse
|
4
|
A 3-Gene Random Forest Model to Diagnose Non-obstructive Azoospermia Based on Transcription Factor-Related Henes. Reprod Sci 2023; 30:233-246. [PMID: 35715550 DOI: 10.1007/s43032-022-01008-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 06/10/2022] [Indexed: 01/11/2023]
Abstract
Non-obstructive azoospermia (NOA) is one of the most severe forms of male infertility, but its diagnosis biomarkers with high sensitivity and specificity are largely unknown. Transcription factors (TFs) play essential roles in many pathological processes in different diseases. Herein, we aimed to identify the TFs showing high diagnosis ability for NOA through machine learning algorithms. The transcriptome data of the testicular tissue from 11 control and 47 NOA subjects were set as the training dataset; meanwhile, 1665 TFs were retrieved from the HumanTFDB. Through the feature extraction methods, including genomic difference analysis, Lasso, Boruta, SVM-RFE, and logistic regression, ETV2, TBX2, and ZNF689 were ultimately screened and then were included in the random forest (RF) diagnosis model. The RF model displayed high predictive power in the training (F-measure = 1) and two external validation (n = 31, F-measure = 0.902; n = 20, F-measure = 0.941) cohorts. The seminal plasma and testicular biopsy samples of 20 control and 20 NOA patients were collected from the local hospital, and the expression levels of ETV2, TBX2, and ZNF689 were measured via RT-qPCR and immunohistochemistry. The RF model could also distinguish the NOA samples in the local cohort (F-measure = 0.741). Single-cell RNA sequencing analysis, which was based on the 432 testicular cell samples from an NOA patient, showed that ETV2, TBX2, and ZNF689 were all significantly associated with spermatogenesis. In all, a 3-TF random forest diagnosis model was successfully established, providing novel insights into the latent mechanisms of NOA.
Collapse
|
5
|
Xiong T, Chen Y, Han S, Zhang TC, Pu L, Fan YX, Fan WC, Zhang YY, Li YX. Development and analysis of a comprehensive diagnostic model for aortic valve calcification using machine learning methods and artificial neural networks. Front Cardiovasc Med 2022; 9:913776. [PMID: 36531717 PMCID: PMC9751025 DOI: 10.3389/fcvm.2022.913776] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 11/07/2022] [Indexed: 11/27/2023] Open
Abstract
BACKGROUND Although advanced surgical and interventional treatments are available for advanced aortic valve calcification (AVC) with severe clinical symptoms, early diagnosis, and intervention is critical in order to reduce calcification progression and improve patient prognosis. The aim of this study was to develop therapeutic targets for improving outcomes for patients with AVC. MATERIALS AND METHODS We used the public expression profiles of individuals with AVC (GSE12644 and GSE51472) to identify potential diagnostic markers. First, the R software was used to identify differentially expressed genes (DEGs) and perform functional enrichment analysis. Next, we combined bioinformatics techniques with machine learning methodologies such as random forest algorithms and support vector machines to screen for and identify diagnostic markers of AVC. Subsequently, artificial neural networks were employed to filter and model the diagnostic characteristics for AVC incidence. The diagnostic values were determined using the receiver operating characteristic (ROC) curves. Furthermore, CIBERSORT immune infiltration analysis was used to determine the expression of different immune cells in the AVC. Finally, the CMap database was used to predict candidate small compounds as prospective AVC therapeutics. RESULTS A total of 78 strong DEGs were identified. The leukocyte migration and pid integrin 1 pathways were highly enriched for AVC-specific DEGs. CXCL16, GPM6A, BEX2, S100A9, and SCARA5 genes were all regarded diagnostic markers for AVC. The model was effectively constructed using a molecular diagnostic score system with significant diagnostic value (AUC = 0.987) and verified using the independent dataset GSE83453 (AUC = 0.986). Immune cell infiltration research revealed that B cell naive, B cell memory, plasma cells, NK cell activated, monocytes, and macrophage M0 may be involved in the development of AVC. Additionally, all diagnostic characteristics may have varying degrees of correlation with immune cells. The most promising small molecule medicines for reversing AVC gene expression are Doxazosin and Terfenadine. CONCLUSION It was identified that CXCL16, GPM6A, BEX2, S100A9, and SCARA5 are potentially beneficial for diagnosing and treating AVC. A diagnostic model was constructed based on a molecular prognostic score system using machine learning. The aforementioned immune cell infiltration may have a significant influence on the development and incidence of AVC.
Collapse
Affiliation(s)
- Tao Xiong
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yan Chen
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Shen Han
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Tian-Chen Zhang
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Lei Pu
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yu-Xin Fan
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Wei-Chen Fan
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Ya-Yong Zhang
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Ya-Xiong Li
- Department of Cardiovascular Surgery, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| |
Collapse
|
6
|
He Y, Cong L, He Q, Feng N, Wu Y. Development and validation of immune-based biomarkers and deep learning models for Alzheimer’s disease. Front Genet 2022; 13:968598. [PMID: 36072674 PMCID: PMC9441688 DOI: 10.3389/fgene.2022.968598] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 07/22/2022] [Indexed: 12/30/2022] Open
Abstract
Background: Alzheimer’s disease (AD) is the most common form of dementia in old age and poses a severe threat to the health and life of the elderly. However, traditional diagnostic methods and the ATN diagnostic framework have limitations in clinical practice. Developing novel biomarkers and diagnostic models is necessary to complement existing diagnostic procedures. Methods: The AD expression profile dataset GSE63060 was downloaded from the NCBI GEO public database for preprocessing. AD-related differentially expressed genes were screened using a weighted co-expression network and differential expression analysis, and functional enrichment analysis was performed. Subsequently, we screened hub genes by random forest, analyzed the correlation between hub genes and immune cells using ssGSEA, and finally built an AD diagnostic model using an artificial neural network and validated it. Results: Based on the random forest algorithm, we screened a total of seven hub genes from AD-related DEGs, based on which we confirmed that hub genes play an essential role in the immune microenvironment and successfully established a novel diagnostic model for AD using artificial neural networks, and validated its effectiveness in the publicly available datasets GSE63060 and GSE97760. Conclusion: Our study establishes a reliable model for screening and diagnosing AD that provides a theoretical basis for adding diagnostic biomarkers for the AD gene.
Collapse
Affiliation(s)
| | | | | | | | - Yun Wu
- *Correspondence: Yun Wu, ; Nianping Feng,
| |
Collapse
|