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Lee S, Ahn K, Lee T, Cho J, Young Kim M, Uh Y. Development of an equation to predict delta bilirubin levels using machine learning. Clin Chim Acta 2024; 564:119938. [PMID: 39181293 DOI: 10.1016/j.cca.2024.119938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 08/06/2024] [Accepted: 08/19/2024] [Indexed: 08/27/2024]
Abstract
OBJECTIVE Delta bilirubin (albumin-covalently bound bilirubin) may provide important clinical utility in identifying impaired hepatic excretion of conjugated bilirubin, but it cannot be measured in real-time for diagnostic purposes in clinical laboratories. METHODS A total of 210 samples were collected, and their delta bilirubin levels were measured four times using high-performance liquid chromatography. Data collected included age, sex, diagnosis code, delta bilirubin, total bilirubin, direct bilirubin, total protein, albumin, globulin, aspartate aminotransferase, alanine transaminase, alkaline phosphatase, gamma-glutamyl transferase, lactate dehydrogenase, hemoglobin, serum hemolysis value, hemolysis index, icterus value (Iv), icterus index (Ii), lipemia value (Lv), and lipemia index. To conduct feature selection and identify the optimal combination of variables, linear regression machine learning was performed 1,000 times. RESULTS The selected variables were total bilirubin, direct bilirubin, total protein, albumin, hemoglobin, Iv, Ii, and Lv. The best predictive performance for high delta bilirubin concentrations was achieved with the combination of albumin-direct bilirubin-hemoglobin-Iv-Lv. The final equation composed of these variables was as follows: delta bilirubin = 0.35 × Iv + 0.05 × Lv - 0.23 × direct bilirubin - 0.05 × hemoglobin - 0.04 × albumin + 0.10. CONCLUSION The equation established in this study is practical and can be easily applied in real-time in clinical laboratories.
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Affiliation(s)
- Saejin Lee
- Department of Laboratory Medicine, Jeonju Hospital, Yeongkyeong Medical Foundation, Jeonju, Korea
| | - Kwangjin Ahn
- Department of Laboratory Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Taesic Lee
- Department of Family Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Jooyoung Cho
- Department of Laboratory Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Moon Young Kim
- Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Young Uh
- Department of Laboratory Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea.
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Liu Y, Ji Y, Chen J, Zhang Y, Li X, Li X. Pioneering noninvasive colorectal cancer detection with an AI-enhanced breath volatilomics platform. Theranostics 2024; 14:4240-4255. [PMID: 39113791 PMCID: PMC11303087 DOI: 10.7150/thno.94950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 05/02/2024] [Indexed: 08/10/2024] Open
Abstract
Background: The sensitivity and specificity of current breath biomarkers are often inadequate for effective cancer screening, particularly in colorectal cancer (CRC). While a few exhaled biomarkers in CRC exhibit high specificity, they lack the requisite sensitivity for early-stage detection, thereby limiting improvements in patient survival rates. Methods: In this study, we developed an advanced Mass Spectrometry-based volatilomics platform, complemented by an enhanced breath sampler. The platform integrates artificial intelligence (AI)-assisted algorithms to detect multiple volatile organic compounds (VOCs) biomarkers in human breath. Subsequently, we applied this platform to analyze 364 clinical CRC and normal exhaled samples. Results: The diagnostic signatures, including 2-methyl, octane, and butyric acid, generated by the platform effectively discriminated CRC patients from normal controls with high sensitivity (89.7%), specificity (86.8%), and accuracy (AUC = 0.91). Furthermore, the metastatic signature correctly identified over 50% of metastatic patients who tested negative for carcinoembryonic antigen (CEA). Fecal validation indicated that elevated breath biomarkers correlated with an inflammatory response guided by Bacteroides fragilis in CRC. Conclusion: This study introduces a sophisticated AI-aided Mass Spectrometry-based platform capable of identifying novel and feasible breath biomarkers for early-stage CRC detection. The promising results position the platform as an efficient noninvasive screening test for clinical applications, offering potential advancements in early detection and improved survival rates for CRC patients.
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Affiliation(s)
- Yongqian Liu
- Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, P.R. China
| | - Yongyan Ji
- Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, P.R. China
| | - Jian Chen
- Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, P.R. China
| | - Yixuan Zhang
- Department of gastroenterology, Huadong hospital, Fudan University, Shanghai 200040, P.R. China
| | - Xiaowen Li
- Department of gastroenterology, Huadong hospital, Fudan University, Shanghai 200040, P.R. China
| | - Xiang Li
- Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, P.R. China
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3
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Tang M, Rong Y, Liu S, Wu Z, Ma G, Li X, Cai H. Potential role of lncRNA LOXL1-AS1 in human cancer development: a narrative review. Transl Cancer Res 2024; 13:1997-2011. [PMID: 38737681 PMCID: PMC11082674 DOI: 10.21037/tcr-23-1450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 02/29/2024] [Indexed: 05/14/2024]
Abstract
Background and Objective Long non-coding RNAs (lncRNAs) are a group of non-coding RNAs consisting of more than 200 nucleotides that are widely involved in various physiological and pathobiological processes in the body. LncRNA plays a crucial role in tumorigenesis and development with its unique functions, such as playing a role in a variety of biological processes of malignant tumors as a cancer-promoting factor or a cancer-suppressor factor. Lysyl oxidase-like protein 1-antisense RNA1 (LOXL1-AS1) is a novel functional lncRNA recently reported. This article reviews the current findings on the role of LOXL1-AS1 in cancer, and discusses the potential clinical significance and application prospects, in order to provide a theoretical basis and reference for the clinical diagnosis, treatment and screening of prognostic markers for malignant tumors. Methods The PubMed and Embase databases were searched using the keywords "cancer" or "tumor" or "neoplasm" and "LOXL1-AS1" for publications from 2018 to the present. The English literature was searched, with a focus on relevant articles. These articles validated the role and mechanism of LOXL1-AS1 in different cancers. Key Content and Findings LOXL1-AS1 is a recently reported novel lncRNA, which is abnormally expressed and upregulated in more than ten cancers, and is positively correlated with adverse clinical features and poor prognosis in cancer patients. LOXL1-AS1 competently binds to a variety of microRNAs to regulate the expression of downstream target genes and regulate related signaling pathways, including proliferation, migration, invasion and inhibition of malignant biological behaviors such as apoptosis. Conclusions LOXL1-AS1 is expected to become a novel biomarker for cancer diagnosis and treatment, with great potential as an independent prognostic indicator.
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Affiliation(s)
- Mingzheng Tang
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou, China
- General Surgery Clinical Medical Center, Gansu Provincial Hospital, Lanzhou, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Lanzhou, China
- National Health Council Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China
| | - Yao Rong
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou, China
- General Surgery Clinical Medical Center, Gansu Provincial Hospital, Lanzhou, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Lanzhou, China
- National Health Council Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China
| | - Songhua Liu
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou, China
- General Surgery Clinical Medical Center, Gansu Provincial Hospital, Lanzhou, China
| | - Zhihang Wu
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou, China
| | - Guorong Ma
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou, China
| | - Xiaofeng Li
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou, China
| | - Hui Cai
- General Surgery Clinical Medical Center, Gansu Provincial Hospital, Lanzhou, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Lanzhou, China
- National Health Council Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China
- The First Clinical Medical College of Lanzhou University, Lanzhou, China
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4
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Jiang L, Zhou X, Yang G, Chi H. Letter to the editor for the article "identification of biomarkers and potential therapeutic targets of kidney stone disease using bioinformatics". World J Urol 2024; 42:271. [PMID: 38683366 DOI: 10.1007/s00345-024-05006-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Accepted: 04/15/2024] [Indexed: 05/01/2024] Open
Affiliation(s)
- Lai Jiang
- Clinical Medical College, Southwest Medical University, Luzhou, 646000, China
| | - Xuancheng Zhou
- Clinical Medical College, Southwest Medical University, Luzhou, 646000, China
| | - Guanhu Yang
- Department of Specialty Medicine, Ohio University, Athens, OH, 45701, USA
| | - Hao Chi
- Clinical Medical College, Southwest Medical University, Luzhou, 646000, China.
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5
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Sathish G, Monavarshini LK, Sundaram K, Subramanian S, Kannayiram G. Immunotherapy for lung cancer. Pathol Res Pract 2024; 254:155104. [PMID: 38244436 DOI: 10.1016/j.prp.2024.155104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/22/2024]
Abstract
Immune checkpoint blockers have transformed non-small-cell lung cancer treatment, but they can lead to autoimmune and inflammatory side effects, leading to the concurrent use of immunosuppressive treatments. In this analysis, we delve into the potential of antibodies checkpoint blockade, focusing on CTLA-4 inhibition using ipilimumab, as a groundbreaking cancer immunotherapy. We also concentrate on the role of biomarkers, particularly PD-L1 activity and mutation significance, in predicting the response to programmed cell death protein 1 blockage and the prevalence of side effects associated with immune-related side effects. In describing the patterns of cancer response to immunotherapy, we underline the limitations of response assessment criteria like RECIST and World Health Organization. We also stress the necessity of ongoing studies and clinical trials, standardized guidelines, and additional research to improve response assessment in the era of immunotherapy.
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Affiliation(s)
- Girshani Sathish
- Department of Biotechnology, Dr. M.G.R. Educational and Research Institute, Maduravoyal, Chennai 600095, India
| | - L K Monavarshini
- Department of Biotechnology, Dr. M.G.R. Educational and Research Institute, Maduravoyal, Chennai 600095, India
| | - Keerthi Sundaram
- Department of Biotechnology, Dr. M.G.R. Educational and Research Institute, Maduravoyal, Chennai 600095, India
| | - Sendilvelan Subramanian
- Deparment of Mechanical Engineering, Dr.MGR Educational and Research Institute, Maduravoyal, Chennai 600095, India
| | - Gomathi Kannayiram
- Department of Biotechnology, Dr. M.G.R. Educational and Research Institute, Maduravoyal, Chennai 600095, India.
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Mukherjee S, Vagha S, Gadkari P. Navigating the Future: A Comprehensive Review of Artificial Intelligence Applications in Gastrointestinal Cancer. Cureus 2024; 16:e54467. [PMID: 38510911 PMCID: PMC10953838 DOI: 10.7759/cureus.54467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 02/18/2024] [Indexed: 03/22/2024] Open
Abstract
This comprehensive review explores the transformative role of artificial intelligence (AI) in the realm of gastrointestinal cancer. Gastrointestinal cancers present unique challenges, necessitating precise diagnostic tools and personalized treatment strategies. Leveraging AI, particularly machine learning and deep learning algorithms, has demonstrated remarkable potential in revolutionizing early detection, treatment planning, prognosis, and drug development. The analysis of current research and technological advancements underscores the capacity of AI to unravel intricate patterns within extensive datasets, providing actionable insights that enhance diagnostic accuracy and treatment efficacy. The transformative impact of AI on the landscape of gastrointestinal cancer is emphasized, signaling a paradigm shift towards more precise and targeted cancer care. The conclusion emphasizes the need for sustained research efforts and collaborative initiatives among AI researchers, healthcare professionals, and policymakers. By fostering interdisciplinary collaboration, we can navigate the evolving field of gastrointestinal cancer care, embracing the potential of AI to improve patient outcomes and contribute to a more effective and personalized approach to cancer management.
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Affiliation(s)
- Sreetama Mukherjee
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Sunita Vagha
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Pravin Gadkari
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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7
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Torlakovic EE, Cheung CC. Immunohistochemistry: The Importance of Precision Ontology to Precision Oncology. Appl Immunohistochem Mol Morphol 2024; 32:61-63. [PMID: 38251659 DOI: 10.1097/pai.0000000000001184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Affiliation(s)
- Emina E Torlakovic
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan and Saskatchewan Health Authority, Saskatoon, SK
| | - Carol C Cheung
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
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8
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Dantkale KS, Agrawal M. A Comprehensive Review of Current Trends in the Diagnosis and Treatment of Ovarian Germ Cell Tumors. Cureus 2024; 16:e52650. [PMID: 38380211 PMCID: PMC10877227 DOI: 10.7759/cureus.52650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 01/20/2024] [Indexed: 02/22/2024] Open
Abstract
Ovarian germ cell tumors constitute a rare and intricate spectrum of neoplasms characterized by diverse histological subtypes. This comprehensive review elucidates the classification, diagnosis, treatment, prognosis, and unique challenges associated with these tumors. The classification is rooted in histological attributes, with principal subtypes encompassing dysgerminoma, immature teratoma, yolk sac tumor (endodermal sinus tumor), choriocarcinoma, and mixed germ cell tumors. Each subtype bears distinct characteristics and clinical implications, necessitating precise diagnosis and tailored therapeutic strategies. Diagnosis hinges upon recognizing the broad clinical presentation, employing imaging techniques (such as ultrasound and MRI), evaluating tumor markers (alpha-fetoprotein and beta-human chorionic gonadotropin), and conducting histopathological examinations where necessary. Staging, primarily utilizing the International Federation of Gynecology and Obstetrics (FIGO) system, is pivotal in determining the extent of disease, guiding treatment choices, and facilitating prognostic assessment. Treatment modalities encompass surgery, chemotherapy (including standard regimens and emerging therapies), radiation therapy, targeted therapies, and immunotherapy. Prognosis is influenced by histological subtype, tumor stage, patient age, surgical success, response to chemotherapy, and tumor markers, while predictive biomarkers are continually emerging. Despite advances in treatment, ovarian germ cell tumors pose distinct challenges, including late diagnosis, treatment-related side effects, and the enigma of chemoresistance. An integral aspect of comprehensive care is supportive strategies to manage symptoms and offer psychological and emotional support. This review accentuates the vital role of early diagnosis and multidisciplinary care in optimizing outcomes. Future research directions and evolving clinical practices are explored in these intricate and distinctive malignancies, highlighting the dynamic landscape of ovarian germ cell tumors.
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Affiliation(s)
- Ketki S Dantkale
- Department of Obstetrics and Gynecology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research (DMIHER), Wardha, IND
| | - Manjusha Agrawal
- Department of Obstetrics and Gynecology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research (DMIHER), Wardha, IND
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9
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Ahmadieh-Yazdi A, Mahdavinezhad A, Tapak L, Nouri F, Taherkhani A, Afshar S. Using machine learning approach for screening metastatic biomarkers in colorectal cancer and predictive modeling with experimental validation. Sci Rep 2023; 13:19426. [PMID: 37940644 PMCID: PMC10632378 DOI: 10.1038/s41598-023-46633-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 11/03/2023] [Indexed: 11/10/2023] Open
Abstract
Colorectal cancer (CRC) liver metastasis accounts for the majority of fatalities associated with CRC. Early detection of metastasis is crucial for improving patient outcomes but can be delayed due to a lack of symptoms. In this research, we aimed to investigate CRC metastasis-related biomarkers by employing a machine learning (ML) approach and experimental validation. The gene expression profile of CRC patients with liver metastasis was obtained using the GSE41568 dataset, and the differentially expressed genes between primary and metastatic samples were screened. Subsequently, we carried out feature selection to identify the most relevant DEGs using LASSO and Penalized-SVM methods. DEGs commonly selected by these methods were selected for further analysis. Finally, the experimental validation was done through qRT-PCR. 11 genes were commonly selected by LASSO and P-SVM algorithms, among which seven had prognostic value in colorectal cancer. It was found that the expression of the MMP3 gene decreases in stage IV of colorectal cancer compared to other stages (P value < 0.01). Also, the expression level of the WNT11 gene was observed to increase significantly in this stage (P value < 0.001). It was also found that the expression of WNT5a, TNFSF11, and MMP3 is significantly lower, and the expression level of WNT11 is significantly higher in liver metastasis samples compared to primary tumors. In summary, this study has identified a set of potential biomarkers for CRC metastasis using ML algorithms. The findings of this research may provide new insights into identifying biomarkers for CRC metastasis and may potentially lay the groundwork for innovative therapeutic strategies for treatment of this disease.
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Affiliation(s)
- Amirhossein Ahmadieh-Yazdi
- Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
- Department of Medical Biotechnology, School of Advanced Medical Sciences and Technologies, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Ali Mahdavinezhad
- Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Leili Tapak
- Department of Biostatistics, School of Public Health and Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Fatemeh Nouri
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Amir Taherkhani
- Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Saeid Afshar
- Department of Medical Biotechnology, School of Advanced Medical Sciences and Technologies, Hamadan University of Medical Sciences, Hamadan, Iran.
- Cancer Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
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10
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Rodrigues WF, Miguel CB, de Abreu MCM, Neto JM, Oliveira CJF. Potential Associations between Vascular Biology and Hodgkin's Lymphoma: An Overview. Cancers (Basel) 2023; 15:5299. [PMID: 37958472 PMCID: PMC10649902 DOI: 10.3390/cancers15215299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/24/2023] [Accepted: 11/03/2023] [Indexed: 11/15/2023] Open
Abstract
Hodgkin's lymphoma (HL) is a lymphatic neoplasm typically found in the cervical lymph nodes. The disease is multifactorial, and in recent years, the relationships between various vascular molecules have been explored in the field of vascular biology. The connection between vascular biology and HL is intricate and the roles of several pathways remain unclear. This review summarizes the cellular and molecular relationships between vascular biology and HL. Proteins associated with various functions in vascular biology, including cytokines (TNF-α, IL-1, IL-13, and IL-21), chemokines (CXCL10, CXCL12, and CCL21), adhesion molecules (ELAM-1/VCAM-1), and growth factors (BDNF/NT-3, platelet-derived growth factor receptor-α), have been linked to tumor activity. Notable tumor activities include the induction of paracrine activation of NF-kB-dependent pathways, upregulation of adhesion molecule regulation, genome amplification, and effective loss of antigen presentation mediated by MHC-II. Preclinical study models, primarily those using cell culture, have been optimized for HL. Animal models, particularly mice, are also used as alternatives to complex biological systems, with studies primarily focusing on the physiopathogenic evaluation of the disease. These biomolecules warrant further study because they may shed light on obscure pathways and serve as targets for prevention and/or treatment interventions.
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Affiliation(s)
- Wellington Francisco Rodrigues
- Postgraduate Course in Tropical Medicine and Infectious Diseases, Federal University of Triangulo Mineiro, UFTM, Uberaba 38025-440, MG, Brazil; (C.B.M.); (C.J.F.O.)
- University Center of Mineiros, Unifimes, Mineiros 75833-130, GO, Brazil; (M.C.M.d.A.); (J.M.N.)
| | - Camila Botelho Miguel
- Postgraduate Course in Tropical Medicine and Infectious Diseases, Federal University of Triangulo Mineiro, UFTM, Uberaba 38025-440, MG, Brazil; (C.B.M.); (C.J.F.O.)
- University Center of Mineiros, Unifimes, Mineiros 75833-130, GO, Brazil; (M.C.M.d.A.); (J.M.N.)
| | | | - Jamil Miguel Neto
- University Center of Mineiros, Unifimes, Mineiros 75833-130, GO, Brazil; (M.C.M.d.A.); (J.M.N.)
| | - Carlo José Freire Oliveira
- Postgraduate Course in Tropical Medicine and Infectious Diseases, Federal University of Triangulo Mineiro, UFTM, Uberaba 38025-440, MG, Brazil; (C.B.M.); (C.J.F.O.)
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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12
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Al-Tashi Q, Saad MB, Sheshadri A, Wu CC, Chang JY, Al-Lazikani B, Gibbons C, Vokes NI, Zhang J, Lee JJ, Heymach JV, Jaffray D, Mirjalili S, Wu J. SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers. PATTERNS (NEW YORK, N.Y.) 2023; 4:100777. [PMID: 37602223 PMCID: PMC10435962 DOI: 10.1016/j.patter.2023.100777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 04/18/2023] [Accepted: 05/26/2023] [Indexed: 08/22/2023]
Abstract
Survival models exist to study relationships between biomarkers and treatment effects. Deep learning-powered survival models supersede the classical Cox proportional hazards (CoxPH) model, but substantial performance drops were observed on high-dimensional features because of irrelevant/redundant information. To fill this gap, we proposed SwarmDeepSurv by integrating swarm intelligence algorithms with the deep survival model. Furthermore, four objective functions were designed to optimize prognostic prediction while regularizing selected feature numbers. When testing on multicenter sets (n = 1,058) of four different cancer types, SwarmDeepSurv was less prone to overfitting and achieved optimal patient risk stratification compared with popular survival modeling algorithms. Strikingly, SwarmDeepSurv selected different features compared with classical feature selection algorithms, including the least absolute shrinkage and selection operator (LASSO), with nearly no feature overlapping across these models. Taken together, SwarmDeepSurv offers an alternative approach to model relationships between radiomics features and survival endpoints, which can further extend to study other input data types including genomics.
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Affiliation(s)
- Qasem Al-Tashi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Maliazurina B. Saad
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Carol C. Wu
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Joe Y. Chang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Bissan Al-Lazikani
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Christopher Gibbons
- Section of Patient-Centered Analytics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Natalie I. Vokes
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - John V. Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - David Jaffray
- Office of the Chief Technology and Digital Officer, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
| | - Jia Wu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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13
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Chaithanya P, Meshram RJ. Chemo Markers as Biomarkers in Septic Shock: A Comprehensive Review of Their Utility and Clinical Applications. Cureus 2023; 15:e42558. [PMID: 37637638 PMCID: PMC10460194 DOI: 10.7759/cureus.42558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 07/27/2023] [Indexed: 08/29/2023] Open
Abstract
Sepsis is a life-threatening condition characterized by a dysregulated host response to infection, often leading to septic shock. Early diagnosis and prompt intervention are crucial for improving patient outcomes. Chemo markers, which are measurable biological substances associated with the pathophysiology of septic shock, have emerged as potential biomarkers for the identification, risk stratification, and management of this condition. This comprehensive review aims to thoroughly evaluate the utility and clinical applications of chemo markers in septic shock. The review begins by discussing the criteria for ideal chemo markers, including specificity, sensitivity, dynamic range, stability, non-invasiveness, and prognostic value. These characteristics ensure accurate diagnosis, early detection, effective monitoring, and prediction of clinical outcomes. Furthermore, the review explores the role of chemo markers in monitoring treatment response and disease progression, highlighting their ability to serve as objective indicators for assessing the effectiveness of interventions and making timely adjustments in management strategies. Moreover, the prognostic value of chemo markers in predicting outcomes is discussed, emphasizing their association with mortality, hospital stays, and the development of complications. Integration of chemo markers into prognostic models or scoring systems enhances risk stratification and informs therapeutic decisions. The review also delves into recent advances in chemo marker research and technology, emphasizing the potential for discovering novel chemo markers with enhanced diagnostic and prognostic capabilities. It highlights the use of high-throughput proteomics, genomics, and transcriptomics in identifying specific molecular signatures associated with septic shock. This contributes to a deeper understanding of the complex immune and inflammatory responses involved. In conclusion, chemo markers have emerged as valuable biomarkers in septic shock, offering potential utility in diagnosis, risk stratification, treatment monitoring, and prediction of outcomes. Continued research, validation, and integration into clinical practice are necessary to fully realize their potential in improving patient care and outcomes in septic shock.
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Affiliation(s)
- Pulivarthi Chaithanya
- Pediatrics, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Revat J Meshram
- Pediatrics, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Wardha, IND
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14
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Ma B, Bao S, Li Y. Identification and validation of m6A-GPI signatures as a novel prognostic model for colorectal cancer. Front Oncol 2023; 13:1145753. [PMID: 37427112 PMCID: PMC10328717 DOI: 10.3389/fonc.2023.1145753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/12/2023] [Indexed: 07/11/2023] Open
Abstract
In order to develop an N6-methyladenosine-related gene prognostic index (m6A-GPI) that can predict the prognosis in colorectal cancer (CRC), we obtained m6A-related differentially expressed genes (DEGs) based on The Cancer Genome Atlas (TCGA) and m6Avar database, seven genes were screened by weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) analysis. Then, m6A-GPI was constructed based on the risk score. Survival analysis indicated that patients in the lower m6A-GPI group have more prolonged disease-free survival (DFS), and different clinical characteristic groups (tumor site and stage) also showed differential risk scores. In the analysis of the molecular characteristics, the risk score is positively associated with homologous recombination defects (HRD), copy number alterations (CNA), and the mRNA expression-based stemness index (mRNAsi). In addition, m6A-GPI also plays an essential role in tumor immune cell infiltration. The immune cell infiltration in the low m6A-GPI group is significantly higher in CRC. Moreover, we found that CIITA, one of the genes in m6A-GPI was up-regulated in CRC tissues based on real-time RT-PCR and Western blot. m6A-GPI is a promising prognostic biomarker that can be used to distinguish the prognosis of CRC patients in CRC.
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Affiliation(s)
- Bin Ma
- Department of Colorectal Surgery, Cancer Hospital of China Medical University, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, Shenyang, China
- The Liaoning Provincial Key Laboratory of Interdisciplinary Research on Gastrointestinal Tumor Combining Medicine with Engineering, Shenyang, China
| | - Simeng Bao
- Central Laboratory, Cancer Hospital of China Medical University, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Yongmin Li
- Department of Colorectal Surgery, Cancer Hospital of China Medical University, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, Shenyang, China
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15
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Le X, Elamin YY, Zhang J. New Actions on Actionable Mutations in Lung Cancers. Cancers (Basel) 2023; 15:cancers15112917. [PMID: 37296880 DOI: 10.3390/cancers15112917] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 05/24/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
Actionable mutations refer to DNA alterations that, if detected, would be expected to affect patients' response to treatments [...].
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Affiliation(s)
- Xiuning Le
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yasir Y Elamin
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
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