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Aalami AH, Shahriari A, Mazaheri M, Aalami F, Amirabadi A, Sahebkar A. Diagnostic accuracy of tumor M2-pyruvate kinase (tM2-PK) as a non-invasive biomarker in colorectal cancer: A systematic review and meta-analysis. Clin Biochem 2023; 120:110652. [PMID: 37757965 DOI: 10.1016/j.clinbiochem.2023.110652] [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: 06/16/2023] [Revised: 09/16/2023] [Accepted: 09/20/2023] [Indexed: 09/29/2023]
Abstract
INTRODUCTION The tumor pyruvate kinase M2 isoform (tM2-PK) is a glycolytic enzyme isoform that is present on the surface of rapidly proliferating cancer cells. The objective of this investigation was to assess the efficacy of the tM2-PK measurement assay in detecting colorectal cancer (CRC) through the analysis of serum/plasma and stool samples obtained from patients. METHODS The pooled diagnostic performance measures, including sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), the area under the curve (AUC), Q*index, and summary receiver-operating characteristic curve (SROC), were computed using the Meta-Disc V.1.4 and Comprehensive Meta-Analysis V.3.3 software. The statistical methods of I2 and chi-square were employed to assess the presence of heterogeneity. The estimation of publication bias was conducted through the implementation of Begg's rank correlation and Egger's regression asymmetry tests. RESULTS A total of 28 studies were found, involving 2900 participants (1560 cases and 1340 controls). The diagnostic accuracy of tM2-PK was calculated in CRC based on the pooled sensitivity of 83.70% (95% CI: 82.0% - 85.30%), specificity of 74.0% (95% CI: 72.0% - 76.0%), PLR of 4.432 (95% CI: 3.33 - 5.60), NLR of 0.187 (95% CI: 0.144 - 0.243), DOR of 30.182 (95% CI: 19.761 - 46.10) as well as AUC at 91.6%, and Q*-index at 85.0%. Publication bias was seen based on Begg's (p = 0.0006) and Egger's (p = 0.00015) tests. CONCLUSION The results demonstrate that tM2-PK exhibits promise as a fair marker for CTRC, with the potential to serve as a non-invasive biomarker.
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Affiliation(s)
- Amir Hossein Aalami
- Department of Nutrition and Integrative Physiology, College of Health, University of Utah, Salt Lake City, UT, USA; Department of Internal Medicine, Division of Nephrology, University of Utah, Salt Lake City, UT, USA.
| | - Ali Shahriari
- Department of Internal Medicine, Faculty of Medicine, Mashhad Medical Sciences, Islamic Azad University, Mashhad, Iran
| | - Mohammad Mazaheri
- Department of Molecular, Cell and Systems Biology, College of Natural and Agricultural Sciences, University of California Riverside, Riverside, CA, USA
| | - Farnoosh Aalami
- Student Research Committee, Faculty of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran
| | - Amir Amirabadi
- Department of Internal Medicine, Faculty of Medicine, Mashhad Medical Sciences, Islamic Azad University, Mashhad, Iran
| | - Amirhossein Sahebkar
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Applied Biomedical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
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Jiang H, Zhou S, Li G. Novel biomarkers used for early diagnosis and tyrosine kinase inhibitors as targeted therapies in colorectal cancer. Front Pharmacol 2023; 14:1189799. [PMID: 37719843 PMCID: PMC10502318 DOI: 10.3389/fphar.2023.1189799] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 08/14/2023] [Indexed: 09/19/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common and second most lethal type of cancer worldwide, presenting major health risks as well as economic costs to both people and society. CRC survival chances are significantly higher if the cancer is diagnosed and treated early. With the development of molecular biology, numerous initiatives have been undertaken to identify novel biomarkers for the early diagnosis of CRC. Pathological disorders can be diagnosed at a lower cost with the help of biomarkers, which can be detected in stool, blood, and tissue samples. Several lines of evidence suggest that the gut microbiota could be used as a biomarker for CRC screening and treatment. CRC treatment choices include surgical resection, chemotherapy, immunotherapy, gene therapy, and combination therapies. Targeted therapies are a relatively new and promising modality of treatment that has been shown to increase patients' overall survival (OS) rates and can inhibit cancer cell development. Several small-molecule tyrosine kinase inhibitors (TKIs) are being investigated as potential treatments due to our increasing awareness of CRC's molecular causes and oncogenic signaling. These compounds may inhibit critical enzymes in controlling signaling pathways, which are crucial for CRC cells' development, differentiation, proliferation, and survival. On the other hand, only one of the approximately 42 TKIs that demonstrated anti-tumor effects in pre-clinical studies has been licensed for clinical usage in CRC. A significant knowledge gap exists when bringing these tailored medicines into the clinic. As a result, the emphasis of this review is placed on recently discovered biomarkers for early diagnosis as well as tyrosine kinase inhibitors as possible therapy options for CRC.
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Shields PG. Role of untargeted omics biomarkers of exposure and effect for tobacco research. ADDICTION NEUROSCIENCE 2023; 7:100098. [PMID: 37396411 PMCID: PMC10310069 DOI: 10.1016/j.addicn.2023.100098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.
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Affiliation(s)
- Peter G. Shields
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH
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Jiang S, Wang T, Zhang KH. Data-driven decision-making for precision diagnosis of digestive diseases. Biomed Eng Online 2023; 22:87. [PMID: 37658345 PMCID: PMC10472739 DOI: 10.1186/s12938-023-01148-1] [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: 01/14/2023] [Accepted: 08/15/2023] [Indexed: 09/03/2023] Open
Abstract
Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.
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Affiliation(s)
- Song Jiang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Ting Wang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Kun-He Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
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Liao J, Li X, Gan Y, Han S, Rong P, Wang W, Li W, Zhou L. Artificial intelligence assists precision medicine in cancer treatment. Front Oncol 2023; 12:998222. [PMID: 36686757 PMCID: PMC9846804 DOI: 10.3389/fonc.2022.998222] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/22/2022] [Indexed: 01/06/2023] Open
Abstract
Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.
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Affiliation(s)
- Jinzhuang Liao
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiaoying Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yu Gan
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Shuangze Han
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Pengfei Rong
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Wei Wang
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Wei Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Li Zhou
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,Department of Pathology, The Xiangya Hospital of Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
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Fang T, Zhang L, Yin X, Wang Y, Zhang X, Bian X, Jiang X, Yang S, Xue Y. The prognostic marker elastin correlates with epithelial-mesenchymal transition and vimentin-positive fibroblasts in gastric cancer. J Pathol Clin Res 2022; 9:56-72. [PMID: 36226731 PMCID: PMC9732685 DOI: 10.1002/cjp2.298] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/19/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022]
Abstract
Elastin (ELN) fibers are essential constituents of the tumor microenvironment of gastric cancer (GC). However, few studies have investigated the clinical prognostic significance of ELN in GC. We screened for molecular markers that were highly related to distant metastasis by transcriptome sequencing. The Cancer Genome Atlas (TCGA) and Harbin Medical University (HMU) validation cohorts were used to validate ELN expression and to explore molecular mechanisms. Immunohistochemistry for ELN, vimentin (VIM), and fibroblast activation protein, and elastic fiber-specific staining were used to evaluate the relationship between ELN and prognosis. R studio was used to construct a nomogram prognostic model. In this study, we found that ELN mRNA levels were significantly higher in cancer tissues and were associated with poor prognosis in TCGA and HMU patients. Gene set enrichment analysis showed that ELN was mainly enriched in the epithelial-mesenchymal transition (EMT) pathway. The mRNA expression of ELN was positively correlated with fibroblast molecular markers, especially VIM. For validation, we collected a tissue microarray containing 180 pairs of samples. We found that ELN was positively correlated with VIM expression in cancer tissue but not in paracancerous tissues by immunohistochemistry staining. Univariate and multivariate analyses showed that the expression of ELN and lymph node metastasis rate were independent predictors for overall survival. Moreover, a nomogram model was used to evaluate the risk of death by combining the expression of ELN and lymph node metastasis rate. ELN may play an important role in the progression of GC by regulating EMT and is a useful prognostic indicator in predicting the prognosis of GC.
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Affiliation(s)
- Tianyi Fang
- Department of Gastroenterological SurgeryHarbin Medical University Cancer Hospital, Harbin Medical UniversityHarbinPR China
| | - Lei Zhang
- Department of PathologyHarbin Medical UniversityHarbinPR China
| | - Xin Yin
- Department of Gastroenterological SurgeryHarbin Medical University Cancer Hospital, Harbin Medical UniversityHarbinPR China
| | - Yufei Wang
- Department of Gastroenterological SurgeryHarbin Medical University Cancer Hospital, Harbin Medical UniversityHarbinPR China
| | - Xinghai Zhang
- Department of PathologyHarbin Medical UniversityHarbinPR China
| | - Xiulan Bian
- Department of PathologyHarbin Medical UniversityHarbinPR China
| | - Xinju Jiang
- Department of PathologyHarbin Medical UniversityHarbinPR China
| | - Shuo Yang
- Department of PathologyHarbin Medical UniversityHarbinPR China
| | - Yingwei Xue
- Department of Gastroenterological SurgeryHarbin Medical University Cancer Hospital, Harbin Medical UniversityHarbinPR China
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Wong GYM, Diakos C, Hugh TJ, Molloy MP. Proteomic Profiling and Biomarker Discovery in Colorectal Liver Metastases. Int J Mol Sci 2022; 23:ijms23116091. [PMID: 35682769 PMCID: PMC9181741 DOI: 10.3390/ijms23116091] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 05/27/2022] [Accepted: 05/27/2022] [Indexed: 12/14/2022] Open
Abstract
Colorectal liver metastases (CRLM) are the leading cause of death among patients with metastatic colorectal cancer (CRC). As part of multimodal therapy, liver resection is the mainstay of curative-intent treatment for select patients with CRLM. However, effective treatment of CRLM remains challenging as recurrence occurs in most patients after liver resection. Proposed clinicopathologic factors for predicting recurrence are inconsistent and lose prognostic significance over time. The rapid development of next-generation sequencing technologies and decreasing DNA sequencing costs have accelerated the genomic profiling of various cancers. The characterisation of genomic alterations in CRC has significantly improved our understanding of its carcinogenesis. However, the functional context at the protein level has not been established for most of this genomic information. Furthermore, genomic alterations do not always result in predicted changes in the corresponding proteins and cancer phenotype, while post-transcriptional and post-translational regulation may alter synthesised protein levels, affecting phenotypes. More recent advancements in mass spectrometry-based technology enable accurate protein quantitation and comprehensive proteomic profiling of cancers. Several studies have explored proteomic biomarkers for predicting CRLM after oncologic resection of primary CRC and recurrence after curative-intent resection of CRLM. The current review aims to rationalise the proteomic complexity of CRC and explore the potential applications of proteomic biomarkers in CRLM.
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Affiliation(s)
- Geoffrey Yuet Mun Wong
- Department of Upper Gastrointestinal Surgery, Royal North Shore Hospital, Sydney, NSW 2065, Australia;
- Northern Clinical School, The University of Sydney, Sydney, NSW 2065, Australia;
- Correspondence:
| | - Connie Diakos
- Northern Clinical School, The University of Sydney, Sydney, NSW 2065, Australia;
- Department of Medical Oncology, Royal North Shore Hospital, Sydney, NSW 2065, Australia
| | - Thomas J. Hugh
- Department of Upper Gastrointestinal Surgery, Royal North Shore Hospital, Sydney, NSW 2065, Australia;
- Northern Clinical School, The University of Sydney, Sydney, NSW 2065, Australia;
| | - Mark P. Molloy
- Bowel Cancer and Biomarker Research Laboratory, Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia;
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