1
|
Dang Q, Zuo L, Hu X, Zhou Z, Chen S, Liu S, Ba Y, Zuo A, Xu H, Weng S, Zhang Y, Luo P, Cheng Q, Liu Z, Han X. Molecular subtypes of colorectal cancer in the era of precision oncotherapy: Current inspirations and future challenges. Cancer Med 2024; 13:e70041. [PMID: 39054866 PMCID: PMC11272957 DOI: 10.1002/cam4.70041] [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: 03/29/2024] [Revised: 07/07/2024] [Accepted: 07/12/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is among the most hackneyed malignancies. Even patients with identical clinical symptoms and the same TNM stage still exhibit radically different clinical outcomes after receiving equivalent treatment regimens, indicating extensive heterogeneity of CRC. Myriad molecular subtypes of CRC have been exploited for decades, including the most compelling consensus molecular subtype (CMS) classification that has been broadly applied for patient stratification and biomarker-drug combination formulation. Encountering barriers to clinical translation, however, CMS classification fails to fully reflect inter- or intra-tumor heterogeneity of CRC. As a consequence, addressing heterogeneity and precisely managing CRC patients with unique characteristics remain arduous tasks for clinicians. REVIEW In this review, we systematically summarize molecular subtypes of CRC and further elaborate on their clinical applications, limitations, and future orientations. CONCLUSION In recent years, exploration of subtypes through cell lines, animal models, patient-derived xenografts (PDXs), organoids, and clinical trials contributes to refining biological insights and unraveling subtype-specific therapies in CRC. Therapeutic interventions including nanotechnology, clustered regulatory interspaced short palindromic repeat/CRISPR-associated nuclease 9 (CRISPR/Cas9), gut microbiome, and liquid biopsy are powerful tools with the possibility to shift the immunologic landscape and outlook for CRC precise medicine.
Collapse
Affiliation(s)
- Qin Dang
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
- Department of Colorectal SurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Lulu Zuo
- Center for Reproductive MedicineThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Xinru Hu
- Department of Cardiology, West China HospitalSichuan UniversityChengduSichuanChina
| | - Zhaokai Zhou
- Department of UrologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Shuang Chen
- Center for Reproductive MedicineThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Shutong Liu
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Yuhao Ba
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Anning Zuo
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Hui Xu
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Siyuan Weng
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Yuyuan Zhang
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Peng Luo
- Department of Oncology, Zhujiang HospitalSouthern Medical UniversityGuangzhouGuangdongChina
| | - Quan Cheng
- Department of Neurosurgery, Xiangya HospitalCentral South UniversityChangshaHunanChina
| | - Zaoqu Liu
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
- Interventional Treatment and Clinical Research Center of Henan ProvinceZhengzhouHenanChina
- Interventional Institute of Zhengzhou UniversityZhengzhouHenanChina
- Institute of Basic Medical SciencesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Xinwei Han
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
- Interventional Treatment and Clinical Research Center of Henan ProvinceZhengzhouHenanChina
- Interventional Institute of Zhengzhou UniversityZhengzhouHenanChina
| |
Collapse
|
2
|
Xu Z, Li W, Dong X, Chen Y, Zhang D, Wang J, Zhou L, He G. Precision medicine in colorectal cancer: Leveraging multi-omics, spatial omics, and artificial intelligence. Clin Chim Acta 2024; 559:119686. [PMID: 38663471 DOI: 10.1016/j.cca.2024.119686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/22/2024] [Accepted: 04/22/2024] [Indexed: 05/03/2024]
Abstract
Colorectal cancer (CRC) is a leading cause of cancer-related deaths. Recent advancements in genomic technologies and analytical approaches have revolutionized CRC research, enabling precision medicine. This review highlights the integration of multi-omics, spatial omics, and artificial intelligence (AI) in advancing precision medicine for CRC. Multi-omics approaches have uncovered molecular mechanisms driving CRC progression, while spatial omics have provided insights into the spatial heterogeneity of gene expression in CRC tissues. AI techniques have been utilized to analyze complex datasets, identify new treatment targets, and enhance diagnosis and prognosis. Despite the tumor's heterogeneity and genetic and epigenetic complexity, the fusion of multi-omics, spatial omics, and AI shows the potential to overcome these challenges and advance precision medicine in CRC. The future lies in integrating these technologies to provide deeper insights and enable personalized therapies for CRC patients.
Collapse
Affiliation(s)
- Zishan Xu
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China
| | - Wei Li
- School of Forensic Medicine, Xinxiang Medical University, Xinxiang 453000, China
| | - Xiangyang Dong
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China
| | - Yingying Chen
- School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang 453000, China
| | - Dan Zhang
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China
| | - Jingnan Wang
- Xinxiang Medical University SanQuan Medical College, Xinxiang 453003, China
| | - Lin Zhou
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Guoyang He
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China.
| |
Collapse
|
3
|
Kiran N, Yashaswini C, Maheshwari R, Bhattacharya S, Prajapati BG. Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies. ACS Pharmacol Transl Sci 2024; 7:967-990. [PMID: 38633600 PMCID: PMC11019743 DOI: 10.1021/acsptsci.4c00008] [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: 01/10/2024] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 04/19/2024]
Abstract
Precision medicine is transforming colorectal cancer treatment through the integration of advanced technologies and biomarkers, enhancing personalized and effective disease management. Identification of key driver mutations and molecular profiling have deepened our comprehension of the genetic alterations in colorectal cancer, facilitating targeted therapy and immunotherapy selection. Biomarkers such as microsatellite instability (MSI) and DNA mismatch repair deficiency (dMMR) guide treatment decisions, opening avenues for immunotherapy. Emerging technologies such as liquid biopsies, artificial intelligence, and machine learning promise to revolutionize early detection, monitoring, and treatment selection in precision medicine. Despite these advancements, ethical and regulatory challenges, including equitable access and data privacy, emphasize the importance of responsible implementation. The dynamic nature of colorectal cancer, with its tumor heterogeneity and clonal evolution, underscores the necessity for adaptive and personalized treatment strategies. The future of precision medicine in colorectal cancer lies in its potential to enhance patient care, clinical outcomes, and our understanding of this intricate disease, marked by ongoing evolution in the field. The current reviews focus on providing in-depth knowledge on the various and diverse approaches utilized for precision medicine against colorectal cancer, at both molecular and biochemical levels.
Collapse
Affiliation(s)
- Neelakanta
Sarvashiva Kiran
- Department
of Biotechnology, School of Applied Sciences, REVA University, Bengaluru, Karnataka 560064, India
| | - Chandrashekar Yashaswini
- Department
of Biotechnology, School of Applied Sciences, REVA University, Bengaluru, Karnataka 560064, India
| | - Rahul Maheshwari
- School
of Pharmacy and Technology Management, SVKM’s
Narsee Monjee Institute of Management Studies (NMIMS) Deemed-to-University, Green Industrial Park, TSIIC,, Jadcherla, Hyderabad 509301, India
| | - Sankha Bhattacharya
- School
of Pharmacy and Technology Management, SVKM’S
NMIMS Deemed-to-be University, Shirpur, Maharashtra 425405, India
| | - Bhupendra G. Prajapati
- Shree.
S. K. Patel College of Pharmaceutical Education and Research, Ganpat University, Kherva, Gujarat 384012, India
| |
Collapse
|
4
|
Moetamani-Ahmadi M, Mahmoud Ahmadzadeh A, Alaei M, Zafari N, Negahbanzaferanloo Z, Pourbagher-Shahri AM, Forouzanfar F, Fiuji H, Mahaki H, Khazaei M, Gataa IS, Ferns GA, Peters GJ, Batra J, Lam AKY, Giovannetti E, TanzadehPanah H, Avan A. Pegylated nanoliposomal cisplatin ameliorates chemotherapy-induced peripheral neuropathy. Int J Pharm 2024; 652:123839. [PMID: 38266944 DOI: 10.1016/j.ijpharm.2024.123839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 01/19/2024] [Accepted: 01/20/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND Chemotherapy-induced peripheral neuropathy (CIPN) is a serious adverse effect of cisplatin. The current study aimed to determine whether PEGylated nanoliposomal cisplatin can limit CIPN in an animal model. METHODS Cisplatin-loaded PEGylated liposome nanoparticles (Cis-PL) were produced as a combination of lecithin, cholesterol, and DSPE-mPEG2000 in a molar ratio of 50:45:5 and were characterized by polydispersity index (PDI), zeta potential, Field emission scanning electron microscopy (FESEM) analysis, as well as encapsulation efficiency (EE). Fifteen male rats were provided and randomly divided into 3 groups including Cis-PL group, cisplatin group, and control group. Behavioural tests (hot-plate test and acetone drop test) were used for evaluating CIPN. Moreover, oxidative stress markers and histopathological analysis were applied. Treatment-related toxicity was assessed by haematological analysis as well as liver and renal function tests. RESULTS Cis-PL had an average particle size of 125.4, PDI of 0.127, and zeta potential of -40.9 mV. Moreover, the Cis-PL exhibited a high EE as well as low levels of leakage rate at 25 °C. In a hot-plate test, paw withdrawal latency was longer in Cis-PL group in comparison to rats treated with cisplatin. A lower number of withdrawal responses was detected during acetone drop test in Cis-PL group than in cisplatin-treated rats. Assessment of oxidative stress markers showed that Cis-PL could improve oxidative stress. Additionally, histopathological assessment demonstrated that the number of satellite cells was significantly reduced in the dorsal root ganglion (DRG) of Cis-PL-treated rats compared with those treated with cisplatin. The cisplatin group had elevated white blood cells counts, reduced platelet counts, and higher levels of bilirubin, ALT (alanine aminotransferase, and AST (aspartate aminotransferase), and creatinine compared with the control group, which was ameliorated in Cis-PL group. CONCLUSIONS Data from the current study support the previous hypothesis that Cisplatin-loaded PEGylated liposome could be a promising solution for CIPN in the future by modulating oxidative stress and preventing glial cell activation in DRG, suggesting further clinical studies to investigate the efficacy of this agent and its potential application in clinical practice.
Collapse
Affiliation(s)
- Mehrdad Moetamani-Ahmadi
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amir Mahmoud Ahmadzadeh
- Transplant Research Center, Clinical Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Radiology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Maryam Alaei
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Nima Zafari
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | | | - Fatemeh Forouzanfar
- Medical Toxicology Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hamid Fiuji
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Location VUMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Hanie Mahaki
- Vascular and Endovascular Surgery Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Khazaei
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Gordon A Ferns
- Brighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, Sussex BN1 9PH, UK
| | - Godefridus J Peters
- Professor In Biochemistry, Medical University of Gdansk, Gdansk, Poland; Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Location VUMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jyotsna Batra
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, 4059, Australia; Translational Research Institute, Centre for Genomics and Personalised Health, Brisbane, Australia
| | - Alfred King-Yin Lam
- Pathology, School of Medicine and Dentistry, Gold Coast Campus, Griffith University, Gold Coast, QLD 4222, Australia
| | - Elisa Giovannetti
- Cancer Pharmacology Lab, AIRC Start up Unit, Fondazione Pisana per La Scienza, Pisa, Italy; Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Location VUMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Hamid TanzadehPanah
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Antimicrobial Resistance Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Faculty of Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, 4059, Australia; College of Medicine and Health Sciences, National University of Science and Technology, Sultanate of Oman.
| |
Collapse
|
5
|
Li X, Li J, Li J, Liu N, Zhuang L. Development and validation of epigenetic modification-related signals for the diagnosis and prognosis of colorectal cancer. BMC Genomics 2024; 25:51. [PMID: 38212708 PMCID: PMC10782594 DOI: 10.1186/s12864-023-09815-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 11/18/2023] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is one of the world's most common malignancies. Epigenetics is the study of heritable changes in characteristics beyond the DNA sequence. Epigenetic information is essential for maintaining specific expression patterns of genes and the normal development of individuals, and disorders of epigenetic modifications may alter the expression of oncogenes and tumor suppressor genes and affect the development of cancer. This study elucidates the relationship between epigenetics and the prognosis of CRC patients by developing a predictive model to explore the potential value of epigenetics in the treatment of CRC. METHODS Gene expression data of CRC patients' tumor tissue and controls were downloaded from GEO database. Combined with the 720 epigenetic-related genes (ERGs) downloaded from EpiFactors database, prognosis-related epigenetic genes were selected by univariate cox and LASSO analyses. The Kaplan-Meier and ROC curve were used to analyze the accuracy of the model. Data of 238 CRC samples with survival data downloaded from the GSE17538 were used for validation. Finally, the risk model is combined with the clinical characteristics of CRC patients to perform univariate and multivariate cox regression analysis to obtain independent risk factors and draw nomogram. Then we evaluated the accuracy of its prediction by calibration curves. RESULTS A total of 2906 differentially expressed genes (DEGs) were identified between CRC and control samples. After overlapping DEGs with 720 ERGs, 56 epigenetic-related DEGs (DEERGs) were identified. Combining univariate and LASSO regression analysis, the 8 epigenetic-related genes-based risk score model of CRC was established. The ROC curves and survival difference of high and low risk groups revealed the good performance of the risk score model based on prognostic biomarkers in both training and validation sets. A nomogram with good performance to predict the survival of CRC patients were established based on age, NM stage and risk score. The calibration curves showed that the prognostic model had good predictive performance. CONCLUSION In this study, an epigenetically relevant 8-gene signature was constructed that can effectively predict the prognosis of CRC patients and provide potential directions for targeted therapies for CRC.
Collapse
Affiliation(s)
- Xia Li
- Department of Gastroenterology and Hepatology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China
| | - Jingjing Li
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, Heilongjiang Province, China
| | - Jie Li
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, Heilongjiang Province, China
| | - Nannan Liu
- Department of Gastroenterology and Hepatology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China
| | - Liwei Zhuang
- Department of Gastroenterology and Hepatology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China.
| |
Collapse
|
6
|
Pourali G, Kazemi D, Chadeganipour AS, Arastonejad M, Kashani SN, Pourali R, Maftooh M, Akbarzade H, Fiuji H, Hassanian SM, Ghayour-Mobarhan M, Ferns GA, Khazaei M, Avan A. Microbiome as a biomarker and therapeutic target in pancreatic cancer. BMC Microbiol 2024; 24:16. [PMID: 38183010 PMCID: PMC10768369 DOI: 10.1186/s12866-023-03166-4] [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: 04/11/2023] [Accepted: 12/18/2023] [Indexed: 01/07/2024] Open
Abstract
Studying the effects of the microbiome on the development of different types of cancer has recently received increasing research attention. In this context, the microbial content of organs of the gastrointestinal tract has been proposed to play a potential role in the development of pancreatic cancer (PC). Proposed mechanisms for the pathogenesis of PC include persistent inflammation caused by microbiota leading to an impairment of antitumor immune surveillance and altered cellular processes in the tumor microenvironment. The limited available diagnostic markers that can currently be used for screening suggest the importance of microbial composition as a non-invasive biomarker that can be used in clinical settings. Samples including saliva, stool, and blood can be analyzed by 16 s rRNA sequencing to determine the relative abundance of specific bacteria. Studies have shown the potentially beneficial effects of prebiotics, probiotics, antibiotics, fecal microbial transplantation, and bacteriophage therapy in altering microbial diversity, and subsequently improving treatment outcomes. In this review, we summarize the potential impact of the microbiome in the pathogenesis of PC, and the role these microorganisms might play as biomarkers in the diagnosis and determining the prognosis of patients. We also discuss novel treatment methods being used to minimize or prevent the progression of dysbiosis by modulating the microbial composition. Emerging evidence is supportive of applying these findings to improve current therapeutic strategies employed in the treatment of PC.
Collapse
Affiliation(s)
- Ghazaleh Pourali
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Danial Kazemi
- Student Research Committee, Isfahan University of Medical Sciences, Hezar Jerib Street, Isfahan, Iran
| | | | - Mahshid Arastonejad
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | | | - Roozbeh Pourali
- Student Research Committee, Faculty of Veterinary Medicine, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mina Maftooh
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hamed Akbarzade
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hamid Fiuji
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyed Mahdi Hassanian
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Ghayour-Mobarhan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Brighton & Sussex Medical School, Department of Medical Education, Falmer, Brighton, Sussex, BN1 9PH, UK
| | - Majid Khazaei
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
- College of Medicine, University of Warith Al-Anbiyaa, Karbala, Iraq.
- School of Mechanical, Medical and Process Engineering, Science and Engineering Faculty, Queensland University of Technology, 2 George St, Brisbane City, QLD, 4000, Australia.
| |
Collapse
|
7
|
Gholamalizadeh H, Zafari N, Velayati M, Fiuji H, Maftooh M, Ghorbani E, Hassanian SM, Khazaei M, Ferns GA, Nazari E, Avan A. Prognostic value of primary tumor location in colorectal cancer: an updated meta-analysis. Clin Exp Med 2023; 23:4369-4383. [PMID: 37405571 DOI: 10.1007/s10238-023-01120-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 06/12/2023] [Indexed: 07/06/2023]
Abstract
The clinical, histological, and molecular differences between right-sided colon cancer (RCC) and left-sided colon cancer (RCC) have received considerable attention. Over the past decade, many articles have been published concerning the association between primary tumor location (PTL) of colorectal cancer and survival outcomes. Therefore, there is a growing need for an updated meta-analysis integrating the outcomes of recent studies to determine the prognostic role of right vs left-sidedness of PTL in patients with colorectal cancer. We conducted a comprehensive database review using PubMed, SCOPUS, and Cochrane library databases from February 2016 to March 2023 for prospective or retrospective studies reporting data on overall survival (OS) and cancer-specific survival (CSS) of RCC compared with LCC. A total of 60 cohort studies comprising 1,494,445 patients were included in the meta-analysis. We demonstrated that RCC is associated with a significantly increased risk of death compared with LCC by 25% (hazard ratio (HR), 1.25; 95% confidence interval (CI), 1.19-1.31; I2 = 78.4%; Z = 43.68). Results showed that patients with RCC have a worse OS compared with LCC only in advanced stages (Stage III: HR, 1.275; 95% CI 1.16-1.4; P = 0.0002; I2 = 85.8%; Stage IV: HR, 1.34; 95% CI 1.25-1.44; P < 0.0001; I2 = 69.2%) but not in primary stages (Stage I/II: HR, 1.275; 95% CI 1.16-1.4; P = 0.0002; I2 = 85.8%). Moreover, a meta-analysis of 13 studies including 812,644 patients revealed that there is no significant difference in CSS between RCC and LCC (HR, 1.121; 95% CI 0.97-1.3; P = 0.112). Findings from the present meta-analysis highlight the importance of PTL in clinical decision-making for patients with CRC, especially in advanced stages. We provide further evidence supporting the hypothesis that RCC and LCC are distinct disease entities that should be managed differently.
Collapse
Affiliation(s)
- Hanieh Gholamalizadeh
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Nima Zafari
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahla Velayati
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hamid Fiuji
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mina Maftooh
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Elnaz Ghorbani
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyed Mahdi Hassanian
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Khazaei
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Division of Medical Education, Brighton and Sussex Medical School, Falmer, Brighton, Sussex, BN1 9PH, UK
| | - Elham Nazari
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
- College of Medicine, University of Warith Al-Anbiyaa, Karbala, Iraq.
- School of Mechanical, Medical and Process Engineering, Science and Engineering Faculty, Queensland University of Technology, 2 George St, Brisbane, QLD, 4000, Australia.
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia.
| |
Collapse
|
8
|
Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol 2023; 16:114. [PMID: 38012673 PMCID: PMC10680201 DOI: 10.1186/s13045-023-01514-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.
Collapse
Affiliation(s)
- Chaoyi Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jianhui Yang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| |
Collapse
|
9
|
Rafieenia F, Ebrahimi SO, Emadi ES, Taheri F, Reiisi S. Bioengineered chimeric tRNA/pre-miRNAs as prodrugs in cancer therapy. Biotechnol Prog 2023; 39:e3387. [PMID: 37608520 DOI: 10.1002/btpr.3387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 08/24/2023]
Abstract
Today, biologic prodrugs have led to targeting specific tumor markers and have increased specificity and selectivity in cancer therapy. Various studies have shown the role of ncRNAs in cancer pathology and tumorigenesis and have suggested that ncRNAs, especially miRNAs, are valuable molecules in understanding cancer biology and therapeutic processes. Most miRNAs-based research and treatment are limited to chemically synthesized miRNAs. Synthetic alterations in these miRNA mimics may affect their folding, safety profile, and even biological activity. However, despite synthetic miRNA mimics produced by automated systems, various carriers could be used to achieve efficient production of bioengineered miRNAs through economical microbial fermentation. These bioengineered miRNAs as biological prodrugs could provide a new approach for safe therapeutic methods and drug production. In this regard, bioengineered chimeric miRNAs could be selectively processed to mature miRNAs in different types of cancer cells by targeting the desired gene and regulating cancer progression. In this article, we aim to review bioengineered miRNAs and their use in cancer therapy, as well as offering advances in this area, including the use of chimeric tRNA/pre-miRNAs.
Collapse
Affiliation(s)
- Fatemeh Rafieenia
- Department of Medical Genetics, Faculty of Medicine, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | - Seyed Omar Ebrahimi
- Department of Genetics, Faculty of Basic Sciences, Shahrekord University, Shahrekord, Iran
| | - Ensieh Sadat Emadi
- Department of Genetics, Faculty of Basic Sciences, Shahrekord University, Shahrekord, Iran
| | - Forough Taheri
- Department of Genetics, Sharekord Branch, Islamic Azad University, Sharekord
| | - Somayeh Reiisi
- Department of Genetics, Faculty of Basic Sciences, Shahrekord University, Shahrekord, Iran
| |
Collapse
|
10
|
Ranjbari S, Arslanturk S. Integration of incomplete multi-omics data using Knowledge Distillation and Supervised Variational Autoencoders for disease progression prediction. J Biomed Inform 2023; 147:104512. [PMID: 37813325 DOI: 10.1016/j.jbi.2023.104512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 08/31/2023] [Accepted: 10/03/2023] [Indexed: 10/11/2023]
Abstract
OBJECTIVE The rapid advancement of high-throughput technologies in the biomedical field has resulted in the accumulation of diverse omics data types, such as mRNA expression, DNA methylation, and microRNA expression, for studying various diseases. Integrating these multi-omics datasets enables a comprehensive understanding of the molecular basis of cancer and facilitates accurate prediction of disease progression. METHODS However, conventional approaches face challenges due to the dimensionality curse problem. This paper introduces a novel framework called Knowledge Distillation and Supervised Variational AutoEncoders utilizing View Correlation Discovery Network (KD-SVAE-VCDN) to address the integration of high-dimensional multi-omics data with limited common samples. Through our experimental evaluation, we demonstrate that the proposed KD-SVAE-VCDN architecture accurately predicts the progression of breast and kidney carcinoma by effectively classifying patients as long- or short-term survivors. Furthermore, our approach outperforms other state-of-the-art multi-omics integration models. RESULTS Our findings highlight the efficacy of the KD-SVAE-VCDN architecture in predicting the disease progression of breast and kidney carcinoma. By enabling the classification of patients based on survival outcomes, our model contributes to personalized and targeted treatments. The favorable performance of our approach in comparison to several existing models suggests its potential to contribute to the advancement of cancer understanding and management. CONCLUSION The development of a robust predictive model capable of accurately forecasting disease progression at the time of diagnosis holds immense promise for advancing personalized medicine. By leveraging multi-omics data integration, our proposed KD-SVAE-VCDN framework offers an effective solution to this challenge, paving the way for more precise and tailored treatment strategies for patients with different types of cancer.
Collapse
Affiliation(s)
- Sima Ranjbari
- Department of Computer Science, Wayne State University, Detroit, 48202, MI, USA.
| | - Suzan Arslanturk
- Department of Computer Science, Wayne State University, Detroit, 48202, MI, USA.
| |
Collapse
|
11
|
Blutt SE, Coarfa C, Neu J, Pammi M. Multiomic Investigations into Lung Health and Disease. Microorganisms 2023; 11:2116. [PMID: 37630676 PMCID: PMC10459661 DOI: 10.3390/microorganisms11082116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/08/2023] [Accepted: 08/13/2023] [Indexed: 08/27/2023] Open
Abstract
Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.
Collapse
Affiliation(s)
- Sarah E. Blutt
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030, USA;
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Cristian Coarfa
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA;
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Josef Neu
- Department of Pediatrics, Section of Neonatology, University of Florida, Gainesville, FL 32611, USA;
| | - Mohan Pammi
- Department of Pediatrics, Section of Neonatology, Baylor College of Medicine and Texas Children’s Hospital, Houston, TX 77030, USA
| |
Collapse
|