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Satish KS, Saravanan KS, Augustine D, Saraswathy GR, V SS, Khan SS, H VC, Chakraborty S, Dsouza PL, N KH, Halawani IF, Alzahrani FM, Alzahrani KJ, Patil S. Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer. Front Oncol 2024; 13:1183766. [PMID: 38234400 PMCID: PMC10792052 DOI: 10.3389/fonc.2023.1183766] [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: 03/30/2023] [Accepted: 11/30/2023] [Indexed: 01/19/2024] Open
Abstract
Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird's eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies.
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Affiliation(s)
- Kshreeraja S. Satish
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Kamatchi Sundara Saravanan
- Department of Pharmacognosy, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Dominic Augustine
- Department of Oral Pathology & Microbiology, Faculty of Dental Sciences, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Ganesan Rajalekshmi Saraswathy
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Sowmya S. V
- Department of Oral Pathology & Microbiology, Faculty of Dental Sciences, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Samar Saeed Khan
- Department of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral and Maxillofacial Pathology, College of Dentistry, Jazan University, Jazan, Saudi Arabia
| | - Vanishri C. H
- Department of Oral Pathology & Microbiology, Faculty of Dental Sciences, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Shreshtha Chakraborty
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Prizvan Lawrence Dsouza
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Kavya H. N
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Ibrahim F. Halawani
- Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
- Haematology and Immunology Department, Faculty of Medicine, Umm Al-Qura University, AI Abdeyah, Makkah, Saudi Arabia
| | - Fuad M. Alzahrani
- Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Khalid J. Alzahrani
- Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Shankargouda Patil
- College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT, United States
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Yin C, Yan B. Machine learning in basic scientific research on oral diseases. DIGITAL MEDICINE 2023; 9. [DOI: 10.1097/dm-2023-00001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Identification of Potential Key Biomarkers and Immune Infiltration in Oral Lichen Planus. DISEASE MARKERS 2022; 2022:7386895. [PMID: 35256894 PMCID: PMC8898126 DOI: 10.1155/2022/7386895] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 01/11/2022] [Indexed: 12/03/2022]
Abstract
Background Oral lichen planus (OLP) is a chronic autoimmune oral mucosal disease that seriously affects the life quality of the patients. But till now, the exact etiology and pathogenesis of OLP remain unclear. Our study is aimed at finding the key molecules and pathways involved in the pathogenesis mechanisms of OLP, providing more effective therapeutic strategies for OLP. Methods Data from GSE52130 were downloaded from GEO datasets for analysis. Then, we carried out enrichment analysis of the differentially expressed genes (DEGs) using Gene Ontology (GO) and KEGG pathway analyses. Next, the CIBERSORT algorithm was used to assess immune cell infiltration in OLP patients. Furthermore, we also constructed a protein-protein interaction network using STRING and Cytoscape and simultaneously sought potential transcription factors plug-in including MCODE CytoHubba and iRegulon. In addition, ROC analysis was employed to assess the diagnostic performance of these hub genes. Lastly, we identified 6 promising novel drugs to treat OLP through Connectivity Map. Results We illustrated that 255 DEGs were mainly enriched in the focal adhesion pathway and metabolism pathways. Besides, Cibersort analysis showed that M1 macrophages, T follicular helper cells, and T regulatory cells are more infiltrated in OLP samples. In addition, ROC analysis demonstrated that these hub genes owned higher diagnostic value in OLP, in which SPRR1B had the highest diagnostic value. And we also predicted that SOX7 was the most relevant transcription factor of those hub genes. Lastly, through the CMap database, we identified 6 small molecules as possible treatment drugs of OLP. Conclusion Our research identified that SPRR1B could be used as potential biomarkers for the early diagnosis of OLP. In addition, as a chronic autoimmune oral mucosal disease, OLP has different infiltration types of immune cells. Furthermore, 6 small molecules were proposed as promising novel treatment drugs for OLP patients. Therefore, our research may provide new impetus for the development of effective OLP biological treatment options.
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Zhang J, Ding N, He Y, Tao C, Liang Z, Xin W, Zhang Q, Wang F. Bioinformatic identification of genomic instability-associated lncRNAs signatures for improving the clinical outcome of cervical cancer by a prognostic model. Sci Rep 2021; 11:20929. [PMID: 34686717 PMCID: PMC8536663 DOI: 10.1038/s41598-021-00384-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/05/2021] [Indexed: 02/07/2023] Open
Abstract
The research is executed to analyze the connection between genomic instability-associated long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We set a prognostic model up and explored different risk groups' features. The clinical datasets and gene expression profiles of 307 patients have been downloaded from The Cancer Genome Atlas database. We established a prognostic model that combined somatic mutation profiles and lncRNA expression profiles in a tumor genome and identified 35 genomic instability-associated lncRNAs in cervical cancer as a case study. We then stratified patients into low-risk and high-risk groups and were further checked in multiple independent patient cohorts. Patients were separated into two sets: the testing set and the training set. The prognostic model was built using three genomic instability-associated lncRNAs (AC107464.2, MIR100HG, and AP001527.2). Patients in the training set were divided into the high-risk group with shorter overall survival and the low-risk group with longer overall survival (p < 0.001); in the meantime, similar comparable results were found in the testing set (p = 0.046), whole set (p < 0.001). There are also significant differences in patients with histological grades, FIGO stages, and different ages (p < 0.05). The prognostic model focused on genomic instability-associated lncRNAs could predict the prognosis of cervical cancer patients, paving the way for further research into the function and resource of lncRNAs, as well as a key approach to customizing individual care decision-making.
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Affiliation(s)
- Jian Zhang
- Department of Reproductive Medicine, Lanzhou University Second Hospital, Lanzhou, 730030, China
| | - Nan Ding
- Department of Reproductive Medicine, Lanzhou University Second Hospital, Lanzhou, 730030, China
| | - Yongxing He
- School of Life Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Chengbin Tao
- Department of Reproductive Medicine, Lanzhou University Second Hospital, Lanzhou, 730030, China
| | - Zhongzhen Liang
- Department of Reproductive Medicine, Lanzhou University Second Hospital, Lanzhou, 730030, China
| | - Wenhu Xin
- Department of Reproductive Medicine, Lanzhou University Second Hospital, Lanzhou, 730030, China
| | - Qianyun Zhang
- Department of Reproductive Medicine, Lanzhou University Second Hospital, Lanzhou, 730030, China
| | - Fang Wang
- Department of Reproductive Medicine, Lanzhou University Second Hospital, Lanzhou, 730030, China.
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Five candidate biomarkers associated with the diagnosis and prognosis of cervical cancer. Biosci Rep 2021; 41:227898. [PMID: 33616161 PMCID: PMC7955105 DOI: 10.1042/bsr20204394] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/02/2021] [Accepted: 02/17/2021] [Indexed: 02/06/2023] Open
Abstract
Purpose: Cervical cancer (CC) is one of the most general gynecological malignancies and is associated with high morbidity and mortality. We aimed to select candidate genes related to the diagnosis and prognosis of CC. Methods: The mRNA expression profile datasets were downloaded. We also downloaded RNA-sequencing gene expression data and related clinical materials from TCGA, which included 307 CC samples and 3 normal samples. Differentially expressed genes (DEGs) were obtained by R software. GO function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed in the DAVID dataset. Using machine learning, the optimal diagnostic mRNA biomarkers for CC were identified. We used qRT-PCR and Human Protein Atlas (HPA) database to exhibit the differences in gene and protein levels of candidate genes. Results: A total of 313 DEGs were screened from the microarray expression profile datasets. DNA methyltransferase 1 (DNMT1), Chromatin Assembly Factor 1, subunit B (CHAF1B), Chromatin Assembly Factor 1, subunit A (CHAF1A), MCM2, CDKN2A were identified as optimal diagnostic mRNA biomarkers for CC. Additionally, the GEPIA database showed that the DNMT1, CHAF1B, CHAF1A, MCM2 and CDKN2A were associated with the poor survival of CC patients. HPA database and qRT-PCR confirmed that these genes were highly expressed in CC tissues. Conclusion: The present study identified five DEmRNAs, including DNMT1, CHAF1B, CHAF1A, MCM2 and Kinetochore-related protein 1 (KNTC1), as potential diagnostic and prognostic biomarkers of CC.
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Ouyang Z, Li G, Zhu H, Wang J, Qi T, Qu Q, Tu C, Qu J, Lu Q. Construction of a Five-Super-Enhancer-Associated-Genes Prognostic Model for Osteosarcoma Patients. Front Cell Dev Biol 2020; 8:598660. [PMID: 33195283 PMCID: PMC7661850 DOI: 10.3389/fcell.2020.598660] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 10/05/2020] [Indexed: 12/20/2022] Open
Abstract
Osteosarcoma is a malignant tumor most commonly arising in children and adolescents and associated with poor prognosis. In recent years, some prognostic models have been constructed to assist clinicians in the treatment of osteosarcoma. However, the prognosis and treatment of patients with osteosarcoma remain unsatisfactory. Notably, super-enhancer (SE)-associated genes strongly promote the progression of osteosarcoma. In the present study, we constructed a novel effective prognostic model using SE-associated genes from osteosarcoma. Five SE-associated genes were initially screened through the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, as well as univariate and multivariate Cox regression analyses. Meanwhile, a risk score model was constructed using the expression of these five genes. The excellent performance of the five-SE-associated-gene-based prognostic model was determined via time-dependent receiver operating characteristic (ROC) curves and Kaplan-Meier curves. Inferior outcome of overall survival (OS) was predicted in the high-risk group. A nomogram based on the polygenic risk score model was further established to validate the performance of the prognostic model. It showed that our prognostic model performed outstandingly in predicting 1-, 3-, and 5-year OS of patients with osteosarcoma. Meanwhile, these five genes also belonged to the hub genes associated with survival and necrosis of osteosarcoma according to the result of weighted gene co-expression network analysis based on the dataset of GSE39058. Therefore, we believe that the five-SE-associated-gene-based prognostic model established in this study can accurately predict the prognosis of patients with osteosarcoma and effectively assist clinicians in treating osteosarcoma in the future.
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Affiliation(s)
- Zhanbo Ouyang
- Department of Pharmacy, The Second Xiangya Hospital, Central South University; Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Guohua Li
- Department of Pharmacy, The Second Xiangya Hospital, Central South University; Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Haihong Zhu
- Department of Pharmacy, The Second Xiangya Hospital, Central South University; Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Jiaojiao Wang
- Department of Pharmacy, The Second Xiangya Hospital, Central South University; Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Tingting Qi
- Department of Pharmacy, The Second Xiangya Hospital, Central South University; Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Qiang Qu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China
| | - Chao Tu
- Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Jian Qu
- Department of Pharmacy, The Second Xiangya Hospital, Central South University; Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Qiong Lu
- Department of Pharmacy, The Second Xiangya Hospital, Central South University; Institute of Clinical Pharmacy, Central South University, Changsha, China
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