1
|
Zhang C, Jia Y, Kong Q. Case report: Squamous cell carcinoma of the prostate-a clinicopathological and genomic sequencing-based investigation. Pathol Oncol Res 2023; 29:1611343. [PMID: 38089646 PMCID: PMC10713708 DOI: 10.3389/pore.2023.1611343] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 11/14/2023] [Indexed: 12/18/2023]
Abstract
Squamous differentiation of prostate cancer, which accounts for less than 1% of all cases, is typically associated with androgen deprivation treatment (ADT) or radiotherapy. This entity is aggressive and exhibits poor prognosis due to limited response to traditional treatment. However, the underlying molecular mechanisms and etiology are not fully understood. Previous findings suggest that squamous cell differentiation may potentially arise from prostate adenocarcinoma (AC), but further validation is required to confirm this hypothesis. This paper presents a case of advanced prostate cancer with a combined histologic pattern, including keratinizing SCC and AC. The study utilized whole-exome sequencing (WES) data to analyze both subtypes and identified a significant overlap in driver gene mutations between them. This suggests that the two components shared a common origin of clones. These findings emphasize the importance of personalized clinical management for prostate SCC, and specific molecular findings can help optimize treatment strategies.
Collapse
Affiliation(s)
- Caixin Zhang
- Department of Pathology, Qingdao Municipal Hospital, Qingdao, China
| | - Yong Jia
- Department of Urology, Qingdao Municipal Hospital, Qingdao, China
| | - Qingnuan Kong
- Department of Pathology, Qingdao Municipal Hospital, Qingdao, China
| |
Collapse
|
2
|
Siddalingappa R, Kanagaraj S. K-nearest-neighbor algorithm to predict the survival time and classification of various stages of oral cancer: a machine learning approach. F1000Res 2023; 11:70. [PMID: 38046542 PMCID: PMC10690040 DOI: 10.12688/f1000research.75469.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/16/2023] [Indexed: 12/05/2023] Open
Abstract
Background:For years now, cancer treatments have entailed tried-and-true methods. Yet, oncologists and clinicians recommend a series of surgeries, chemotherapy, and radiation therapy. Yet, even amidst these treatments, the number of deaths due to cancer increases at an alarming rate. The prognosis of cancer patients is influenced by mutations, age, and various cancer stages. However, the association between these variables is unclear. Methods: The present work adopts a machine learning technique-k-nearest neighbor; for both regression and classification tasks, regression for predicting the survival time of oral cancer patients, and classification for classifying the patients into one of the predefined oral cancer stages. Two cross-validation approaches-hold-out and k-fold methods-have been used to examine the prediction results. Results: The experimental results show that the k-fold method performs better than the hold-out method, providing the least mean absolute error score of 0.015. Additionally, the model classifies patients into a valid group. Of the 429 records, 97 (out of 106), 99 (out of 119), 95 (out of 113), and 77 (out of 91) were classified to its correct label as stages - 1, 2, 3, and 4. The accuracy, recall, precision, and F-measure for each classification group obtained are 0.84, 0.85, 0.85, and 0.84. Conclusions: The study showed that aged patients with a higher number of mutations than young patients have a higher risk of short survival. Senior patients with a more significant number of mutations have an increased risk of getting into the last cancer stage.
Collapse
Affiliation(s)
- Rashmi Siddalingappa
- Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka, 560012, India
| | - Sekar Kanagaraj
- Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka, 560012, India
| |
Collapse
|
3
|
Budhraja S, Doborjeh M, Singh B, Tan S, Doborjeh Z, Lai E, Merkin A, Lee J, Goh W, Kasabov N. Filter and Wrapper Stacking Ensemble (FWSE): a robust approach for reliable biomarker discovery in high-dimensional omics data. Brief Bioinform 2023; 24:bbad382. [PMID: 37889118 PMCID: PMC10605029 DOI: 10.1093/bib/bbad382] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 09/18/2023] [Accepted: 10/03/2023] [Indexed: 10/28/2023] Open
Abstract
Selecting informative features, such as accurate biomarkers for disease diagnosis, prognosis and response to treatment, is an essential task in the field of bioinformatics. Medical data often contain thousands of features and identifying potential biomarkers is challenging due to small number of samples in the data, method dependence and non-reproducibility. This paper proposes a novel ensemble feature selection method, named Filter and Wrapper Stacking Ensemble (FWSE), to identify reproducible biomarkers from high-dimensional omics data. In FWSE, filter feature selection methods are run on numerous subsets of the data to eliminate irrelevant features, and then wrapper feature selection methods are applied to rank the top features. The method was validated on four high-dimensional medical datasets related to mental illnesses and cancer. The results indicate that the features selected by FWSE are stable and statistically more significant than the ones obtained by existing methods while also demonstrating biological relevance. Furthermore, FWSE is a generic method, applicable to various high-dimensional datasets in the fields of machine intelligence and bioinformatics.
Collapse
Affiliation(s)
- Sugam Budhraja
- Knowledge Engineering and Discovery Research Innovation (KEDRI), School of Engineering Computer and Mathematical Sciences, Auckland University of Technology, 55 Wellesley Street East, 1010 Auckland, New Zealand
| | - Maryam Doborjeh
- Knowledge Engineering and Discovery Research Innovation (KEDRI), School of Engineering Computer and Mathematical Sciences, Auckland University of Technology, 55 Wellesley Street East, 1010 Auckland, New Zealand
| | - Balkaran Singh
- Knowledge Engineering and Discovery Research Innovation (KEDRI), School of Engineering Computer and Mathematical Sciences, Auckland University of Technology, 55 Wellesley Street East, 1010 Auckland, New Zealand
| | - Samuel Tan
- Lee Kong Chian School of Medicine, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore
| | - Zohreh Doborjeh
- School of Population Health, The University of Auckland, Grafton, 1023,Auckland, New Zealand
| | - Edmund Lai
- Knowledge Engineering and Discovery Research Innovation (KEDRI), School of Engineering Computer and Mathematical Sciences, Auckland University of Technology, 55 Wellesley Street East, 1010 Auckland, New Zealand
| | - Alexander Merkin
- National Institute for Stroke and Applied Neuroscience, Auckland University of Technology, 55 Wellesley Street East, 1010 Auckland, New Zealand
| | - Jimmy Lee
- Lee Kong Chian School of Medicine, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore
- Institute of Mental Health, 10 Buangkok View, 539747, Singapore
| | - Wilson Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore
- School of Biological Sciences, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore
| | - Nikola Kasabov
- Knowledge Engineering and Discovery Research Innovation (KEDRI), School of Engineering Computer and Mathematical Sciences, Auckland University of Technology, 55 Wellesley Street East, 1010 Auckland, New Zealand
- Intelligent Systems Research Center, Ulster University, Magee Campus, Derry, BT48 7JL, Ulster, United Kingdom
- Auckland Bioengineering Institute, The University of Auckland, 6/70 Symonds Street, 1010 Auckland, New Zealand
- Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria
| |
Collapse
|
4
|
Ye S, Pan J, Ye Z, Cao Z, Cai X, Zheng H, Ye H. Construction and Validation of Early Warning Model of Lung Cancer Based on Machine Learning: A Retrospective Study. Technol Cancer Res Treat 2022; 21:15330338221136724. [PMID: 36380607 PMCID: PMC9676307 DOI: 10.1177/15330338221136724] [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] [Indexed: 11/17/2022] Open
Abstract
Background: This study is a retrospective study. The purpose of this study is to construct and validate an early warning model of lung cancer through machine learning. Methods: The CDKN2A gene expression profile and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database and divided into a tumor group and a normal group (n = 57). The top 5 somatic mutation-related genes were extracted from 567 somatic mutation data downloaded from TCGA database using random forest algorithm. Cox proportional hazard model and nomogram were constructed combining CDKN2A, 5 somatic mutation-related genes, gender, and smoking index. Patients were divided into high-risk and low-risk groups according to risk score. The predictability of the model in the prognosis of lung cancer was estimated by Kaplan-Meier survival analysis and receiver operating characteristics curve. Results: We constructed a prognostic model consisting of 5 somatic mutation-related genes (sphingosine 1-phosphate receptor 1 [S1PR1], dedicator of cytokinesis 7 [DOCK7], DEAD-box helicase 4 [DDX4], laminin subunit beta 3 [LAMB3], and importin 5 [IPO5]), cyclin-dependent kinase inhibitor 2A (CDKN2A), gender, and smoking indicators. The high-risk group had a lower overall survival rate compared to the low-risk group (hazard ratio = 2.14, P = 0 .0323). The area under the curve predicted for 3-year, 5-year, and 10-year survival rates are 0.609, 0.673, and 0.698, respectively. The accuracy, sensitivity, and specificity of the model for predicting the 10-year survival rate of lung cancer are 76.19%, 56.71%, and 86.23%. Conclusion: The lung cancer early warning model and nomogram may provide an essential reference for patients with lung cancer management in the clinic.
Collapse
Affiliation(s)
- Siyu Ye
- School of Public Administration, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jiongwei Pan
- Respiratory Department, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Zaiting Ye
- Radiology Department, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Zhuo Cao
- Respiratory Department, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Xiaoping Cai
- Respiratory Department, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Hao Zheng
- Respiratory Department, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui, China
| | - Hong Ye
- PE Center, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui, China,Hong Ye, PE Center, The Sixth Affiliated Hospital of Wenzhou Medical University, 15# Dazhong street, Liandu District, Lishui City, Zhejiang Province, 323000, China.
| |
Collapse
|
5
|
Li Y, Wu X, Yang P, Jiang G, Luo Y. Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:850-866. [PMID: 36462630 PMCID: PMC10025752 DOI: 10.1016/j.gpb.2022.11.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 10/03/2022] [Accepted: 11/17/2022] [Indexed: 12/03/2022]
Abstract
The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this review, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice. Moreover, we highlight the challenges and opportunities for future applications of machine learning in lung cancer.
Collapse
Affiliation(s)
- Yawei Li
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Xin Wu
- Department of Medicine, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Ping Yang
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905 / Scottsdale, AZ 85259, USA
| | - Guoqian Jiang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN 55905, USA
| | - Yuan Luo
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
| |
Collapse
|
6
|
Gupta S, Gupta MK, Shabaz M, Sharma A. Deep learning techniques for cancer classification using microarray gene expression data. Front Physiol 2022; 13:952709. [PMID: 36246115 PMCID: PMC9563992 DOI: 10.3389/fphys.2022.952709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 09/01/2022] [Indexed: 11/28/2022] Open
Abstract
Cancer is one of the top causes of death globally. Recently, microarray gene expression data has been used to aid in cancer’s effective and early detection. The use of DNA microarray technology to uncover information from the expression levels of thousands of genes has enormous promise. The DNA microarray technique can determine the levels of thousands of genes simultaneously in a single experiment. The analysis of gene expression is critical in many disciplines of biological study to obtain the necessary information. This study analyses all the research studies focused on optimizing gene selection for cancer detection using artificial intelligence. One of the most challenging issues is figuring out how to extract meaningful information from massive databases. Deep Learning architectures have performed efficiently in numerous sectors and are used to diagnose many other chronic diseases and to assist physicians in making medical decisions. In this study, we have evaluated the results of different optimizers on a RNA sequence dataset. The Deep learning algorithm proposed in the study classifies five different forms of cancer, including kidney renal clear cell carcinoma (KIRC), Breast Invasive Carcinoma (BRCA), lung adenocarcinoma (LUAD), Prostate Adenocarcinoma (PRAD) and Colon Adenocarcinoma (COAD). The performance of different optimizers like Stochastic gradient descent (SGD), Root Mean Squared Propagation (RMSProp), Adaptive Gradient Optimizer (AdaGrad), and Adaptive Momentum (AdaM). The experimental results gathered on the dataset affirm that AdaGrad and Adam. Also, the performance analysis has been done using different learning rates and decay rates. This study discusses current advancements in deep learning-based gene expression data analysis using optimized feature selection methods.
Collapse
Affiliation(s)
- Surbhi Gupta
- Department of Computer Science and Engineering Department, SMVDU, Jammu, India
- Model Institute of Engineering and Technology, Jammu, India
| | - Manoj K. Gupta
- Department of Computer Science and Engineering Department, SMVDU, Jammu, India
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology, Jammu, India
- *Correspondence: Mohammad Shabaz,
| | - Ashutosh Sharma
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
| |
Collapse
|
7
|
Liu D, Zhu J, Ma X, Zhang L, Wu Y, Zhu W, Xing Y, Jia Y, Wang Y. Transcriptomic and Metabolomic Profiling in Helicobacter pylori-Induced Gastric Cancer Identified Prognosis- and Immunotherapy-Relevant Gene Signatures. Front Cell Dev Biol 2022; 9:769409. [PMID: 35004676 PMCID: PMC8740065 DOI: 10.3389/fcell.2021.769409] [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: 09/02/2021] [Accepted: 12/01/2021] [Indexed: 12/14/2022] Open
Abstract
Background: Chronic Helicobacter pylori (HP) infection is considered the major cause of non-cardia gastric cancer (GC). However, how HP infection influences the metabolism and further regulates the progression of GC remains unknown. Methods: We comprehensively evaluated the metabolic pattern of HP-positive (HP+) GC samples using transcriptomic data and correlated these patterns with tumor microenvironment (TME)-infiltrating characteristics. The metabolic score was constructed to quantify metabolic patterns of individual tumors using principal component analysis (PCA) algorithms. The expression alterations of key metabolism-related genes (MRGs) and downstream metabolites were validated by PCR and untargeted metabolomics analysis. Results: Two distinct metabolic patterns and differential metabolic scores were identified in HP+ GC, which had various biological pathways in common and were associated with clinical outcomes. TME-infiltrating profiles under both patterns were highly consistent with the immunophenotype. Furthermore, the analysis indicated that a low metabolic score was correlated with an increased EMT subtype, immunosuppression status, and worse survival. Importantly, we identified that the expression of five MRGs, GSS, GMPPA, OGDH, SGPP2, and PIK3CA, was remarkably correlated with HP infection, patient survival, and therapy response. Furthermore, the carbohydrate metabolism and citric acid may be downstream regulators of the function of metabolic genes in HP-induced GC. Conclusion: Our findings suggest that there is cross talk between metabolism and immune promotion during HP infection. MRG-specific transcriptional alterations may serve as predictive biomarkers of survival outcomes and potential targets for treatment of patients with HP-induced GC.
Collapse
Affiliation(s)
- Duanrui Liu
- Research Center of Basic Medicine, Jinan Central Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Research Center of Basic Medicine, Jinan Central Hospital, Shandong First Medical University, Jinan, China
| | - Jingyu Zhu
- Department of Gastroenterology, Jinan Central Hospital, Shandong First Medical University, Jinan, China
| | - Xiaoli Ma
- Research Center of Basic Medicine, Jinan Central Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Research Center of Basic Medicine, Jinan Central Hospital, Shandong First Medical University, Jinan, China
| | - Lulu Zhang
- Research Center of Basic Medicine, Jinan Central Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Research Center of Basic Medicine, Jinan Central Hospital, Shandong First Medical University, Jinan, China
| | - Yufei Wu
- Research Center of Basic Medicine, Jinan Central Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wenshuai Zhu
- Research Center of Basic Medicine, Jinan Central Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yuanxin Xing
- Research Center of Basic Medicine, Jinan Central Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Research Center of Basic Medicine, Jinan Central Hospital, Shandong First Medical University, Jinan, China
| | - Yanfei Jia
- Research Center of Basic Medicine, Jinan Central Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Research Center of Basic Medicine, Jinan Central Hospital, Shandong First Medical University, Jinan, China
| | - Yunshan Wang
- Research Center of Basic Medicine, Jinan Central Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Research Center of Basic Medicine, Jinan Central Hospital, Shandong First Medical University, Jinan, China
| |
Collapse
|
8
|
Shi JY, Bi YY, Yu BF, Wang QF, Teng D, Wu DN. Alternative Splicing Events in Tumor Immune Infiltration in Colorectal Cancer. Front Oncol 2021; 11:583547. [PMID: 33996533 PMCID: PMC8117221 DOI: 10.3389/fonc.2021.583547] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 03/31/2021] [Indexed: 01/05/2023] Open
Abstract
Despite extensive research, the exact mechanisms involved in colorectal cancer (CRC) etiology and pathogenesis remain unclear. This study aimed to examine the correlation between tumor-associated alternative splicing (AS) events and tumor immune infiltration (TII) in CRC. We analyzed transcriptome profiling and clinical CRC data from The Cancer Genome Atlas (TCGA) database and lists of AS-related and immune-related signatures from the SpliceSeq and Innate databases, respectively to develop and validate a risk model of differential AS events and subsequently a TII risk model. We then conducted a two-factor survival analysis to study the association between TII and AS risk and evaluated the associations between immune signatures and six types of immune cells based on the TIMER database. Subsequently, we studied the distribution of six types of TII cells in high- and low-risk groups for seven AS events and in total. We obtained the profiles of AS events/genes for 484 patients, which included 473 CRC tumor samples and 41 corresponding normal samples, and detected 22581 AS events in 8122 genes. Exon Skip (ES) (8446) and Mutually Exclusive Exons (ME) (74) exhibited the most and fewest AS events, respectively. We then classified the 433 patients with CRC into low-risk (n = 217) and high-risk (n = 216) groups based on the median risk score in different AS events. Compared with patients with low-risk scores (mortality = 11.8%), patients with high-risk scores were associated with poor overall survival (mortality = 27.6%). The risk score, cancer stage, and pathological stage (T, M, and N) were closely correlated with prognosis in patients with CRC (P < 0.001). We identified 6479 differentially expressed genes from the transcriptome profiles of CRC and intersected 468 differential immune-related signatures. High-AS-risk and high-TII-risk predicted a poor prognosis in CRC. Different AS types were associated with different TII risk characteristics. Alternate Acceptor site (AA) and Alternate Promoter (AP) events directly affected the concentration of CD4T cells, and the level of CD8T cells was closely correlated with Alternate Terminator (AT) and Exon Skip (ES) events. Thus, the concentration of CD4T and CD8T cells in the CRC immune microenvironment was not specifically modulated by AS. However, B cell, dendritic cell, macrophage, and neutrophilic cell levels were strongly correlated with AS events. These results indicate adverse associations between AS event risk levels and immune cell infiltration density. Taken together, our findings show a clear association between tumor-associated alternative splicing and immune cell infiltration events and patient outcome and could form a basis for the identification of novel markers and therapeutic targets for CRC and other cancers in the future.
Collapse
Affiliation(s)
- Jian-Yu Shi
- Department of Proctology, Ping Yi People's Hospital, Linyi, China
| | - Yan-Yan Bi
- Department of Proctology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Ji Nan, China
| | - Bian-Fang Yu
- Department of Proctology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Ji Nan, China
| | - Qing-Feng Wang
- Department of Basic Pharmacology, College of Integration of Traditional and Western Medicine, Liaoning University of Traditional Chinese Medicine, Shenyang, China
| | - Dan Teng
- Artificial Intelligence and Big Data College, HE University, Shenyang, China
| | - Dong-Ning Wu
- Clinical Evaluation Center, Chinese Academy of Chinese Medical Sciences, Beijing, China
| |
Collapse
|
9
|
TGF-β promote epithelial-mesenchymal transition via NF-κB/NOX4/ROS signal pathway in lung cancer cells. Mol Biol Rep 2021; 48:2365-2375. [PMID: 33792826 DOI: 10.1007/s11033-021-06268-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Accepted: 03/05/2021] [Indexed: 01/17/2023]
Abstract
Epithelial-mesenchymal transition (EMT), transforming growth factor β(TGF-β) and reactive oxygen species(ROS) plays a central role in cancer metastasis. Moreover, nicotinamide adenine dinucleotide phosphate 4(NOX4) is one of the main sources of ROS in lung cancer cells suggesting that NOX4 is associated with tumor cell migration. NF-κB(Nuclear factor-Kappa-B) is known to regulate ROS-mediated EMT process by activating Snail transcription factor in A549 cells. The purpose of this study was to explore the relationship between NF-κB and NOX4 in ROS production during TGF-β induced EMT process. Several fractions have been pooled to evaluates the EMT process on lung cancer cells through real-time PCR, Western Blot and flow cytometry with DCFH-DA probe etc. Cells proliferation and migration activities were monitored by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide) assay and wound healing assay respectively. The result showed that TGF-β induction decreased the expression of E-cadherin, increased the Vimentin and the EMT transcription factor Snail in A549 cells. DPI (Diphenyleneiodonium chloride, an inhibitor of NOX4) inhibited the NOX4 expression and reduced ROS production induced by TGF-β, but didn't affect the activation of NF-κB induced by TGF-β (P > 0.05). BAY11-7082 (an inhibitor of NF-κB) inhibited the NF-κB (p65) expression and prevented the increase of NOX4 expression and ROS production induced by TGF-β (P < 0.001), which has also verified reduced TGF-β induced cell migration by inhibiting the EMT process, and also reduced cell proliferation of A549 cells (P < 0.001). The current research confirmed the TGF-β mediated EMT process via NF-κB/NOX4/ROS signaling pathway, NF-κB and NOX4 are likely to be the potential therapeutic targets for lung cancer metastasis.
Collapse
|
10
|
Identifying Novel Cell Glycolysis-Related Gene Signature Predictive of Overall Survival in Gastric Cancer. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9656947. [PMID: 33791386 PMCID: PMC7982000 DOI: 10.1155/2021/9656947] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 02/06/2021] [Accepted: 02/28/2021] [Indexed: 01/16/2023]
Abstract
Background Gastric cancer (GC) is believed to be one of the most common digestive tract malignant tumors. The prognosis of GC remains poor due to its high malignancy, high incidence of metastasis and relapse, and lack of effective treatment. The constant progress in bioinformatics and molecular biology techniques has given rise to the discovery of biomarkers with clinical value to predict the GC patients' prognosis. However, the use of a single gene biomarker can hardly achieve the satisfactory specificity and sensitivity. Therefore, it is urgent to identify novel genetic markers to forecast the prognosis of patients with GC. Materials and Methods In our research, data mining was applied to perform expression profile analysis of mRNAs in the 443 GC patients from The Cancer Genome Atlas (TCGA) cohort. Genes associated with the overall survival (OS) of GC were identified using univariate analysis. The prognostic predictive value of the risk factors was determined using the Kaplan-Meier survival analysis and multivariate analysis. The risk scoring system was built in TCGA dataset and validated in an independent Gene Expression Omnibus (GEO) dataset comprising 300 GC patients. Based on the median of the risk score, GC patients were grouped into high-risk and low-risk groups. Results We identified four genes (GMPPA, GPC3, NUP50, and VCAN) that were significantly correlated with GC patients' OS. The high-risk group showed poor prognosis, indicating that the risk score was an effective predictor for the prognosis of GC patients. Conclusion The signature consisting of four glycolysis-related genes could be used to forecast the GC patients' prognosis.
Collapse
|
11
|
Deng F, Shen L, Wang H, Zhang L. Classify multicategory outcome in patients with lung adenocarcinoma using clinical, transcriptomic and clinico-transcriptomic data: machine learning versus multinomial models. Am J Cancer Res 2020; 10:4624-4639. [PMID: 33415023 PMCID: PMC7783755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 11/25/2020] [Indexed: 06/12/2023] Open
Abstract
Classification of multicategory survival-outcome is important for precision oncology. Machine learning (ML) algorithms have been used to accurately classify multi-category survival-outcome of some cancer-types, but not yet that of lung adenocarcinoma. Therefore, we compared the performances of 3 ML models (random forests, support vector machine [SVM], multilayer perceptron) and multinomial logistic regression (Mlogit) models for classifying 4-category survival-outcome of lung adenocarcinoma using the TCGA. Mlogit model overall performed similar to SVM and multilayer perceptron models (micro-average area under curve=0.82), while random forests model was inferior. Surprisingly, transcriptomic data alone and clinico-transcriptomic data appeared sufficient to accurately classify the 4-category survival-outcome in these patients, but no models using clinical data alone performed well. Notably, NDUFS5, P2RY2, PRPF18, CCL24, ZNF813, MYL6, FLJ41941, POU5F1B, and SUV420H1 were the top-ranked genes that were associated with alive without disease and inversely linked to other outcomes. Similarly, BDKRB2, TERC, DNAJA3, MRPL15, SLC16A13, CRHBP and ACSBG2 were associated with alive with progression and GAL3ST3, AD2, RAB41, HDC, and PLEKHG1 associated with dead with disease, respectively, while also inversely linked other outcomes. These cross-linked genes may be used for risk-stratification and future treatment development.
Collapse
Affiliation(s)
- Fei Deng
- School of Electrical and Electronic Engineering, Shanghai Institute of TechnologyShanghai, China
| | - Lanlan Shen
- Department of Pediatrics, Baylor College of Medicine, USDA/ARS Children’s Nutrition Research CenterHouston, TX, USA
| | - He Wang
- Department of Pathology, Yale University School of MedicineNew Haven, CT, USA
| | - Lanjing Zhang
- Department of Pathology, Princeton Medical CenterPlainsboro, NJ, USA
- Department of Biological Sciences, Rutgers UniversityNewark, NJ
- Rutgers Cancer Institute of New JerseyNew Brunswick, NJ, USA
- Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers UniversityPiscataway, NJ, USA
| |
Collapse
|
12
|
Luo T, Du Y, Duan J, Liang C, Chen G, Jiang K, Chen Y, Chen Y. Development and Validation of a Scoring System Based on 9 Glycolysis-Related Genes for Prognosis Prediction in Gastric Cancer. Technol Cancer Res Treat 2020; 19:1533033820971670. [PMID: 33161837 PMCID: PMC7658532 DOI: 10.1177/1533033820971670] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Gastric cancer is a malignant tumor with high morbidity and mortality worldwide. However, increasing evidences have revealed the correlation between the glycolysis process and tumorigenesis. This study is aim to develop a list of glycolysis-related genes for risk stratification in gastric cancer patients. We included 500 patients’ sample data from GSE62254 and GSE26942 datasets, and classified patients into training (n = 350) and testing sets (n = 150) at a ratio of 7: 3. Univariate and multivariate Cox regression analysis were performed to screen genes having prognostic value. Based on HALLMARK gene sets, we identified 9 glycolysis-related genes (BPNT1, DCN, FUT8, GMPPA, GPC3, LDHC, ME2, PLOD2, and UGP2). On the basis of risk score developed by the 9 genes, patients were classified into high- and low-risk groups. The survival analysis showed that the high-risk patients had a worse prognosis (p < 0.001). Similar finding was observed in the testing cohort and 2 independent cohorts (GSE13861 and TCGA-STAD, all p < 0.001). The multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for overall survival (p < 0.001). Furthermore, we constructed a nomogram that integrated the risk score and tumor stage, age, and adjuvant chemotherapy. Through comparing the results of the receiver operating characteristic curves and decision curve analysis, we found that the nomogram had a superior predictive accuracy than conventional TNM staging system, suggesting that the risk score combined with other clinical factors (age, tumor stage, and adjuvant chemotherapy) can develop a robust prediction for survival and improve the individualized clinical decision making of the patient. In conclusion, we identified 9 glycolysis-related genes from hallmark glycolysis pathway. Based on the 9 genes, gastric cancer patients were separated into different risk groups related to survival.
Collapse
Affiliation(s)
- Tianqi Luo
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yufei Du
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- Department of Biotherapy, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jinling Duan
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- Department of Experimental Research (Cancer Institute), Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Chengcai Liang
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Guoming Chen
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Kaiming Jiang
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yongming Chen
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Yingbo Chen, and Yongming Chen, Dongfeng East Road 651, Guangzhou, Guangdong 510060, China. Emails: ;
| | - Yingbo Chen
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Yingbo Chen, and Yongming Chen, Dongfeng East Road 651, Guangzhou, Guangdong 510060, China. Emails: ;
| |
Collapse
|
13
|
Zheng X, Amos CI, Frost HR. Cancer prognosis prediction using somatic point mutation and copy number variation data: a comparison of gene-level and pathway-based models. BMC Bioinformatics 2020; 21:467. [PMID: 33081688 PMCID: PMC7574407 DOI: 10.1186/s12859-020-03791-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 09/30/2020] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Genomic profiling of solid human tumors by projects such as The Cancer Genome Atlas (TCGA) has provided important information regarding the somatic alterations that drive cancer progression and patient survival. Although researchers have successfully leveraged TCGA data to build prognostic models, most efforts have focused on specific cancer types and a targeted set of gene-level predictors. Less is known about the prognostic ability of pathway-level variables in a pan-cancer setting. To address these limitations, we systematically evaluated and compared the prognostic ability of somatic point mutation (SPM) and copy number variation (CNV) data, gene-level and pathway-level models for a diverse set of TCGA cancer types and predictive modeling approaches. RESULTS We evaluated gene-level and pathway-level penalized Cox proportional hazards models using SPM and CNV data for 29 different TCGA cohorts. We measured predictive accuracy as the concordance index for predicting survival outcomes. Our comprehensive analysis suggests that the use of pathway-level predictors did not offer superior predictive power relative to gene-level models for all cancer types but had the advantages of robustness and parsimony. We identified a set of cohorts for which somatic alterations could not predict prognosis, and a unique cohort LGG, for which SPM data was more predictive than CNV data and the predictive accuracy is good for all model types. We found that the pathway-level predictors provide superior interpretative value and that there is often a serious collinearity issue for the gene-level models while pathway-level models avoided this issue. CONCLUSION Our comprehensive analysis suggests that when using somatic alterations data for cancer prognosis prediction, pathway-level models are more interpretable, stable and parsimonious compared to gene-level models. Pathway-level models also avoid the issue of collinearity, which can be serious for gene-level somatic alterations. The prognostic power of somatic alterations is highly variable across different cancer types and we have identified a set of cohorts for which somatic alterations could not predict prognosis. In general, CNV data predicts prognosis better than SPM data with the exception of the LGG cohort.
Collapse
Affiliation(s)
- Xingyu Zheng
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA
| | - Christopher I Amos
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA.
- Department of Medicine, Institute for Clinical and Translational Research, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA.
| | - H Robert Frost
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA.
| |
Collapse
|
14
|
Liu X, Hui X, Kang H, Fang Q, Chen A, Hu Y, Lu D, Chen X, Wang Y. A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features. Front Genet 2020; 11:940. [PMID: 33005171 PMCID: PMC7479248 DOI: 10.3389/fgene.2020.00940] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 07/28/2020] [Indexed: 12/24/2022] Open
Abstract
Background Stomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations. Methods In this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association. Results Typical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly. Conclusion The study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts.
Collapse
Affiliation(s)
- Xianming Liu
- Department of Gastrointestinal Surgery, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, Shenzhen, China
| | - Xinjie Hui
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
| | - Huayu Kang
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
| | - Qiongfang Fang
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
| | - Aiyue Chen
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
| | - Yueming Hu
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
| | - Desheng Lu
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
| | - Xianxiong Chen
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
| | - Yejun Wang
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
| |
Collapse
|
15
|
Li G, Wang G, Guo Y, Li S, Zhang Y, Li J, Peng B. Development of a novel prognostic score combining clinicopathologic variables, gene expression, and mutation profiles for lung adenocarcinoma. World J Surg Oncol 2020; 18:249. [PMID: 32950055 PMCID: PMC7502202 DOI: 10.1186/s12957-020-02025-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 09/10/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Integrating phenotypic and genotypic information to improve prognostic prediction is under active investigation for lung adenocarcinoma (LUAD). In this study, we developed a new prognostic model for event-free survival (EFS) and recurrence-free survival (RFS) based on the combination of clinicopathologic variables, gene expression, and mutation data. METHODS We enrolled a total of 408 patients from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) project for the study. We pre-selected gene expression or mutation features and constructed 14 different input feature sets for predictive model development. We assessed model performance with multiple evaluation metrics including the distribution of C-index on testing dataset, risk score significance, and time-dependent AUC under competing risks scenario. We stratified patients into higher- and lower-risk subgroups by the final risk score and further investigated underlying immune phenotyping variations associated with the differential risk. RESULTS The model integrating all three types of data achieved the best prediction performance. The resultant risk score provided a higher-resolution risk stratification than other models within pathologically defined subgroups. The score could account for extra EFS-related variations that were not captured by clinicopathologic scores. Being validated for RFS prediction under a competing risks modeling framework, the score achieved a significantly higher time-dependent AUC as compared to that of the conventional clinicopathologic variables-based model (0.772 vs. 0.646, p value < 0.001). The higher-risk patients were characterized with transcriptional aberrations of multiple immune-related genes, and a significant depletion of mast cells and natural killer cells. CONCLUSIONS We developed a novel prognostic risk score with improved prediction accuracy, using clinicopathologic variables, gene expression and mutation profiles as input, for LUAD. Such score was a significant predictor of both EFS and RFS. TRIAL REGISTRATION This study was based on public open data from TCGA and hence the study objects were retrospectively registered.
Collapse
Affiliation(s)
- Guofeng Li
- Department of Thoracic Surgery, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, Luohu District, Shenzhen, 518020, China
| | - Guangsuo Wang
- Department of Thoracic Surgery, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, Luohu District, Shenzhen, 518020, China
| | - Yanhua Guo
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Yangpu District, Shanghai, 200433, China
| | - Shixuan Li
- Department of Thoracic Surgery, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, Luohu District, Shenzhen, 518020, China
| | - Youlong Zhang
- Department of Biostatistics, HuaJia Biomedical Intelligence, Shenzhen Overseas Chinese High-Tech Venture Park, Nanshan District, Shenzhen, 518057, China
| | - Jialu Li
- Department of Biostatistics, HuaJia Biomedical Intelligence, Shenzhen Overseas Chinese High-Tech Venture Park, Nanshan District, Shenzhen, 518057, China.
| | - Bin Peng
- Department of Thoracic Surgery, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, Luohu District, Shenzhen, 518020, China.
| |
Collapse
|
16
|
Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments. BMC Med Genomics 2020; 13:111. [PMID: 32948183 PMCID: PMC7499993 DOI: 10.1186/s12920-020-00759-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 07/27/2020] [Indexed: 12/18/2022] Open
Abstract
Background Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn’t allow sufficient training of ML classifiers that could be used for improving molecular diagnostics. Methods We reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories. Results We identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases. Conclusions We collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others – microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide.
Collapse
|
17
|
Buzdin A, Sorokin M, Garazha A, Glusker A, Aleshin A, Poddubskaya E, Sekacheva M, Kim E, Gaifullin N, Giese A, Seryakov A, Rumiantsev P, Moshkovskii S, Moiseev A. RNA sequencing for research and diagnostics in clinical oncology. Semin Cancer Biol 2019; 60:311-323. [PMID: 31412295 DOI: 10.1016/j.semcancer.2019.07.010] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 07/16/2019] [Indexed: 12/26/2022]
Abstract
Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis.
Collapse
Affiliation(s)
- Anton Buzdin
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia; Omicsway Corp., Walnut, CA, USA; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
| | - Maxim Sorokin
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia; Omicsway Corp., Walnut, CA, USA; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | | | | | - Alex Aleshin
- Stanford University School of Medicine, Stanford, 94305, CA, USA
| | - Elena Poddubskaya
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia; Vitamed Oncological Clinics, Moscow, Russia
| | - Marina Sekacheva
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Ella Kim
- Johannes Gutenberg University Mainz, Mainz, Germany
| | - Nurshat Gaifullin
- Lomonosov Moscow State University, Faculty of Medicine, Moscow, Russia
| | | | | | | | - Sergey Moshkovskii
- Institute of Biomedical Chemistry, Moscow, 119121, Russia; Pirogov Russian National Research Medical University (RNRMU), Moscow, 117997, Russia
| | - Alexey Moiseev
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| |
Collapse
|
18
|
Borisov N, Buzdin A. New Paradigm of Machine Learning (ML) in Personalized Oncology: Data Trimming for Squeezing More Biomarkers From Clinical Datasets. Front Oncol 2019; 9:658. [PMID: 31380288 PMCID: PMC6650540 DOI: 10.3389/fonc.2019.00658] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 07/05/2019] [Indexed: 11/13/2022] Open
Affiliation(s)
- Nicolas Borisov
- Department of Personalized Medicine, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Anton Buzdin
- Department of Personalized Medicine, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.,Department of Genomics and Postgenomic Technologies, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.,Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States
| |
Collapse
|
19
|
Yu J, Hu Y, Xu Y, Wang J, Kuang J, Zhang W, Shao J, Guo D, Wang Y. LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features. BMC Cancer 2019; 19:263. [PMID: 30902072 PMCID: PMC6431052 DOI: 10.1186/s12885-019-5433-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 03/03/2019] [Indexed: 02/08/2023] Open
Abstract
Background Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation. Methods We obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis. Results Genes were found with higher mutation rates in good (≥ 3-year survival) than in poor (< 3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. Support Vector Machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC curves. Conclusion The prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine. Electronic supplementary material The online version of this article (10.1186/s12885-019-5433-7) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Jiaxian Yu
- Department of Cell Biology and Genetics, Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Yueming Hu
- Department of Cell Biology and Genetics, Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Yafei Xu
- Department of Cell Biology and Genetics, Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Jue Wang
- State Key Laboratory of Agrobiotechnology and School of Life Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Jiajie Kuang
- Department of Cell Biology and Genetics, Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Wei Zhang
- Sehnzhen GenRead Technology Co., Ltd., Shenzhen, 518000, China
| | - Jianlin Shao
- Zhejiang Hospital, 12 Lingyin Rd, Hangzhou, 310003, China
| | - Dianjing Guo
- State Key Laboratory of Agrobiotechnology and School of Life Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
| | - Yejun Wang
- Department of Cell Biology and Genetics, Shenzhen University Health Science Center, Shenzhen, 518060, China.
| |
Collapse
|
20
|
Tkachev V, Sorokin M, Mescheryakov A, Simonov A, Garazha A, Buzdin A, Muchnik I, Borisov N. FLOating-Window Projective Separator (FloWPS): A Data Trimming Tool for Support Vector Machines (SVM) to Improve Robustness of the Classifier. Front Genet 2019; 9:717. [PMID: 30697229 PMCID: PMC6341065 DOI: 10.3389/fgene.2018.00717] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Accepted: 12/21/2018] [Indexed: 01/31/2023] Open
Abstract
Here, we propose a heuristic technique of data trimming for SVM termed FLOating Window Projective Separator (FloWPS), tailored for personalized predictions based on molecular data. This procedure can operate with high throughput genetic datasets like gene expression or mutation profiles. Its application prevents SVM from extrapolation by excluding non-informative features. FloWPS requires training on the data for the individuals with known clinical outcomes to create a clinically relevant classifier. The genetic profiles linked with the outcomes are broken as usual into the training and validation datasets. The unique property of FloWPS is that irrelevant features in validation dataset that don’t have significant number of neighboring hits in the training dataset are removed from further analyses. Next, similarly to the k nearest neighbors (kNN) method, for each point of a validation dataset, FloWPS takes into account only the proximal points of the training dataset. Thus, for every point of a validation dataset, the training dataset is adjusted to form a floating window. FloWPS performance was tested on ten gene expression datasets for 992 cancer patients either responding or not on the different types of chemotherapy. We experimentally confirmed by leave-one-out cross-validation that FloWPS enables to significantly increase quality of a classifier built based on the classical SVM in most of the applications, particularly for polynomial kernels.
Collapse
Affiliation(s)
- Victor Tkachev
- Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States
| | - Maxim Sorokin
- Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | | | - Alexander Simonov
- Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States
| | - Andrew Garazha
- Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States
| | - Anton Buzdin
- Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.,I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Ilya Muchnik
- Hill Center, Rutgers University, Piscataway, NJ, United States
| | - Nicolas Borisov
- Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States.,I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| |
Collapse
|