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Yang X, Wu C, Liu W, Fu K, Tian Y, Wei X, Zhang W, Sun P, Luo H, Huang J. A clinical-information-free method for early diagnosis of lung cancer from the patients with pulmonary nodules based on backpropagation neural network model. Comput Struct Biotechnol J 2024; 24:404-411. [PMID: 38813092 PMCID: PMC11134880 DOI: 10.1016/j.csbj.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 04/15/2024] [Accepted: 05/07/2024] [Indexed: 05/31/2024] Open
Abstract
Lung cancer is the main cause of cancer-related deaths worldwide. Due to lack of obvious clinical symptoms in the early stage of the lung cancer, it is hard to distinguish between malignancy and pulmonary nodules. Understanding the immune responses in the early stage of malignant lung cancer patients may provide new insights for diagnosis. Here, using high-through-put sequencing, we obtained the TCRβ repertoires in the peripheral blood of 100 patients with Stage I lung cancer and 99 patients with benign pulmonary nodules. Our analysis revealed that the usage frequencies of TRBV, TRBJ genes, and V-J pairs and TCR diversities indicated by D50s, Shannon indexes, Simpson indexes, and the frequencies of the largest TCR clone in the malignant samples were significantly different from those in the benign samples. Furthermore, reduced TCR diversities were correlated with the size of pulmonary nodules. Moreover, we built a backpropagation neural network model with no clinical information to identify lung cancer cases from patients with pulmonary nodules using 15 characteristic TCR clones. Based on the model, we have created a web server named "Lung Cancer Prediction" (LCP), which can be accessed at http://i.uestc.edu.cn/LCP/index.html.
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Affiliation(s)
- Xin Yang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Changchun Wu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wenwen Liu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Kaiyu Fu
- Department of Obstetrics and Gynecology, West China Second University Hospital of Sichuan University, Chengdu 610041, China
| | - Yuke Tian
- Department of medical oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China
| | - Xing Wei
- Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China
| | - Wei Zhang
- Department of medical oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China
| | - Ping Sun
- Department of Health Management Center & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu 610072, China
| | - Huaichao Luo
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, China
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Thirunavukkarasu MK, Ramesh P, Karuppasamy R, Veerappapillai S. Transcriptome profiling and metabolic pathway analysis towards reliable biomarker discovery in early-stage lung cancer. J Appl Genet 2024:10.1007/s13353-024-00847-2. [PMID: 38443694 DOI: 10.1007/s13353-024-00847-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/07/2024]
Abstract
Earlier diagnosis of lung cancer is crucial for reducing mortality and morbidity in high-risk patients. Liquid biopsy is a critical technique for detecting the cancer earlier and tracking the treatment outcomes. However, noninvasive biomarkers are desperately needed due to the lack of therapeutic sensitivity and early-stage diagnosis. Therefore, we have utilized transcriptomic profiling of early-stage lung cancer patients to discover promising biomarkers and their associated metabolic functions. Initially, PCA highlights the diversity level of gene expression in three stages of lung cancer samples. We have identified two major clusters consisting of highly variant genes among the three stages. Further, a total of 7742, 6611, and 643 genes were identified as DGE for stages I-III respectively. Topological analysis of the protein-protein interaction network resulted in seven candidate biomarkers such as JUN, LYN, PTK2, UBC, HSP90AA1, TP53, and UBB cumulatively for the three stages of lung cancers. Gene enrichment and KEGG pathway analyses aid in the comprehension of pathway mechanisms and regulation of identified hub genes in lung cancer. Importantly, the medial survival rates up to ~ 70 months were identified for hub genes during the Kaplan-Meier survival analysis. Moreover, the hub genes displayed the significance of risk factors during gene expression analysis using TIMER2.0 analysis. Therefore, we have reason that these biomarkers may serve as a prospective targeting candidate with higher treatment efficacy in early-stage lung cancer patients.
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Affiliation(s)
| | - Priyanka Ramesh
- Bioinformatics Core, College of Agriculture, Agriculture Research and Graduate Education, Purdue University, West Lafayette, IN, 47907, USA
| | - Ramanathan Karuppasamy
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India
| | - Shanthi Veerappapillai
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
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Bhattacharya S, Mahato RK, Singh S, Bhatti GK, Mastana SS, Bhatti JS. Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches. Life Sci 2023; 332:122110. [PMID: 37734434 DOI: 10.1016/j.lfs.2023.122110] [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: 09/08/2023] [Accepted: 09/18/2023] [Indexed: 09/23/2023]
Abstract
Thyroid cancer continues to exhibit a rising incidence globally, predominantly affecting women. Despite stable mortality rates, the unique characteristics of thyroid carcinoma warrant a distinct approach. Differentiated thyroid cancer, comprising most cases, is effectively managed through standard treatments such as thyroidectomy and radioiodine therapy. However, rarer variants, including anaplastic thyroid carcinoma, necessitate specialized interventions, often employing targeted therapies. Although these drugs focus on symptom management, they are not curative. This review delves into the fundamental modulators of thyroid cancers, encompassing genetic, epigenetic, and non-coding RNA factors while exploring their intricate interplay and influence. Epigenetic modifications directly affect the expression of causal genes, while long non-coding RNAs impact the function and expression of micro-RNAs, culminating in tumorigenesis. Additionally, this article provides a concise overview of the advantages and disadvantages associated with pharmacological and non-pharmacological therapeutic interventions in thyroid cancer. Furthermore, with technological advancements, integrating modern software and computing into healthcare and medical practices has become increasingly prevalent. Artificial intelligence and machine learning techniques hold the potential to predict treatment outcomes, analyze data, and develop personalized therapeutic approaches catering to patient specificity. In thyroid cancer, cutting-edge machine learning and deep learning technologies analyze factors such as ultrasonography results for tumor textures and biopsy samples from fine needle aspirations, paving the way for a more accurate and effective therapeutic landscape in the near future.
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Affiliation(s)
- Srinjan Bhattacharya
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda 151401, Punjab, India
| | - Rahul Kumar Mahato
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda 151401, Punjab, India
| | - Satwinder Singh
- Department of Computer Science and Technology, Central University of Punjab, Bathinda 151401, Punjab, India.
| | - Gurjit Kaur Bhatti
- Department of Medical Lab Technology, University Institute of Applied Health Sciences, Chandigarh University, Mohali, India
| | - Sarabjit Singh Mastana
- School of Sport, Exercise and Health Sciences, Loughborough University, Epinal Way, Leicestershire, Loughborough LE11 3TU, UK.
| | - Jasvinder Singh Bhatti
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda 151401, Punjab, India.
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Leal AIC, Mathios D, Jakubowski D, Johansen JS, Lau A, Wu T, Cristiano S, Medina JE, Phallen J, Bruhm DC, Carey J, Dracopoli NC, Bojesen SE, Scharpf RB, Velculescu VE, Vachani A, Bach PB. Cell-Free DNA Fragmentomes in the Diagnostic Evaluation of Patients With Symptoms Suggestive of Lung Cancer. Chest 2023; 164:1019-1027. [PMID: 37116747 DOI: 10.1016/j.chest.2023.04.033] [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: 10/31/2022] [Revised: 04/13/2023] [Accepted: 04/16/2023] [Indexed: 04/30/2023] Open
Abstract
BACKGROUND The diagnostic workup of individuals suspected of having lung cancer can be complex and protracted because conventional symptoms of lung cancer have low specificity and sensitivity. RESEARCH QUESTION Among individuals with symptoms of lung cancer, can a blood-based approach to analyze cell-free DNA (cfDNA) fragmentation (the DNA evaluation of fragments for early interception [DELFI] score) enhance evaluation for the possible presence of lung cancer? STUDY DESIGN AND METHODS Adults were referred to Bispebjerg Hospital (Copenhagen, Denmark) for diagnostic evaluation of initial imaging anomalies and symptoms consistent with lung cancer. Numbers and types of symptoms were extracted from medical records. cfDNA from plasma samples obtained at the prediagnostic visit was isolated, sequenced, and analyzed for genome-wide cfDNA fragmentation patterns. The relationships among clinical presentation, cancer status, and DELFI score were examined. RESULTS A total of 296 individuals were analyzed. Median DELFI scores were higher for those with lung cancer (n = 98) than those without cancer (n = 198; 0.94 vs 0.19; P < .001). In a multivariate model adjusted for age, smoking history, and presenting symptoms, the addition of the DELFI score improved the prediction of lung cancer for those who demonstrated symptoms (area under the receiver operating characteristic curve, 0.74-0.94). INTERPRETATION The DELFI score distinguishes individuals with lung cancer from those without cancer better than suspicious symptoms do. These results represent proof-of-concept support that fragmentation-based biomarker approaches may facilitate diagnostic resolution for patients with concerning symptoms of lung cancer.
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Affiliation(s)
| | - Dimitrios Mathios
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | | | - Jakob S Johansen
- Department of Oncology, Copenhagen University Hospital-Herlev and Gentofte Hospital, Herlev, Denmark
| | - Anna Lau
- Delfi Diagnostics, Inc., Baltimore, MD
| | - Tony Wu
- Delfi Diagnostics, Inc., Baltimore, MD
| | - Stephen Cristiano
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Jamie E Medina
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Jillian Phallen
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Daniel C Bruhm
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | | | | | - Stig E Bojesen
- Department of Clinical Biochemistry, Copenhagen University Hospital-Herlev and Gentofte Hospital, Herlev, Denmark
| | - Robert B Scharpf
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Victor E Velculescu
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Anil Vachani
- University of Pennsylvania School of Medicine, Philadelphia, PA
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Ye Q, Raese R, Luo D, Cao S, Wan YW, Qian Y, Guo NL. MicroRNA, mRNA, and Proteomics Biomarkers and Therapeutic Targets for Improving Lung Cancer Treatment Outcomes. Cancers (Basel) 2023; 15:cancers15082294. [PMID: 37190222 DOI: 10.3390/cancers15082294] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 05/17/2023] Open
Abstract
The majority of lung cancer patients are diagnosed with metastatic disease. This study identified a set of 73 microRNAs (miRNAs) that classified lung cancer tumors from normal lung tissues with an overall accuracy of 96.3% in the training patient cohort (n = 109) and 91.7% in unsupervised classification and 92.3% in supervised classification in the validation set (n = 375). Based on association with patient survival (n = 1016), 10 miRNAs were identified as potential tumor suppressors (hsa-miR-144, hsa-miR-195, hsa-miR-223, hsa-miR-30a, hsa-miR-30b, hsa-miR-30d, hsa-miR-335, hsa-miR-363, hsa-miR-451, and hsa-miR-99a), and 4 were identified as potential oncogenes (hsa-miR-21, hsa-miR-31, hsa-miR-411, and hsa-miR-494) in lung cancer. Experimentally confirmed target genes were identified for the 73 diagnostic miRNAs, from which proliferation genes were selected from CRISPR-Cas9/RNA interference (RNAi) screening assays. Pansensitive and panresistant genes to 21 NCCN-recommended drugs with concordant mRNA and protein expression were identified. DGKE and WDR47 were found with significant associations with responses to both systemic therapies and radiotherapy in lung cancer. Based on our identified miRNA-regulated molecular machinery, an inhibitor of PDK1/Akt BX-912, an anthracycline antibiotic daunorubicin, and a multi-targeted protein kinase inhibitor midostaurin were discovered as potential repositioning drugs for treating lung cancer. These findings have implications for improving lung cancer diagnosis, optimizing treatment selection, and discovering new drug options for better patient outcomes.
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Affiliation(s)
- Qing Ye
- West Virginia University Cancer Institute, West Virginia University, Morgantown, WV 26506, USA
| | - Rebecca Raese
- West Virginia University Cancer Institute, West Virginia University, Morgantown, WV 26506, USA
| | - Dajie Luo
- West Virginia University Cancer Institute, West Virginia University, Morgantown, WV 26506, USA
| | - Shu Cao
- West Virginia University Cancer Institute, West Virginia University, Morgantown, WV 26506, USA
| | - Ying-Wooi Wan
- West Virginia University Cancer Institute, West Virginia University, Morgantown, WV 26506, USA
| | - Yong Qian
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505, USA
| | - Nancy Lan Guo
- West Virginia University Cancer Institute, West Virginia University, Morgantown, WV 26506, USA
- Department of Occupational and Environmental Health Sciences, School of Public Health, West Virginia University, Morgantown, WV 26506, USA
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Zheng C, Hu X, Sun S, Zhu L, Wang N, Zhang J, Huang G, Wang Y, Huang X, Wang L, Shen Z. Hairpin allosteric molecular beacons-based cascaded amplification for effective detection of lung cancer-associated microRNA. Talanta 2022; 244:123412. [DOI: 10.1016/j.talanta.2022.123412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 02/07/2022] [Accepted: 03/25/2022] [Indexed: 12/25/2022]
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Circulating MicroRNAs as a Tool for Diagnosis of Liver Disease Progression in People Living with HIV-1. Viruses 2022; 14:v14061118. [PMID: 35746590 PMCID: PMC9227922 DOI: 10.3390/v14061118] [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: 03/22/2022] [Accepted: 05/20/2022] [Indexed: 02/07/2023] Open
Abstract
MicroRNAs (miRNAs) are small, non-coding RNAs that post-transcriptionally regulate gene expression by binding specific cell mRNA targets, preventing their translation. miRNAs are implicated in the regulation of important physiological and pathological pathways. Liver disease, including injury, fibrosis, metabolism dysregulation, and tumor development disrupts liver-associated miRNAs. In addition to their effect in the originating tissue, miRNAs can also circulate in body fluids. miRNA release is an important form of intercellular communication that plays a role in the physiological and pathological processes underlying multiple diseases. Circulating plasma levels of miRNAs have been identified as potential disease biomarkers. One of the main challenges clinics face is the lack of available noninvasive biomarkers for diagnosing and predicting the different stages of liver disease (e.g., nonalcoholic fatty liver disease and nonalcoholic steatohepatitis), particularly among individuals infected with human immunodeficiency virus type 1 (HIV-1). Liver disease is a leading cause of death unrelated to acquired immunodeficiency syndrome (AIDS) among people living with HIV-1 (PLWH). Here, we review and discuss the utility of circulating miRNAs as biomarkers for early diagnosis, prognosis, and assessment of liver disease in PLWH. Remarkably, the identification of dysregulated miRNA expression may also identify targets for new therapeutics.
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9
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Yao Q, Zhang X, Chen D. Emerging Roles and Mechanisms of lncRNA FOXD3-AS1 in Human Diseases. Front Oncol 2022; 12:848296. [PMID: 35280790 PMCID: PMC8914342 DOI: 10.3389/fonc.2022.848296] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 02/01/2022] [Indexed: 01/02/2023] Open
Abstract
Numerous long noncoding RNAs (lncRNAs) have been identified as powerful regulators of human diseases. The lncRNA FOXD3-AS1 is a novel lncRNA that was recently shown to exert imperative roles in the initialization and progression of several diseases. Emerging studies have shown aberrant expression of FOXD3-AS1 and close correlation with pathophysiological traits of numerous diseases, particularly cancers. More importantly, FOXD3-AS1 was also found to ubiquitously impact a range of biological functions. This study aims to summarize the expression, associated clinicopathological features, major functions and molecular mechanisms of FOXD3-AS1 in human diseases and to explore its possible clinical applications.
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Affiliation(s)
- Qinfan Yao
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Kidney Disease Prevention and Control Technology, Hangzhou, China
- National Key Clinical Department of Kidney Diseases, Institute of Nephrology, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Xiuyuan Zhang
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Kidney Disease Prevention and Control Technology, Hangzhou, China
- National Key Clinical Department of Kidney Diseases, Institute of Nephrology, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Dajin Chen
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Kidney Disease Prevention and Control Technology, Hangzhou, China
- National Key Clinical Department of Kidney Diseases, Institute of Nephrology, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
- *Correspondence: Dajin Chen,
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Computational Analyses of YY1 and Its Target RKIP Reveal Their Diagnostic and Prognostic Roles in Lung Cancer. Cancers (Basel) 2022; 14:cancers14040922. [PMID: 35205667 PMCID: PMC8869872 DOI: 10.3390/cancers14040922] [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: 11/11/2021] [Revised: 01/18/2022] [Accepted: 02/08/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Lung cancer (LC) is the tumor with the highest global mortality rate. Novel personalized therapies are currently being tested (e.g., targeted inhibitors, the immune-checkpoint inhibitors), but they cannot yet prevent the very frequent relapse and generalized metastases observed in a large population of LC patients. Currently, there is an urgent need for novel reliable biomarkers for the prognosis and diagnosis of LC. Through the systematic analysis of multiple deposited expression datasets, this report aims to explore the role of the Yin-Yang 1 (YY1) transcription factor and its target the Raf Kinase Inhibitory Protein (RKIP) in LC. The computational analysis suggested the predictive diagnostic and prognostic roles for both YY1 and RKIP stimulating further studies for proving their implication as novel biomarkers, as well as therapeutically druggable targets in LC. Abstract Lung cancer (LC) represents a global threat, being the tumor with the highest mortality rate. Despite the introduction of novel therapies (e.g., targeted inhibitors, immune-checkpoint inhibitors), relapses are still very frequent. Accordingly, there is an urgent need for reliable predictive biomarkers and therapeutically druggable targets. Yin-Yang 1 (YY1) is a transcription factor that may work either as an oncogene or a tumor suppressor, depending on the genotype and the phenotype of the tumor. The Raf Kinase Inhibitory Protein (RKIP), is a tumor suppressor and immune enhancer often found downregulated in the majority of the examined cancers. In the present report, the role of both YY1 and RKIP in LC is thoroughly explored through the analysis of several deposited RNA and protein expression datasets. The computational analyses revealed that YY1 negatively regulates RKIP expression in LC, as corroborated by the deposited YY1-ChIP-Seq experiments and validated by their robust negative correlation. Additionally, YY1 expression is significantly higher in LC samples compared to normal matching ones, whereas RKIP expression is lower in LC and high in normal matching tissues. These observed differences, unlike many current biomarkers, bear a diagnostic significance, as proven by the ROC analyses. Finally, the survival data support the notion that both YY1 and RKIP might represent strong prognostic biomarkers. Overall, the reported findings indicate that YY1 and RKIP expression levels may play a role in LC as potential biomarkers and therapeutic targets. However, further studies will be necessary to validate the in silico results.
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