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Charkiewicz R, Sulewska A, Karabowicz P, Lapuc G, Charkiewicz A, Kraska M, Pancewicz J, Lukasik M, Kozlowski M, Stec R, Ziembicka D, Piszcz W, Miltyk W, Niklinska W. Six-Gene Signature for Differential Diagnosis and Therapeutic Decisions in Non-Small-Cell Lung Cancer-A Validation Study. Int J Mol Sci 2024; 25:3607. [PMID: 38612418 PMCID: PMC11011743 DOI: 10.3390/ijms25073607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/21/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024] Open
Abstract
Non-small-cell lung cancer (NSCLC) poses a challenge due to its heterogeneity, necessitating precise histopathological subtyping and prognostication for optimal treatment decision-making. Molecular markers emerge as a potential solution, overcoming the limitations of conventional methods and supporting the diagnostic-therapeutic interventions. In this study, we validated the expression of six genes (MIR205HG, KRT5, KRT6A, KRT6C, SERPINB5, and DSG3), previously identified within a 53-gene signature developed by our team, utilizing gene expression microarray technology. Real-time PCR on 140 thoroughly characterized early-stage NSCLC samples revealed substantial upregulation of all six genes in squamous cell carcinoma (SCC) compared to adenocarcinoma (ADC), regardless of clinical factors. The decision boundaries of the logistic regression model demonstrated effective separation of the relative expression levels between SCC and ADC for most genes, excluding KRT6C. Logistic regression and gradient boosting decision tree classifiers, incorporating all six validated genes, exhibited notable performance (AUC: 0.8930 and 0.8909, respectively) in distinguishing NSCLC subtypes. Nevertheless, our investigation revealed that the gene expression profiles failed to yield predictive value regarding the progression of early-stage NSCLC. Our molecular diagnostic models manifest the potential for an exhaustive molecular characterization of NSCLC, subsequently informing personalized treatment decisions and elevating the standards of clinical management and prognosis for patients.
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Affiliation(s)
- Radoslaw Charkiewicz
- Center of Experimental Medicine, Medical University of Bialystok, 15-369 Bialystok, Poland
- Department of Clinical Molecular Biology, Medical University of Bialystok, 15-269 Bialystok, Poland; (A.S.); (M.K.); (W.P.)
| | - Anetta Sulewska
- Department of Clinical Molecular Biology, Medical University of Bialystok, 15-269 Bialystok, Poland; (A.S.); (M.K.); (W.P.)
| | - Piotr Karabowicz
- Biobank, Medical University of Bialystok, 15-269 Bialystok, Poland;
| | - Grzegorz Lapuc
- Department of Thoracic Surgery, Medical University of Bialystok, 15-269 Bialystok, Poland; (G.L.); (M.K.)
| | - Alicja Charkiewicz
- Department of Analysis and Bioanalysis of Medicines, Medical University of Bialystok, 15-089 Bialystok, Poland; (A.C.); (W.M.)
| | - Marcin Kraska
- Department of Clinical Molecular Biology, Medical University of Bialystok, 15-269 Bialystok, Poland; (A.S.); (M.K.); (W.P.)
- Department of Medical Pathomorphology, Medical University of Bialystok, 15-269 Bialystok, Poland
| | - Joanna Pancewicz
- Department of Histology and Embryology, Medical University of Bialystok, 15-269 Bialystok, Poland; (J.P.); (M.L.)
| | - Malgorzata Lukasik
- Department of Histology and Embryology, Medical University of Bialystok, 15-269 Bialystok, Poland; (J.P.); (M.L.)
| | - Miroslaw Kozlowski
- Department of Thoracic Surgery, Medical University of Bialystok, 15-269 Bialystok, Poland; (G.L.); (M.K.)
| | - Rafal Stec
- Department of Oncology, Medical University of Warsaw, 02-091 Warsaw, Poland;
| | - Dominika Ziembicka
- Department of Public Health, Medical University of Bialystok, 15-295 Bialystok, Poland;
| | - Weronika Piszcz
- Department of Clinical Molecular Biology, Medical University of Bialystok, 15-269 Bialystok, Poland; (A.S.); (M.K.); (W.P.)
| | - Wojciech Miltyk
- Department of Analysis and Bioanalysis of Medicines, Medical University of Bialystok, 15-089 Bialystok, Poland; (A.C.); (W.M.)
| | - Wieslawa Niklinska
- Department of Histology and Embryology, Medical University of Bialystok, 15-269 Bialystok, Poland; (J.P.); (M.L.)
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Pang X, Gao S, Liu T, Xu FX, Fan C, Zhang JF, Jiang H. Identification of STAT3 as a biomarker for cellular senescence in liver fibrosis: A bioinformatics and experimental validation study. Genomics 2024; 116:110800. [PMID: 38286349 DOI: 10.1016/j.ygeno.2024.110800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 01/31/2024]
Abstract
BACKGROUND Cellular senescence is associated with a dysregulated inflammatory response, which is an important driver of the development of liver fibrosis (LF). This study aimed to investigate the effect of cellular senescence on LF and identify potential key biomarkers through bioinformatics analysis combined with validation experiments in vivo and in vitro. METHODS The Gene Expression Omnibus (GEO) database and GeneCards database were used to download the LF dataset and the aging-related gene set, respectively. Functional enrichment analysis of differential genes was then performed using GO and KEGG. Hub genes were further screened using Cytoscape's cytoHubba. Diagnostic values for hub genes were evaluated with a receiver operating characteristic (ROC) curve. Next, CIBERSORTx was used to estimate immune cell types and ratios. Finally, in vivo and in vitro experiments validated the results of the bioinformatics analysis. Moreover, molecular docking was used to simulate drug-gene interactions. RESULTS A total of 44 aging-related differentially expressed genes (AgDEGs) were identified, and enrichment analysis showed that these genes were mainly enriched in inflammatory and immune responses. PPI network analysis identified 6 hub AgDEGs (STAT3, TNF, MMP9, CD44, TGFB1, and TIMP1), and ROC analysis showed that they all have good diagnostic value. Immune infiltration suggested that hub AgDEGs were significantly associated with M1 macrophages or other immune cells. Notably, STAT3 was positively correlated with α-SMA, COL1A1, IL-6 and IL-1β, and was mainly expressed in hepatocytes (HCs). Validation experiments showed that STAT3 expression was upregulated and cellular senescence was increased in LF mice. A co-culture system of HCs and hepatic stellate cells (HSCs) further revealed that inhibiting STAT3 reduced HCs senescence and suppressed HSCs activation. In addition, molecular docking revealed that STAT3 was a potential drug therapy target. CONCLUSIONS STAT3 may be involved in HCs senescence and promote HSCs activation, which in turn leads to the development of LF. Our findings suggest that STAT3 could be a potential biomarker for LF.
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Affiliation(s)
- Xue Pang
- Clinical Research Experiment Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230012, Anhui, China; College of Pharmacy, Anhui University of Chinese Medicine, Hefei 230011, Anhui, China
| | - Shang Gao
- Clinical Research Experiment Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230012, Anhui, China; College of Pharmacy, Anhui University of Chinese Medicine, Hefei 230011, Anhui, China
| | - Tao Liu
- Clinical Research Experiment Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230012, Anhui, China; College of Pharmacy, Anhui University of Chinese Medicine, Hefei 230011, Anhui, China
| | - Feng Xia Xu
- Clinical Research Experiment Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230012, Anhui, China; College of Pharmacy, Anhui University of Chinese Medicine, Hefei 230011, Anhui, China
| | - Chang Fan
- Clinical Research Experiment Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230012, Anhui, China
| | - Jia Fu Zhang
- Department of Pharmacy, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230012, Anhui, China
| | - Hui Jiang
- Clinical Research Experiment Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230012, Anhui, China; College of Pharmacy, Anhui University of Chinese Medicine, Hefei 230011, Anhui, China.
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Alghamdi RA, Al-Zahrani MH. Integrated bioinformatics analyses identifying key transcriptomes correlated with prognosis and immune infiltrations in lung squamous cell carcinoma. Saudi J Biol Sci 2023; 30:103596. [PMID: 36879671 PMCID: PMC9985037 DOI: 10.1016/j.sjbs.2023.103596] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 01/15/2023] [Accepted: 02/06/2023] [Indexed: 02/12/2023] Open
Abstract
Background Lung Squamous Cell Carcinoma (LUSC) is a major subtype of lung malignancies and is associated with the cause of cancer-mediated mortality worldwide. However, identification of transcriptomic signatures associated with survival-prognosis and immunity of tumor remains lacking. Method The GSE2088, GSE6044, GSE19188, GSE21933, GSE33479, GSE33532, and GSE74706 were integrated for identifying differentially expressed genes (DEGs) with combined effect sizes. Also, the TCGA LUSC cohort was used for further analysis. A series of bioinformatics methods were utilized for conducting the whole study. Results The 831 genes (such as DSG3, PKP1, DSC3, TPX2, and UBE2C) were found upregulated and the 731 genes (such as ABCA8, SELENBP1, FAM107A, and CACNA2D2) were downregulated in the LUSC. The functional enrichment analysis identifies the upregulated KEGG pathways, including cell cycle, DNA replication, base excision repair, proteasome, mismatch repair, and cellular senescence. Also, the key hub genes (such as EGFR, HRAS, JUN, CDH1, BRCA1, CASP3, RHOA, HDAC1, HIF1A, and CCNA2) were identified along with the eight gene modules that were significantly related to the protein-protein interaction (PPI). The clinical analyses identified that the overexpression group of CDH3, PLAU, PKP3, STIL, CALU, LOXL2, POSTN, DPP3, GALNT2, LOX, and ITPA are substantially associated with a poor survival prognosis and the downregulated group of IL18R1 showed a similar trend. Moreover, our investigation demonstrated that the survival-associated genes were correlated with the stromal and immune scores in LUSC, indicating that the survival-associated genes regulate tumor immunity. The survival-associated genes were genetically altered in 27% of LUSC patients and showed excellent diagnostic efficiency. Finally, the consistent expression level of CDH3, PLAU, PKP3, STIL, CALU, LOXL2, POSTN, DPP3, GALNT2, and ITPA were found in the TCGA LUSC cohort. Conclusions The identification of key transcriptomic signatures can be elucidated by the crucial mechanism of LUSC carcinogenesis.
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Affiliation(s)
- Rana A. Alghamdi
- Department of Chemistry, Science and Arts College, King Abdulaziz University, Rabigh, Saudi Arabia
- Corresponding author at: Department of Chemistry, Science and Arts College, King Abdulaziz University, Rabigh, Saudi Arabia.
| | - Maryam H. Al-Zahrani
- Biochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
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Zhou Y, Hu Z. Anoikis-related genes combined with single cell sequencing: Insights into model specification of lung adenocarcinoma and applicability for prognosis and therapy. Front Cell Dev Biol 2023; 11:1125782. [PMID: 37169018 PMCID: PMC10165631 DOI: 10.3389/fcell.2023.1125782] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/31/2023] [Indexed: 05/13/2023] Open
Abstract
Background: Anoikis has therapeutic potential against different malignancies including lung adenocarcinoma. This study used anoikis and bioinformatics to construct a prognostic model for lung adenocarcinoma and explore new therapeutic strategies. Methods: Several bioinformatic algorithms (co-expression analysis, univariate Cox analysis, multivariate Cox analysis, and cross-validation) were used to screen anoikis-related genes (ARGs) to construct a risk model. Lung adenocarcinoma patients were divided into training and testing groups at a ratio of 1:1. The prognostic model was validated by risk score comparison between high- and low-risk groups using receiver operating characteristic curve (ROC), nomograms, independent prognostic analysis and principal component analysis. In addition, two anoikis-related genes patterns were classified utilizing consensus clustering method and were compared with each other in survival time, immune microenvironment, and regulation in pathway. Single cell sequencing was applied to analyze anoikis-related genes constructed the model. Results: This study demonstrated the feasibility of the model based on seven anoikis-related genes, as well as identifying axitinib, nibtinib and sorafenib as potential therapeutic strategies for LUAD. Risk score based on this model had could be used as an independent prognostic factor for lung adenocarcinoma (HR > 1; p < 0.001) and had the highest accuracy to predict survival compared with the clinical characteristics. Single cell sequencing analysis discovered Keratin 14 (KRT14, one of the seven anoikis-related genes) was mainly expressed in malignant cells in various cancers. Conclusion: We identified seven anoikis-related genes and constructed an accurate risk model based on bioinformatics analysis that can be used for prognostic prediction and for the design of therapeutic strategies in clinical practice.
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Viehweger F, Azem A, Gorbokon N, Uhlig R, Lennartz M, Rico SD, Kind S, Reiswich V, Kluth M, Hube-Magg C, Bernreuther C, Büscheck F, Clauditz TS, Fraune C, Jacobsen F, Krech T, Lebok P, Steurer S, Burandt E, Minner S, Marx AH, Simon R, Sauter G, Menz A, Hinsch A. Desmoglein 3 (Dsg3) Expression in Cancer: A Tissue Microarray Study on 15,869 Tumors. Pathol Res Pract 2022; 240:154200. [DOI: 10.1016/j.prp.2022.154200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 11/07/2022]
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Kołat D, Kałuzińska Ż, Bednarek AK, Płuciennik E. Prognostic significance of AP-2α/γ targets as cancer therapeutics. Sci Rep 2022; 12:5497. [PMID: 35361846 PMCID: PMC8971500 DOI: 10.1038/s41598-022-09494-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/24/2022] [Indexed: 02/07/2023] Open
Abstract
Identifying genes with prognostic importance could improve cancer treatment. An increasing number of reports suggest the existence of successful strategies based on seemingly "untargetable" transcription factors. In addition to embryogenesis, AP-2 transcription factors are known to play crucial roles in cancer development. Members of this family can be used as prognostic factors in oncological patients, and AP-2α/γ transcription factors were previously investigated in our pan-cancer comparative study using their target genes. The present study investigates tumors that were previously found similar with an emphasis on the possible role of AP-2 factors in specific cancer types. The RData workspace was loaded back to R environment and 3D trajectories were built via Monocle3. The genes that met the requirement of specificity were listed using top_markers(), separately for mutual and unique targets. Furthermore, the candidate genes had to meet the following requirements: correlation with AP-2 factor (through Correlation AnalyzeR) and validated prognostic importance (using GEPIA2 and subsequently KM-plotter or LOGpc). Eventually, the ROC analysis was applied to confirm their predictive value; co-dependence of expression was visualized via BoxPlotR. Some similar tumors were differentiated by AP-2α/γ targets with prognostic value. Requirements were met by only fifteen genes (EMX2, COL7A1, GRIA1, KRT1, KRT14, SLC12A5, SEZ6L, PTPRN, SCG5, DPP6, NTSR1, ARX, COL4A3, PPEF1 and TMEM59L); of these, the last four were excluded based on ROC curves. All the above genes were confronted with the literature, with an emphasis on the possible role played by AP-2 factors in specific cancers. Following ROC analysis, the genes were verified using immunohistochemistry data and progression-related signatures. Staining differences were observed, as well as co-dependence on the expression of e.g. CTNNB1, ERBB2, KRAS, SMAD4, EGFR or MKI67. In conclusion, prognostic value of targets suggested AP-2α/γ as candidates for novel cancer treatment. It was also revealed that AP-2 targets are related to tumor progression and that some mutual target genes could be inversely regulated.
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Affiliation(s)
- Damian Kołat
- Department of Molecular Carcinogenesis, Medical University of Lodz, 90-752, Lodz, Poland.
| | - Żaneta Kałuzińska
- Department of Molecular Carcinogenesis, Medical University of Lodz, 90-752, Lodz, Poland
| | - Andrzej K Bednarek
- Department of Molecular Carcinogenesis, Medical University of Lodz, 90-752, Lodz, Poland
| | - Elżbieta Płuciennik
- Department of Molecular Carcinogenesis, Medical University of Lodz, 90-752, Lodz, Poland
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Cao P, Wu S, Guo W, Zhang Q, Gong W, Li Q, Zhang R, Dong X, Xu S, Liu Y, Shi S, Huang Y, Zhang Y. Precise pathological classification of non-small cell lung adenocarcinoma and squamous carcinoma based on an integrated platform of targeted metabolome and lipidome. Metabolomics 2021; 17:98. [PMID: 34729658 DOI: 10.1007/s11306-021-01849-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/12/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most common subtypes of NSCLC. Despite genetic differences between LUAD and LUSC have been clarified in depth, the metabolic differences of these two subtypes are still unclear. METHODS Totally, 128 plasma samples of NSCLC patients were collected before initial treatments, followed by determination of LC-ESI-Q TRAP-MS/MS. Differentially expressed metabolites were screened based on a strict standard. RESULTS Based on the integrated platform of targeted metabolome and lipidome, a total of 1141 endogenous metabolites (including 809 lipids) were finally detected in the plasma of NSCLC patients, including 16 increased and 3 decreased endogenous compounds in LUAD group when compared with LUSC group. Thereafter, a logistic regression model integrating four differential metabolites [2-(Methylthio) ethanol, Cortisol, D-Glyceric Acid, and N-Acetylhistamine] was established and could accurately differentiate LUAD and LUSC with an area under the ROC curve of 0.946 (95% CI 0.886-1.000). The cut-off value showed a satisfactory efficacy with 92.0% sensitivity and 92.9% specificity. KEGG functional enrichment analysis showed these differentially expressed metabolites could be further enriched in riboflavin metabolism, steroid hormone biosynthesis, prostate cancer, etc. The endogenous metabolites identified in this study have the potential to be used as novel biomarkers to distinguish LUAD from LUSC. CONCLUSIONS Our research might provide more evidence for exploring the pathogenesis and differentiation of NSCLC. This research could promote a deeper understanding and precise treatment of lung cancer.
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Affiliation(s)
- Peng Cao
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Sanlan Wu
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Wei Guo
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Qilin Zhang
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Weijing Gong
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Qiang Li
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Rui Zhang
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Xiaorong Dong
- Cancer center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Shuangbing Xu
- Cancer center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Yani Liu
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Shaojun Shi
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Yifei Huang
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China.
| | - Yu Zhang
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China.
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