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Current Stem Cell Research & Therapy

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ISSN (Print): 1574-888X
ISSN (Online): 2212-3946

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Research Article

Identification of Cancer Stem Cell-related Gene by Single-cell and Machine Learning Predicts Immune Status, Chemotherapy Drug, and Prognosis in Lung Adenocarcinoma

Author(s): Chengcheng Yang, Jinna Zhang, Jintao Xie, Lu Li, Xinyu Zhao, Jinshuang Liu and Xinyan Wang*

Volume 19, Issue 5, 2024

Published on: 09 August, 2023

Page: [767 - 780] Pages: 14

DOI: 10.2174/1574888X18666230714151746

Price: $65

Abstract

Aim: This study aimed to identify the molecular type and prognostic model of lung adenocarcinoma (LUAD) based on cancer stem cell-related genes. Studies have shown that cancer stem cells (CSC) are involved in the development, recurrence, metastasis, and drug resistance of tumors.

Method: The clinical information and RNA-seq of LUAD were obtained from the TCGA database. scRNA dataset GSE131907 and 5 GSE datasets were downloaded from the GEO database. Molecular subtypes were identified by ConsensusClusterPlus. A CSC-related prognostic signature was then constructed via univariate Cox and LASSO Cox-regression analysis.

Result: A scRNA-seq GSE131907 dataset was employed to obtain 11 cell clusters, among which, 173 differentially expressed genes in CSC were identified. Moreover, the CSC score and mRNAsi were higher in tumor samples. 18 of 173 genes were survival time-associated genes in both the TCGA-LUDA dataset and the GSE dataset. Next, two molecular subtypes (namely, CSC1 and CSC2) were identified based on 18 survival-related CSC genes with distinct immune profiles and noticeably different prognoses as well as differences in the sensitivity of chemotherapy drugs. 8 genes were used to build a prognostic model in the TCGA-LUAD dataset. High-risk patients faced worse survival than those with a low risk. The robust predictive ability of the risk score was validated by the time-dependent ROC curve revealed as well as the GSE dataset. TIDE analysis showed a higher sensitivity of patients in the low group to immunotherapy.

Conclusion: This study has revealed the effect of CSC on the heterogeneity of LUAD, and created an 8 genes prognosis model that can be potentially valuable for predicting the prognosis of LUAD and response to immunotherapy.

Keywords: Cancer stem cells, lung adenocarcinoma, scRNA, prognosis, immune status, chemotherapy drug.

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[1]
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68(6): 394-424.
[http://dx.doi.org/10.3322/caac.21492] [PMID: 30207593]
[2]
Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin 2011; 61(2): 69-90.
[http://dx.doi.org/10.3322/caac.20107] [PMID: 21296855]
[3]
Stella GM, Luisetti M, Pozzi E, Comoglio PM. Oncogenes in non-small-cell lung cancer: Emerging connections and novel therapeutic dynamics. Lancet Respir Med 2013; 1(3): 251-61.
[http://dx.doi.org/10.1016/S2213-2600(13)70009-2] [PMID: 24429131]
[4]
Paez JG, Jänne PA, Lee JC, et al. EGFR mutations in lung cancer: Correlation with clinical response to gefitinib therapy. Science 2004; 304(5676): 1497-500.
[http://dx.doi.org/10.1126/science.1099314] [PMID: 15118125]
[5]
Walcher L, Kistenmacher AK, Suo H, et al. Cancer stem cells—origins and biomarkers: Perspectives for targeted personalized therapies. Front Immunol 2020; 11: 1280.
[http://dx.doi.org/10.3389/fimmu.2020.01280] [PMID: 32849491]
[6]
Jones CL, Inguva A, Jordan CT. Targeting energy metabolism in cancer stem cells: Progress and challenges in leukemia and solid tumors. Cell Stem Cell 2021; 28(3): 378-93.
[http://dx.doi.org/10.1016/j.stem.2021.02.013] [PMID: 33667359]
[7]
Eyler CE, Rich JN. Survival of the fittest: Cancer stem cells in therapeutic resistance and angiogenesis. J Clin Oncol 2008; 26(17): 2839-45.
[http://dx.doi.org/10.1200/JCO.2007.15.1829] [PMID: 18539962]
[8]
Liu B, Du R, Zhou L, et al. miR-200c/141 regulates breast cancer stem cell heterogeneity via targeting HIPK1/β-catenin axis. Theranostics 2018; 8(21): 5801-13.
[http://dx.doi.org/10.7150/thno.29380] [PMID: 30613263]
[9]
La Noce M, Paino F, Mele L, et al. HDAC2 depletion promotes osteosarcoma’s stemness both in vitro and in vivo: A study on a putative new target for CSCs directed therapy. J Exp Clin Cancer Res 2018; 37(1): 296.
[http://dx.doi.org/10.1186/s13046-018-0978-x] [PMID: 30509303]
[10]
Takahashi R, Miyazaki H, Ochiya T. The role of microRNAs in the regulation of cancer stem cells. Front Genet 2014; 4: 295.
[http://dx.doi.org/10.3389/fgene.2013.00295] [PMID: 24427168]
[11]
Danaher P, Warren S, Lu R, et al. Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA). J Immunother Cancer 2018; 6(1): 63.
[http://dx.doi.org/10.1186/s40425-018-0367-1] [PMID: 29929551]
[12]
Toro-Domínguez D, Martorell-Marugán J, López-Domínguez R, et al. ImaGEO: Integrative gene expression meta-analysis from GEO database. Bioinformatics 2019; 35(5): 880-2.
[http://dx.doi.org/10.1093/bioinformatics/bty721] [PMID: 30137226]
[13]
Gautier L, Cope L, Bolstad BM, Irizarry RA. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 2004; 20(3): 307-15.
[http://dx.doi.org/10.1093/bioinformatics/btg405] [PMID: 14960456]
[14]
Pereira WJ, Almeida FM, Conde D, et al. Asc-Seurat: Analytical single-cell Seurat-based web application. BMC Bioinformatics 2021; 22(1): 556.
[http://dx.doi.org/10.1186/s12859-021-04472-2] [PMID: 34794383]
[15]
Yi M, Nissley DV, McCormick F, Stephens RM. ssGSEA score-based Ras dependency indexes derived from gene expression data reveal potential Ras addiction mechanisms with possible clinical implications. Sci Rep 2020; 10(1): 10258.
[http://dx.doi.org/10.1038/s41598-020-66986-8] [PMID: 32581224]
[16]
Wilkerson MD, Hayes DN. ConsensusClusterPlus: A class discovery tool with confidence assessments and item tracking. Bioinformatics 2010; 26(12): 1572-3.
[http://dx.doi.org/10.1093/bioinformatics/btq170] [PMID: 20427518]
[17]
Yu G, Wang LG, Han Y, He QY. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS 2012; 16(5): 284-7.
[http://dx.doi.org/10.1089/omi.2011.0118] [PMID: 22455463]
[18]
Charoentong P, Finotello F, Angelova M, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep 2017; 18(1): 248-62.
[http://dx.doi.org/10.1016/j.celrep.2016.12.019] [PMID: 28052254]
[19]
Fu J, Li K, Zhang W, et al. Large-scale public data reuse to model immunotherapy response and resistance. Genome Med 2020; 12(1): 21.
[http://dx.doi.org/10.1186/s13073-020-0721-z] [PMID: 32102694]
[20]
He Y, Jiang Z, Chen C, Wang X. Classification of triple-negative breast cancers based on Immunogenomic profiling. J Exp Clin Cancer Res 2018; 37(1): 327.
[http://dx.doi.org/10.1186/s13046-018-1002-1] [PMID: 30594216]
[21]
Geeleher P, Cox N, Huang RS. pRRophetic: An R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One 2014; 9(9): e107468.
[http://dx.doi.org/10.1371/journal.pone.0107468] [PMID: 25229481]
[22]
Engebretsen S, Bohlin J. Statistical predictions with glmnet. Clin Epigenetics 2019; 11(1): 123.
[http://dx.doi.org/10.1186/s13148-019-0730-1] [PMID: 31443682]
[23]
Shen W, Song Z, Zhong X, et al. Sangerbox: A comprehensive, interaction‐friendly clinical bioinformatics analysis platform. iMeta 2022; 1(3): e36.
[http://dx.doi.org/10.1002/imt2.36]
[24]
Rong L, Xu Y, Zhang K, Jin L, Liu X. HNRNPA2B1 inhibited SFRP2 and activated Wnt-β/catenin via m6A-mediated miR-106b-5p processing to aggravate stemness in lung adenocarcinoma. Pathol Res Pract 2022; 233: 153794.
[http://dx.doi.org/10.1016/j.prp.2022.153794] [PMID: 35364458]
[25]
Xu C, Jin G, Wu H, et al. SIRPγ-expressing cancer stem-like cells promote immune escape of lung cancer via Hippo signaling. J Clin Invest 2022; 132(5): e141797.
[http://dx.doi.org/10.1172/JCI141797]
[26]
Buccarelli M, Marconi M, Pacioni S, et al. Inhibition of autophagy increases susceptibility of glioblastoma stem cells to temozolomide by igniting ferroptosis. Cell Death Dis 2018; 9(8): 841.
[http://dx.doi.org/10.1038/s41419-018-0864-7] [PMID: 30082680]
[27]
Monteleone L, Speciale A, Valenti GE, Traverso N, Ravera S, Garbarino O. PKCα inhibition as a strategy to sensitize neuroblastoma stem cells to etoposide by stimulating ferroptosis. Antioxidants 2021; 10(5): 691.
[http://dx.doi.org/10.3390/antiox10050691]
[28]
Li S, Chen R, Luo W, et al. Identification of a four cancer stem cell-related gene signature and establishment of a prognostic nomogram predicting overall survival of pancreatic adenocarcinoma. Comb Chem High Throughput Screen 2022; 25(12): 2070-81.
[http://dx.doi.org/10.2174/1386207325666220113142212] [PMID: 35048799]
[29]
Kim SI, Woo SR, Noh JK, et al. Association between cancer stem cell gene expression signatures and prognosis in head and neck squamous cell carcinoma. BMC Cancer 2022; 22(1): 1077.
[http://dx.doi.org/10.1186/s12885-022-10184-4] [PMID: 36261806]
[30]
Aikemu B, Shao Y, Yang G, et al. NDRG1 regulates filopodia-induced colorectal cancer invasiveness via modulating CDC42 activity. Int J Biol Sci 2021; 17(7): 1716-30.
[http://dx.doi.org/10.7150/ijbs.56694] [PMID: 33994856]
[31]
Krop I, Parker MT, Bloushtain-Qimron N, et al. HIN-1, an inhibitor of cell growth, invasion, and AKT activation. Cancer Res 2005; 65(21): 9659-69.
[http://dx.doi.org/10.1158/0008-5472.CAN-05-1663] [PMID: 16266985]
[32]
Kiyohara C, Yoshimasu K, Takayama K, Nakanishi Y. EPHX1 polymorphisms and the risk of lung cancer: a HuGE review. Epidemiology 2006; 17(1): 89-99.
[http://dx.doi.org/10.1097/01.ede.0000187627.70026.23] [PMID: 16357600]
[33]
Wu X, Xu QJ, Chen PZ, Yu CB, Ye LF, Li T. Association between CYP17A1, CYB5A polymorphisms and efficacy of abiraterone acetate/prednisone treatment in castration-resistant prostate cancer patients. Pharm Genomics Pers Med 2020; 13: 181-8.
[http://dx.doi.org/10.2147/PGPM.S245086] [PMID: 32581567]
[34]
Blanke KL, Sacco JC, Millikan RC, Olshan AF, Luo J, Trepanier LA. Polymorphisms in the carcinogen detoxification genes CYB5A and CYB5R3 and breast cancer risk in African American women. Cancer Causes Control 2014; 25(11): 1513-21.
[http://dx.doi.org/10.1007/s10552-014-0454-7] [PMID: 25225034]
[35]
Sin DD, Tammemagi CM, Lam S, et al. Pro-surfactant protein B as a biomarker for lung cancer prediction. J Clin Oncol 2013; 31(36): 4536-43.
[http://dx.doi.org/10.1200/JCO.2013.50.6105] [PMID: 24248694]
[36]
Wang Z, Ying M, Wu Q, Wang R, Li Y. Overexpression of myosin VI regulates gastric cancer cell progression. Gene 2016; 593(1): 100-9.
[http://dx.doi.org/10.1016/j.gene.2016.08.015] [PMID: 27515005]
[37]
Yang Q. MicroRNA-5195-3p plays a suppressive role in cell proliferation, migration and invasion by targeting MYO6 in human non-small cell lung cancer. Biosci Biotechnol Biochem 2019; 83(2): 212-20.
[http://dx.doi.org/10.1080/09168451.2018.1540288] [PMID: 30387375]
[38]
Liu S, Zhang HL, Li J, et al. Tubastatin A potently inhibits GPX4 activity to potentiate cancer radiotherapy through boosting ferroptosis. Redox Biol 2023; 62: 102677.
[http://dx.doi.org/10.1016/j.redox.2023.102677] [PMID: 36989572]
[39]
Peter RM, Sarwar MS, Mostafa SZ, Wang Y, Su X, Kong AN. Histone deacetylase inhibitor belinostat regulates metabolic reprogramming in killing KRAS-mutant human lung cancer cells. Mol Carcinog 2023; mc.23551.
[http://dx.doi.org/10.1002/mc.23551] [PMID: 37144836]
[40]
Alliluev AP, Kotel’nikova OV, Kuvakina VA, Basnak’ian IA, Valerius II. [Immunologic properties of purified and complex preparations of group-B meningococcal polysaccharide]. Zh Mikrobiol Epidemiol Immunobiol 1986; (9): 7-12.
[PMID: 3098007]
[41]
Yao Z, Zhang J, Zhang B, et al. Imatinib prevents lung cancer metastasis by inhibiting M2-like polarization of macrophages. Pharmacol Res 2018; 133: 121-31.
[http://dx.doi.org/10.1016/j.phrs.2018.05.002] [PMID: 29730267]
[42]
Krishnamurthy S, Ng VWL, Gao S, Tan MH, Yang YY. Phenformin-loaded polymeric micelles for targeting both cancer cells and cancer stem cells in vitro and in vivo. Biomaterials 2014; 35(33): 9177-86.
[http://dx.doi.org/10.1016/j.biomaterials.2014.07.018] [PMID: 25106770]

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