Which of the following best predicts how GSK3βGSK3β mutations can lead to the development of cancer?

Abstract

Lung adenocarcinoma is one of the most frequent tumor subtypes, involving changes in a variety of oncogenes and tumor suppressor genes. Hydroxysteroid 17-Beta Dehydrogenase 6 [HSD17B6] could synthetize dihydrotestosterone, abnormal levels of which are associated with progression of multiple tumors. Previously, we showed that HSD17B6 inhibits malignant progression of hepatocellular carcinoma. However, the mechanisms underlying inhibiting tumor development by HSD17B6 are not clear. Moreover, its role in lung adenocarcinoma [LUAD] is yet unknown. Here, we investigated its expression profile and biological functions in LUAD. Analysis of data from the LUAD datasets of TCGA, CPTAC, Oncomine, and GEO revealed that HSD17B6 mRNA and protein expression was frequently lower in LUAD than in non-neoplastic lung tissues, and its low expression correlated significantly with advanced tumor stage, large tumor size, poor tumor differentiation, high tumor grade, smoking, and poor prognosis in LUAD. In addition, its expression was negatively regulated by miR-31-5p in LUAD. HSD17B6 suppressed LUAD cell proliferation, migration, invasion, epithelial–mesenchymal transition [EMT], and radioresistance. Furthermore, HSD17B6 overexpression in LUAD cell lines enhanced PTEN expression and inhibited AKT phosphorylation, inactivating downstream oncogenes like GSK3β, β-catenin, and Cyclin-D independent of dihydrotestosterone, revealing an underlying antitumor mechanism of HSD17B6 in LUAD. Our findings indicate that HSD17B6 may function as a tumor suppressor in LUAD and could be a promising prognostic indicator for LUAD patients, especially for those receiving radiotherapy.

Background

More than 2.2 million patients were diagnosed with lung cancer in 2020, making it the second most common tumor worldwide. It caused about 1.8 million deaths in 2020 globally and ranked as the first leading cause of cancer-associated death [1]. Approximately 50% of lung cancers are LUAD, which is the most common subtype [2]. Although new therapeutic strategies, such as targeted therapies, have achieved remarkable improvements in recent years, LUAD is still one of the most aggressive and fatal tumor types with overall survival 50 cells were counted. The surviving fraction was calculated as previously described [60].

Western blotting

For preparing cell lysates for western blotting, cells were lysed in ice-cold cell lysis buffer and centrifuged to remove cell debris. Nuclear and cytoplasmic fractions were extracted from the cell pellets as previously described [61]. After electrophoresis, proteins were transferred to a PVDF membrane. After blocking, membranes were incubated with the following primary antibodies: GAPDH [ProteinTech #60004-1-Ig], HSD17B6 [ProteinTech #11855-1-AP], AKT [CST #C6717], p-AKT [Ser473] [CST #4060], PTEN [ProteinTech #22034-1-AP], MMP2 [ProteinTech #10373-2-AP], MMP9 [ProteinTech #10375-2-AP], E-cadherin [ProteinTech #20874-1-AP], N-cadherin [ProteinTech #66219-1-Ig], Vimentin [ProteinTech #10366-1-AP], snail [CST #3895], survivin [CST #2808], GSK3β [ProteinTech #22104-1-AP], p-GSK3β [CST #9322], β-catenin [CST #8480], cyclin D1 [ProteinTech #60186-1-Ig], cyclin E1 [ProteinTech #11554-1-AP], PCNA [ProteinTech #10205-2-AP], and Lamin A/C [ProteinTech #10298-1-AP] overnight at 4 ˚C. Then, the membranes were incubated with secondary antibodies: HRP-conjugated anti‑rabbit IgG [ProteinTech #SA00001‑2] or anti‑mouse IgG [ProteinTech #SA00001‑1] for 1 h at room temperature. All the experiments were performed in triplicate.

BSP analysis

BSP analysis was performed as previously described [62]. Briefly, genomic DNA was extracted from cells, then qualified and quantified by a NanoPhotometer [IMPLEN]. The bisulfite conversion was carried out using EZ DNA Methylation-Gold Kit [cat. no. D5006; ZYMO Research]. Upstream CpG Island of HSD17B6 gene was amplified using the primers listed below: forward, 5′-GATAGTATTGAGAGTAGGGAAAGAG-3′ and reverse, 5′-TTCTACCCACAAAAACRATAAC-3′. The PCR products from bisulfite-treated DNA were cloned into T- vector and then sequenced.

Luciferase reporter assay

A full length of the human HSD17B6 3′-untranslated region [449 bp] with the miR-31-5p targeting sequence was cloned downstream of the firefly luciferase gene in pGL3-control [Invitrogen] to construct pGL3-luc-HSD17B6. Then, the luciferase activity was determined as previously described [63].

Gene set enrichment analysis [GSEA]

Spearman’s correlation coefficient between the mRNA levels of each gene and HSD17B6 levels was computed and used to create a ranked gene list, which was supplied to pre-ranked analysis on HALLMARK-term database [h.all.v7.3.symbols.gmt] of Molecular Signatures Database [MSigDB] using GSEA software [v4.1.0]. Statistically significant pathways were screened based on the 0.35 as the cutoff criteria [64].

Statistical analysis

Data are presented as mean ± standard deviation. Differences between the two groups were evaluated using the two-sided Student’s t-test for normally distributed data or Mann–Whitney test for non-normally distributed data. F-test was used to compare the variance of two samples before Student’s t-test. Normality of data was determined by Kolmogorov–Smirnov test. Correlation analysis was performed using the Pearson’s test. Statistical analysis was performed with packages in R software or Prism 8.3.4 [GraphPad]. ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05. All data were analyzed blindly.

Data availability

The data that support the findings of the current study are available from the corresponding author on reasonable request.

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49.

    PubMed  Article  Google Scholar 

  2. Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nature 2018;553:446–54.

    CAS  PubMed  Article  Google Scholar 

  3. Denisenko TV, Budkevich IN, Zhivotovsky B. Cell death-based treatment of lung adenocarcinoma. Cell Death Dis. 2018;9:117.

    PubMed  PubMed Central  Article  Google Scholar 

  4. Copur MS, Crockett D, Gauchan D, Ramaekers R, Mleczko K. Molecular testing guideline for the selection of patients with lung cancer for targeted therapy. J Clin Oncol. 2018;36:2006.

    CAS  PubMed  Article  Google Scholar 

  5. Ali A, Goffin JR, Arnold A, Ellis PM. Survival of patients with non-small-cell lung cancer after a diagnosis of brain metastases. Curr Oncol. 2013;20:e300–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. Villano JL, Durbin EB, Normandeau C, Thakkar JP, Moirangthem V, Davis FG. Incidence of brain metastasis at initial presentation of lung cancer. Neuro Oncol. 2015;17:122–8.

    PubMed  Article  Google Scholar 

  7. Huang XF, Luu-The V. Gene structure, chromosomal localization and analysis of 3-ketosteroid reductase activity of the human 3[alpha->beta]-hydroxysteroid epimerase. Biochim Biophys Acta. 2001;1520:124–30.

    CAS  PubMed  Article  Google Scholar 

  8. Huang XF, Luu-The V. Molecular characterization of a first human 3[alpha->beta]-hydroxysteroid epimerase. J Biol Chem. 2000;275:29452–7.

    CAS  PubMed  Article  Google Scholar 

  9. Chan YX, Yeap BB. Dihydrotestosterone and cancer risk. Curr Opin Endocrinol Diabetes Obes. 2018;25:209–17.

    CAS  PubMed  Article  Google Scholar 

  10. Jernberg E, Thysell E, Bovinder Ylitalo E, Rudolfsson S, Crnalic S, Widmark A, et al. Characterization of prostate cancer bone metastases according to expression levels of steroidogenic enzymes and androgen receptor splice variants. PLoS ONE. 2013;8:e77407.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Ma Q, Xu Y, Liao H, Cai Y, Xu L, Xiao D, et al. Identification and validation of key genes associated with non-small-cell lung cancer. J Cell Physiol. 2019;234:22742–52.

    CAS  PubMed  Article  Google Scholar 

  12. Lv L, Zhao Y, Wei Q, Zhao Y, Yi Q. Downexpression of HSD17B6 correlates with clinical prognosis and tumor immune infiltrates in hepatocellular carcinoma. Cancer Cell Int. 2020;20:210.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45:W98–W102.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Chen J, Yang H, Teo ASM, Amer LB, Sherbaf FG, Tan CQ, et al. Genomic landscape of lung adenocarcinoma in East Asians. Nat Genet. 2020;52:177–86.

    CAS  PubMed  Article  Google Scholar 

  15. Lamouille S, Xu J, Derynck R. Molecular mechanisms of epithelial-mesenchymal transition. Nat Rev Mol Cell Biol. 2014;15:178–96.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Scheau C, Badarau IA, Costache R, Caruntu C, Mihai GL, Didilescu AC, et al. The role of matrix metalloproteinases in the epithelial-mesenchymal transition of hepatocellular carcinoma. Anal Cell Pathol. 2019;2019:9423907.

    Article  CAS  Google Scholar 

  17. Tubbs A, Nussenzweig A. Endogenous DNA damage as a source of genomic instability in cancer. Cell 2017;168:644–56.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Xu W, Yang Z, Lu N. A new role for the PI3K/Akt signaling pathway in the epithelial-mesenchymal transition. Cell Adh Migr. 2015;9:317–24.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. Fumarola C, Bonelli MA, Petronini PG, Alfieri RR. Targeting PI3K/AKT/mTOR pathway in non small cell lung cancer. Biochem Pharm. 2014;90:197–207.

    CAS  PubMed  Article  Google Scholar 

  20. Zhang X, Tang N, Hadden TJ, Rishi AK. Akt, FoxO and regulation of apoptosis. Biochim Biophys Acta. 2011;1813:1978–86.

    CAS  PubMed  Article  Google Scholar 

  21. Jo H, Mondal S, Tan D, Nagata E, Takizawa S, Sharma AK, et al. Small molecule-induced cytosolic activation of protein kinase Akt rescues ischemia-elicited neuronal death. Proc Natl Acad Sci USA. 2012;109:10581–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. Vinod SK, Hau E. Radiotherapy treatment for lung cancer: current status and future directions. Respirology 2020;25:61–71. Suppl 2

    PubMed  Article  Google Scholar 

  23. Lord CJ, Ashworth A. The DNA damage response and cancer therapy. Nature 2012;481:287–94.

    CAS  PubMed  Article  Google Scholar 

  24. Wang C, Lees-Miller SP. Detection and repair of ionizing radiation-induced DNA double strand breaks: new developments in nonhomologous end joining. Int J RadiatOncol Biol Phys. 2013;86:440–9.

    CAS  Article  Google Scholar 

  25. Karimi Roshan M, Soltani A, Soleimani A, Rezaie Kahkhaie K, Afshari AR, Soukhtanloo M. Role of AKT and mTOR signaling pathways in the induction of epithelial-mesenchymal transition [EMT] process. Biochimie 2019;165:229–34.

    CAS  PubMed  Article  Google Scholar 

  26. Wei R, Xiao Y, Song Y, Yuan H, Luo J, Xu W. FAT4 regulates the EMT and autophagy in colorectal cancer cells in part via the PI3K-AKT signaling axis. J Exp Clin Cancer Res. 2019;38:112.

    PubMed  PubMed Central  Article  Google Scholar 

  27. Cai LM, Lyu XM, Luo WR, Cui XF, Ye YF, Yuan CC, et al. EBV-miR-BART7-3p promotes the EMT and metastasis of nasopharyngeal carcinoma cells by suppressing the tumor suppressor PTEN. Oncogene 2015;34:2156–66.

    CAS  PubMed  Article  Google Scholar 

  28. Jiang H, Zhou Z, Jin S, Xu K, Zhang H, Xu J, et al. PRMT9 promotes hepatocellular carcinoma invasion and metastasis via activating PI3K/Akt/GSK-3beta/Snail signaling. Cancer Sci. 2018;109:1414–27.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. Perumal E, So Youn K, Sun S, Seung-Hyun J, Suji M, Jieying L, et al. PTEN inactivation induces epithelial-mesenchymal transition and metastasis by intranuclear translocation of beta-catenin and snail/slug in non-small cell lung carcinoma cells. Lung Cancer. 2019;130:25–34.

    PubMed  Article  Google Scholar 

  30. Chen X, Zhong L, Li X, Liu W, Zhao Y, Li J. Down-regulation of microRNA-31-5p inhibits proliferation and invasion of osteosarcoma cells through Wnt/beta-catenin signaling pathway by enhancing AXIN1. Exp Mol Pathol. 2019;108:32–41.

    CAS  PubMed  Article  Google Scholar 

  31. Peng H, Wang L, Su Q, Yi K, Du J, Wang Z. MiR-31-5p promotes the cell growth, migration and invasion of colorectal cancer cells by targeting NUMB. Biomed Pharmacother. 2019;109:208–16.

    CAS  PubMed  Article  Google Scholar 

  32. Moody HL, Lind MJ, Maher SG. MicroRNA-31 regulates chemosensitivity in malignant pleural mesothelioma. Mol Ther Nucleic Acids. 2017;8:317–29.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Yu F, Liang M, Huang Y, Wu W, Zheng B, Chen C. Hypoxic tumor-derived exosomal miR-31-5p promotes lung adenocarcinoma metastasis by negatively regulating SATB2-reversed EMT and activating MEK/ERK signaling. J. Exp Clin Cancer Res. 2021;40:179.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. Dai R, Yan D, Li J, Chen S, Liu Y, Chen R, et al. Activation of PKR/eIF2alpha signaling cascade is associated with dihydrotestosterone-induced cell cycle arrest and apoptosis in human liver cells. J Cell Biochem. 2012;113:1800–8.

    CAS  PubMed  Article  Google Scholar 

  35. Goldman MJ, Craft B, Hastie M, Repecka K, McDade F, Kamath A, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38:675–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA. 2001;98:13790–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Garber ME, Troyanskaya OG, Schluens K, Petersen S, Thaesler Z, Pacyna-Gengelbach M, et al. Diversity of gene expression in adenocarcinoma of the lung. Proc Natl Acad Sci USA. 2001;98:13784–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. Hou J, Aerts J, den Hamer B, van Ijcken W, den Bakker M, Riegman P, et al. Gene expression-based classification of non-small cell lung carcinomas and survival prediction. PLoS ONE. 2010;5:e10312.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  39. Landi MT, Dracheva T, Rotunno M, Figueroa JD, Liu H, Dasgupta A, et al. Gene expression signature of cigarette smoking and its role in lung adenocarcinoma development and survival. PLoS ONE. 2008;3:e1651.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  40. Okayama H, Kohno T, Ishii Y, Shimada Y, Shiraishi K, Iwakawa R, et al. Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res. 2012;72:100–11.

    CAS  PubMed  Article  Google Scholar 

  41. Selamat SA, Chung BS, Girard L, Zhang W, Zhang Y, Campan M, et al. Genome-scale analysis of DNA methylation in lung adenocarcinoma and integration with mRNA expression. Genome Res. 2012;22:1197–211.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. Stearman RS, Dwyer-Nield L, Zerbe L, Blaine SA, Chan Z, Bunn PA Jr., et al. Analysis of orthologous gene expression between human pulmonary adenocarcinoma and a carcinogen-induced murine model. Am J Pathol. 2005;167:1763–75.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. Ding L, Getz G, Wheeler DA, Mardis ER, McLellan MD, Cibulskis K, et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature 2008;455:1069–75.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Lee ES, Son DS, Kim SH, Lee J, Jo J, Han J, et al. Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression. Clin Cancer Res. 2008;14:7397–404.

    CAS  PubMed  Article  Google Scholar 

  45. Gillette MA, Satpathy S, Cao S, Dhanasekaran SM, Vasaikar SV, Krug K, et al. Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell 2020;182:200–25 e35.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. Vasaikar SV, Straub P, Wang J, Zhang B. LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res. 2018;46:D956–D63.

    CAS  PubMed  Article  Google Scholar 

  47. Der SD, Sykes J, Pintilie M, Zhu CQ, Strumpf D, Liu N, et al. Validation of a histology-independent prognostic gene signature for early-stage, non-small-cell lung cancer including stage IA patients. J Thorac Oncol. 2014;9:59–64.

    CAS  PubMed  Article  Google Scholar 

  48. Director’s Challenge Consortium for the Molecular Classification of Lung A, Shedden K, Taylor JM, Enkemann SA, Tsao MS, Yeatman TJ, et al. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med. 2008;14:822–7.

    Article  CAS  Google Scholar 

  49. Botling J, Edlund K, Lohr M, Hellwig B, Holmberg L, Lambe M, et al. Biomarker discovery in non-small cell lung cancer: integrating gene expression profiling, meta-analysis, and tissue microarray validation. Clin Cancer Res. 2013;19:194–204.

    CAS  PubMed  Article  Google Scholar 

  50. Girard L, Rodriguez-Canales J, Behrens C, Thompson DM, Botros IW, Tang H, et al. An expression signature as an aid to the histologic classification of non-small cell lung cancer. Clin Cancer Res. 2016;22:4880–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. Wilkerson MD, Yin X, Walter V, Zhao N, Cabanski CR, Hayward MC, et al. Differential pathogenesis of lung adenocarcinoma subtypes involving sequence mutations, copy number, chromosomal instability, and methylation. PLoS ONE. 2012;7:e36530.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. Tomida S, Takeuchi T, Shimada Y, Arima C, Matsuo K, Mitsudomi T, et al. Relapse-related molecular signature in lung adenocarcinomas identifies patients with dismal prognosis. J Clin Oncol. 2009;27:2793–9.

    CAS  PubMed  Article  Google Scholar 

  53. Tang H, Xiao G, Behrens C, Schiller J, Allen J, Chow CW, et al. A 12-gene set predicts survival benefits from adjuvant chemotherapy in non-small cell lung cancer patients. Clin Cancer Res. 2013;19:1577–86.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. Schabath MB, Welsh EA, Fulp WJ, Chen L, Teer JK, Thompson ZJ, et al. Differential association of STK11 and TP53 with KRAS mutation-associated gene expression, proliferation and immune surveillance in lung adenocarcinoma. Oncogene 2016;35:3209–16.

    CAS  PubMed  Article  Google Scholar 

  55. Mitchell KA, Zingone A, Toulabi L, Boeckelman J, Ryan BM. Comparative transcriptome profiling reveals coding and noncoding RNA differences in NSCLC from African Americans and European Americans. Clin Cancer Res. 2017;23:7412–25.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. Vucic EA, Thu KL, Pikor LA, Enfield KS, Yee J, English JC, et al. Smoking status impacts microRNA mediated prognosis and lung adenocarcinoma biology. BMC Cancer. 2014;14:778.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  57. Jungk C, Mock A, Exner J, Geisenberger C, Warta R, Capper D, et al. Spatial transcriptome analysis reveals Notch pathway-associated prognostic markers in IDH1 wild-type glioblastoma involving the subventricular zone. BMC Med. 2016;14:170.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  58. Lu S, Kong H, Hou Y, Ge D, Huang W, Ou J, et al. Two plasma microRNA panels for diagnosis and subtype discrimination of lung cancer. Lung Cancer. 2018;123:44–51.

    PubMed  Article  Google Scholar 

  59. Lv L, Li Y, Deng H, Zhang C, Pu Y, Qian L, et al. MiR-193a-3p promotes the multi-chemoresistance of bladder cancer by targeting the HOXC9 gene. Cancer Lett. 2015;357:105–13.

    CAS  PubMed  Article  Google Scholar 

  60. Luo W, Zhang H, Liang X, Xia R, Deng H, Yi Q, et al. DNA methylationregulated miR1555p depresses sensitivity of esophageal carcinoma cells to radiation and multiple chemotherapeutic drugs via suppression of MAP3K10. Oncol Rep. 2020;43:1692–704.

    CAS  PubMed  Google Scholar 

  61. Zaretsky JM, Garcia-Diaz A, Shin DS, Escuin-Ordinas H, Hugo W, Hu-Lieskovan S, et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N Engl J Med. 2016;375:819–29.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. Lv L, Yi Q, Yan Y, Chao F, Li M. SPNS2 downregulation induces EMT and promotes colorectal cancer metastasis via activating AKT signaling pathway. Front Oncol. 2021;11:682773.

    PubMed  PubMed Central  Article  Google Scholar 

  63. Lv L, Deng H, Li Y, Zhang C, Liu X, Liu Q, et al. The DNA methylation-regulated miR-193a-3p dictates the multi-chemoresistance of bladder cancer via repression of SRSF2/PLAU/HIC2 expression. Cell Death Dis. 2014;5:e1402.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–50.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [81602230 to LL, 81402327 to QYY, 11805228 to NC], the Provincial Natural Science Research Project of Anhui Colleges [KJ2020A0147 to QYY], and The Youth Fund of Anhui Cancer Hospital [2020YJQN005 to TT].

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Authors and Affiliations

  1. Department of Respiratory Oncology, Anhui Cancer Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230031, People’s Republic of China

    Tian Tian

  2. Department of Radiation Oncology, Anhui Cancer Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230031, People’s Republic of China

    Fu Hong

  3. The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China

    Zhiwen Wang & Jiaru Hu

  4. School of Basic Medical Sciences, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, People’s Republic of China

    Ni Chen & Qiyi Yi

  5. Department of Cancer Epigenetics Program, Anhui Cancer Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230031, People’s Republic of China

    Lei Lv

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  1. Tian Tian

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  2. Fu Hong

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  3. Zhiwen Wang

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  4. Jiaru Hu

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  5. Ni Chen

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  6. Lei Lv

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Contributions

QY and LL conceived and designed the study. QY and LL wrote the manuscript. TT, FH, and NC performed the experiments. WZ and HJ generated and analyzed the bioinformatics data. All authors read and approved the final manuscript.

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Tian, T., Hong, F., Wang, Z. et al. HSD17B6 downregulation predicts poor prognosis and drives tumor progression via activating Akt signaling pathway in lung adenocarcinoma. Cell Death Discov. 7, 341 [2021]. //doi.org/10.1038/s41420-021-00737-0

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  • Received: 10 August 2021

  • Revised: 23 October 2021

  • Accepted: 26 October 2021

  • Published: 08 November 2021

  • DOI: //doi.org/10.1038/s41420-021-00737-0

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