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image of Functional Characterization and Prognostic Value of PIAS Family Genes in Liver Hepatocellular Carcinoma

Abstract

Introduction

Liver Hepatocellular Carcinoma (LIHC) poses a significant global health burden, necessitating comprehensive molecular investigations to elucidate its pathogenesis and identify potential biomarkers and therapeutic targets.

Methods

This study utilized Bioinformatics and detailed molecular experiments to delve into the expression profiling and epigenetic regulation of PIAS family genes in LIHC, shedding light on their diagnostic, prognostic, and therapeutic implications.

Results

Analysis of clinical specimens revealed a pronounced up-regulation of PIAS1, PIAS2, PIAS3, and PIAS4 genes in LIHC cell lines and tissue samples compared to normal controls, emphasizing their potential as diagnostic biomarkers for LIHC. Furthermore, promoter methylation profiling unveiled significant hypomethylation of PIAS1, PIAS2, PIAS3, and PIAS4 genes in LIHC samples, implicating epigenetic dysregulation in LIHC pathogenesis. Validation using independent TCGA datasets corroborated these findings, emphasizing the robustness of PIAS family genes as diagnostic markers for LIHC. Functional analyses revealed that PIAS1 knockdown in HepG2 cells significantly impaired proliferation and colony formation, while paradoxically enhancing cell migration. These results suggest a dual role for PIAS1 in promoting tumor growth while inhibiting metastatic potential. Prognostic modeling demonstrated the collective impact of dysregulated PIAS family genes on overall survival outcomes in LIHC patients, emphasizing their clinical relevance in prognostic assessments. Furthermore, correlation analysis with immune infiltrates and drug sensitivity profiling revealed intricate interactions and therapeutic implications of PIAS family genes in LIHC.

Discussion

The upregulation and hypomethylation of PIAS1–4 in LIHC suggest their role in tumor initiation and progression. PIAS1 knockdown impaired proliferation but increased migration, indicating a dual role in growth and metastasis. These findings align with poor patient survival linked to PIAS dysregulation. Their association with immune infiltration and drug sensitivity highlights potential for targeted therapies.

Conclusion

This study provides valuable insights into the multifaceted roles of PIAS family genes in LIHC pathogenesis and paves the way for personalized diagnostic and therapeutic interventions.

© 2025 Bentham Science Publishers
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2025-10-17
2025-12-13

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References

  1. Qu Y. Gong X. Zhao Z. Zhang Z. Zhang Q. Huang Y. Xie Q. Liu Y. Wei J. Du H. Establishment and validation of novel prognostic subtypes in hepatocellular carcinoma based on bile acid metabolism gene signatures using bulk and single-cell RNA-seq data. Int. J. Mol. Sci. 2024 25 2 919 10.3390/ijms25020919 38255993
    [Google Scholar]
  2. Zheng Z. Yang H. Shi Y. Zhou F. Liu L. Yan Z. Wang X. Identification of tumor antigens and anoikis-based molecular subtypes in the hepatocellular carcinoma immune microenvironment: Implications for mRNA vaccine development and precision treatment. J. Big Data 2023 10 1 129 10.1186/s40537‑023‑00803‑7
    [Google Scholar]
  3. Chen L. Wu L. Zhang L. Sun B. Wu W. Lei Y. Zhu L. Sun T. Liang B. Zhao H. Zheng C. Effect of metformin on hepatocellular carcinoma patients with type II diabetes receiving transarterial chemoembolization: A multicenter retrospective cohort study. Int. J. Surg. 2025 111 1 828 838 10.1097/JS9.0000000000001872 38935094
    [Google Scholar]
  4. He J. Feng X. Liu Y. Wang Y. Ge C. Liu S. Jiang Y. Graveoline attenuates D-GalN/LPS-induced acute liver injury via inhibition of JAK1/STAT3 signaling pathway. Biomed. Pharmacother. 2024 177 117163 10.1016/j.biopha.2024.117163 39018876
    [Google Scholar]
  5. Ding Z. Zhang L. Zhang Y. Yang J. Luo Y. Ge M. Yao W. Hei Z. Chen C. A supervised explainable machine learning model for perioperative neurocognitive disorder in liver-transplantation patients and external validation on the medical information mart for intensive care IV database: Retrospective study. J. Med. Internet Res. 2025 27 e55046 10.2196/55046 39813086
    [Google Scholar]
  6. Liu B. Ao Y. Liu C. Bai F. Zhou Z. Huang J. Wang Q. Creation of an innovative diagnostic framework for hepatocellular carcinoma employing bioinformatics techniques focused on senescence-related and pyroptosis-related genes. Front. Oncol. 2025 15 1485421 10.3389/fonc.2025.1485421 40018411
    [Google Scholar]
  7. Chen F. Zhang K. Wang M. He Z. Yu B. Wang X. Pan X. Luo Y. Xu S. Lau J.T.Y. Han C. Shi Y. Sun Y.E. Li S. Hu Y.P. VEGF‐FGF signaling activates quiescent CD63 + liver stem cells to proliferate and differentiate. Adv. Sci. 2024 11 33 2308711 10.1002/advs.202308711 38881531
    [Google Scholar]
  8. Hossen M.A. Reza M.S. Rana M.M. Hossen M.B. Shoaib M. Mollah M.N.H. Han C. Identification of most representative hub-genes for diagnosis, prognosis, and therapies of hepatocellular carcinoma. Chin Clin. Oncol. 2024 13 3 32 10.21037/cco‑23‑151 38984486
    [Google Scholar]
  9. Zheng P. Xu D. Cai Y. Zhu L. Xiao Q. Peng W. Chen B. A multi-omic analysis reveals that Gamabufotalin exerts anti-hepatocellular carcinoma effects by regulating amino acid metabolism through targeting STAMBPL1. Phytomedicine 2024 135 156094 10.1016/j.phymed.2024.156094 39348778
    [Google Scholar]
  10. Wu J. Yang S. Xu K. Ding C. Zhou Y. Fu X. Patterns and trends of liver cancer incidence rates in Eastern and Southeastern Asian countries (1983–2007) and predictions to 2030. Gastroenterology 2018 154 6 1719 1728.e5 10.1053/j.gastro.2018.01.033
    [Google Scholar]
  11. Lam Y.K. Investigation of TP53 for Loss-and Gain-of-function Roles in Liver Carcinogenesis. Doctor of Philosophy. The Chinese University of Hong Kong 2022
    [Google Scholar]
  12. Gao J. Han S. Gu J. Wu C. Mu X. The prognostic and therapeutic role of histone acetylation modification in LIHC development and progression. Medicina 2023 59 9 1682 10.3390/medicina59091682 37763801
    [Google Scholar]
  13. Nabavi S.M. Ahmed T. Nawaz M. Devi K.P. Balan D.J. Pittalà V. Argüelles-Castilla S. Testai L. Khan H. Sureda A. de Oliveira M.R. Vacca R.A. Xu S. Yousefi B. Curti V. Daglia M. Sobarzo-Sánchez E. Filosa R. Nabavi S.F. Majidinia M. Dehpour A.R. Shirooie S. Targeting STATs in neuroinflammation: The road less traveled! Pharmacol. Res. 2019 141 73 84 10.1016/j.phrs.2018.12.004 30550953
    [Google Scholar]
  14. Rytinki M.M. Kaikkonen S. Pehkonen P. Jääskeläinen T. Palvimo J.J. PIAS proteins: Pleiotropic interactors associated with SUMO. Cell. Mol. Life Sci. 2009 66 18 3029 3041 10.1007/s00018‑009‑0061‑z 19526197
    [Google Scholar]
  15. Li C. Boutet A. Pascariu C.M. Nelson T. Courcelles M. Wu Z. Comtois-Marotte S. Emery G. Thibault P. SUMO proteomics analyses identify protein inhibitor of activated STAT-mediated regulatory networks involved in cell cycle and cell proliferation. J. Proteome Res. 2023 22 3 812 825 10.1021/acs.jproteome.2c00557 36723483
    [Google Scholar]
  16. Kim J.H. Dhanasekaran S.M. Mehra R. Tomlins S.A. Gu W. Yu J. Kumar-Sinha C. Cao X. Dash A. Wang L. Ghosh D. Shedden K. Montie J.E. Rubin M.A. Pienta K.J. Shah R.B. Chinnaiyan A.M. Integrative analysis of genomic aberrations associated with prostate cancer progression. Cancer Res. 2007 67 17 8229 8239 10.1158/0008‑5472.CAN‑07‑1297 17804737
    [Google Scholar]
  17. De Marzo A.M. Meeker A.K. Zha S. Luo J. Nakayama M. Platz E.A. Isaacs W.B. Nelson W.G. Human prostate cancer precursors and pathobiology. Urology 2003 62 5 Suppl. 1 55 62 10.1016/j.urology.2003.09.053 14607218
    [Google Scholar]
  18. Wu M. Song D. Li H. Yang Y. Ma X. Deng S. Ren C. Shu X. Negative regulators of STAT3 signaling pathway in cancers. Cancer Manag. Res. 2019 11 4957 4969 10.2147/CMAR.S206175 31213912
    [Google Scholar]
  19. Zhang Q. Zhang J. Lan T. He J. Lei B. Wang H. Integrative analysis revealed a correlation of PIAS family genes expression with prognosis, immunomodulation and chemotherapy. Eur. J. Med. Res. 2024 29 10.1186/s40001‑024‑01795‑7
    [Google Scholar]
  20. Hou Y. Li X. Li Q. Xu J. Yang H. Xue M. Niu G. Zhuo S. Mu K. Wu G. Li X. Wang H. Zhu J. Zhuang T. STAT 1 facilitates oestrogen receptor α transcription and stimulates breast cancer cell proliferation. J. Cell. Mol. Med. 2018 22 12 6077 6086 10.1111/jcmm.13882 30334368
    [Google Scholar]
  21. Coppola D. Parikh V. Boulware D. Blanck G. Substantially reduced expression of PIAS1 is associated with colon cancer development. J. Cancer Res. Clin. Oncol. 2009 135 9 1287 1291 10.1007/s00432‑009‑0570‑z 19288270
    [Google Scholar]
  22. Ye W. Zhao Y. Wang Y. Wang Y. Zhang H. Wang F. Chen W. Farnesoid X receptor attenuates the tumorigenicity of liver cancer stem cells by inhibiting STAT3 phosphorylation. Int. J. Mol. Sci. 2025 26 3 1122 10.3390/ijms26031122 39940889
    [Google Scholar]
  23. Chen Y. Peng W. Tao Q. Li S. Wu Z. Zhou Y. Xu Q. Shu Y. Xu Y. Shao M. Chen M. Shi Y. Increased small ubiquitin-like modifier-activating enzyme SAE1 promotes hepatocellular carcinoma by enhancing mTOR SUMOylation. Lab. Invest. 2023 103 1 100011 10.1016/j.labinv.2022.100011 36748193
    [Google Scholar]
  24. Shu H. Chen X. Zhao J. Li P. Sun Z. PIAS family gene expression: Implications for prognosis, immunomodulation, and chemotherapy response. Am. J. Transl. Res. 2024 16 11 6346 6364 10.62347/JRDP4018 39678595
    [Google Scholar]
  25. Shuai K. Liu B. Regulation of gene-activation pathways by PIAS proteins in the immune system. Nat. Rev. Immunol. 2005 5 8 593 605 10.1038/nri1667 16056253
    [Google Scholar]
  26. Gavabari F.A. Rastegari-Pouyani M. Afshar S. Mazdeh M. Bahramian A. Shahidi S. Talebi-ghane E. Chalabi M. Eftekharian M.M. Expression levels of protein inhibitor of activated STAT (PIAS) family genes in Parkinson’s disease patients: results from a case-control study. Acta Neurol. Belg. 2025 125 3 727 735 10.1007/s13760‑025‑02752‑9 40016540
    [Google Scholar]
  27. Qian F.C. Zhou L.W. Li Y.Y. Yu Z.M. Li L.D. Wang Y.Z. Xu M.C. Wang Q.Y. Li C.Q. SEanalysis 2.0: A comprehensive super-enhancer regulatory network analysis tool for human and mouse. Nucleic Acids Res. 2023 51 W1 W520 W527 10.1093/nar/gkad408 37194711
    [Google Scholar]
  28. Yang Z. Liu X. Xu H. Teschendorff A.E. Xu L. Li J. Fu M. Liu J. Zhou H. Wang Y. Zhang L. He Y. Lv K. Yang H. Integrative analysis of genomic and epigenomic regulation reveals miRNA mediated tumor heterogeneity and immune evasion in lower grade glioma. Commun. Biol. 2024 7 1 824 10.1038/s42003‑024‑06488‑9 38971948
    [Google Scholar]
  29. Jiang Y. Chen R. Xu S. Ding Y. Zhang M. Bao M. He B. Li S. Endocrine and metabolic factors and the risk of idiopathic pulmonary fibrosis: A Mendelian randomization study. Front. Endocrinol. 2024 14 1321576 10.3389/fendo.2023.1321576 38260151
    [Google Scholar]
  30. Wang Y. Li H. Fan R. Lv H. Hua S. Xie H. Tang T. Luo J. Xia Z. The effects of ferulic acid on the pharmacokinetics of warfarin in rats after biliary drainage. Drug Des. Devel. Ther. 2016 10 2173 2180 10.2147/DDDT.S107917 27462142
    [Google Scholar]
  31. Yi-wen Z. Mei-hua B. Xiao-ya L. Yu C. Jing Y. Hong-hao Z. Effects of oridonin on hepatic cytochrome P450 expression and activities in PXR-humanized mice. Biol. Pharm. Bull. 2018 41 5 707 712 10.1248/bpb.b17‑00882 29709908
    [Google Scholar]
  32. Santos K.K. Daniel I.W. Delabio L.C. Costa M.A. Dutra J.P. Ruginsk B.E. Effective mixed-type tissue crusher and simultaneous isolation of RNA, DNA, and protein from solid tissues using a trizol-based method. Multidiscip. Sci. J. 2025 8 1 3 10.3390/j8010003
    [Google Scholar]
  33. Wani S. Mehraj I. Iralu N. Meghanath D. Hamid A. Extraction of high-quality genomic RNA using modified trizol methods. Detection of Plant. Viruses. New York Humana 2025 77 80 10.1007/978‑1‑0716‑4390‑7_14
    [Google Scholar]
  34. Zhang Y. Gao C. Luo J. Khan A. Salem-Bekhit M.M. Salem M.M. Qi Z. Jiang B. RETRACTED: Deciphering the role of wound healing genes in skin cutaneous melanoma: Insights into expression, methylation, mutations, and therapeutic implications. Int. Wound J. 2024 21 4 e14807 10.1111/iwj.14807 38591163
    [Google Scholar]
  35. Tang G. Cho M. Wang X. Onco DB. An interactive online database for analysis of gene expression and viral infection in cancer. Nucleic Acids Res. 2022 50 D1 D1334 D1339 10.1093/nar/gkab970 34718715
    [Google Scholar]
  36. Tang Z. Kang B. Li C. Chen T. Zhang Z. GEPIA2: An enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019 47 W1 W556 W560 10.1093/nar/gkz430 31114875
    [Google Scholar]
  37. Cerami E. Gao J. Dogrusoz U. Gross B.E. Sumer S.O. Aksoy B.A. Jacobsen A. Byrne C.J. Heuer M.L. Larsson E. Antipin Y. Reva B. Goldberg A.P. Sander C. Schultz N. The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012 2 5 401 404 10.1158/2159‑8290.CD‑12‑0095 22588877
    [Google Scholar]
  38. Lánczky A. Győrffy B. Web-based survival analysis tool tailored for medical research (KMplot): Development and implementation. J. Med. Internet Res. 2021 23 7 e27633 10.2196/27633 34309564
    [Google Scholar]
  39. Xu Y. Wang X. Huang Y. Ye D. Chi P. A LASSO-based survival prediction model for patients with synchronous colorectal carcinomas based on SEER. Transl. Cancer Res. 2022 11 8 2795 2809 10.21037/tcr‑20‑1860 36093555
    [Google Scholar]
  40. Sherman B.T. Hao M. Qiu J. Jiao X. Baseler M.W. Lane H.C. DAVID: A web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022 50 W1 W216 W221 10.1093/nar/gkac194
    [Google Scholar]
  41. Liu C.J. Hu F.F. Xie G.Y. Miao Y.R. Li X.W. Zeng Y. Guo A.Y. GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels. Brief. Bioinform. 2023 24 1 bbac558 10.1093/bib/bbac558 36549921
    [Google Scholar]
  42. Chen Y. Wang X. miRDB: An online database for prediction of functional microRNA targets. Nucleic Acids Res. 2020 48 D1 D127 D131 10.1093/nar/gkz757 31504780
    [Google Scholar]
  43. Sticht C. De La Torre C. Parveen A. Gretz N. miRWalk: An online resource for prediction of microRNA binding sites. PLoS One 2018 13 10 e0206239 10.1371/journal.pone.0206239 30335862
    [Google Scholar]
  44. Quillet A. Saad C. Ferry G. Anouar Y. Vergne N. Lecroq T. Dubessy C. Improving bioinformatics prediction of microRNA targets by ranks aggregation. Front. Genet. 2020 10 1330 1330 10.3389/fgene.2019.01330 32047509
    [Google Scholar]
  45. Kim H.Y. Statistical notes for clinical researchers: Chi-squared test and Fisher’s exact test. Restor. Dent. Endod 2017 42 2 152 155 10.5395/rde.2017.42.2.152 28503482
    [Google Scholar]
  46. Giraud J. Chalopin D. Blanc J.F. Saleh M. Hepatocellular carcinoma immune landscape and the potential of immunotherapies. Front. Immunol. 2021 12 655697 10.3389/fimmu.2021.655697 33815418
    [Google Scholar]
  47. Ju M. Jiang L. Wei Q. Yu L. Chen L. Wang Y. Hu B. Qian P. Zhang M. Zhou C. Li Z. Wei M. Zhao L. Han J. A immune-related signature associated with TME can serve as a potential biomarker for survival and sorafenib resistance in liver cancer. OncoTargets Ther. 2021 14 5065 5083 10.2147/OTT.S326784 34707365
    [Google Scholar]
  48. Huang P.S. Wang L.Y. Wang Y.W. Tsai M.M. Lin T.K. Liao C.J. Yeh C.T. Lin K.H. Evaluation and application of drug resistance by biomarkers in the clinical treatment of liver cancer. Cells 2023 12 6 869 10.3390/cells12060869 36980210
    [Google Scholar]
  49. Liang L.X. Liang X. Zeng Y. Wang F. Yu X.K. Establishment and validation of a nomogram for predicting esophagogastric variceal bleeding in patients with liver cirrhosis. World J. Gastroenterol. 2025 31 9 102714 10.3748/wjg.v31.i9.102714 40061586
    [Google Scholar]
  50. Sun R. Xu Y. Zhang H. Yang Q. Wang K. Shi Y. Wang Z. Mechanistic modeling of gene regulation and metabolism identifies potential targets for hepatocellular carcinoma. Front. Genet. 2020 11 595242 10.3389/fgene.2020.595242 33424926
    [Google Scholar]
  51. Cong S. Bai S. Bi Y. Wang Y. Jin S. He H. Construction of molecular typing in LIHC microenvironment based on lipid metabolism-related genes. Am. J. Cancer Res. 2023 13 7 2814 2840 [PMID: 37559997
    [Google Scholar]
  52. Brito-Rocha T. Constâncio V. Henrique R. Jerónimo C. Shifting the cancer screening paradigm: the rising potential of blood-based multi-cancer early detection tests. Cells 2023 12 6 935 10.3390/cells12060935 36980276
    [Google Scholar]
  53. Zhang Y. Liu Z. Li X. Liu L. Wang L. Han X. Li Z. Comprehensive molecular analyses of a six-gene signature for predicting late recurrence of hepatocellular carcinoma. Front. Oncol. 2021 11 732447 10.3389/fonc.2021.732447 34568069
    [Google Scholar]
  54. Kuang T. Ma W. Zhang J. Yu J. Deng W. Dong K. Wang W. Construction of a Nomogram to Predict Overall Survival in Patients with Early-Onset Hepatocellular Carcinoma: A Retrospective Cohort Study. Cancers (Basel) 2023 15 22 5310 10.3390/cancers15225310 38001570
    [Google Scholar]
  55. Pettas T. Lachanoudi S. Karageorgos F.F. Ziogas I.A. Fylaktou A. Papalois V. Katsanos G. Antoniadis N. Tsoulfas G. Immunotherapy and liver transplantation for hepatocellular carcinoma: Current and future challenges. World J. Transplant. 2025 15 2 98509 10.5500/wjt.v15.i2.98509 40535484
    [Google Scholar]
  56. Lin H.Y. Jeon A.J. Chen K. Lee C.J.M. Wu L. Chong S.L. Anene-Nzelu C.G. Foo R.S-Y. Chow P.K-H. The epigenetic basis of hepatocellular carcinoma – mechanisms and potential directions for biomarkers and therapeutics. Br. J. Cancer 2025 132 10 869 887 10.1038/s41416‑025‑02969‑8
    [Google Scholar]
  57. Ha S. Wong V.W.S. Zhang X. Yu J. Interplay between gut microbiome, host genetic and epigenetic modifications in MASLD and MASLD-related hepatocellular carcinoma. Gut 2025 74 1 141 152 10.1136/gutjnl‑2024‑332398 38950910
    [Google Scholar]
  58. Sial N. Ahmad M. Hussain M.S. Iqbal M.J. Hameed Y. Khan M. Abbas M. Asif R. Rehman J.U. Atif M. Khan M.R. Hameed Z. Saeed H. Tanveer R. Saeed S. Sharif A. Asif H.M. CTHRC1 expression is a novel shared diagnostic and prognostic biomarker of survival in six different human cancer subtypes. Sci. Rep. 2021 11 1 19873 10.1038/s41598‑021‑99321‑w 34615943
    [Google Scholar]
  59. Sial N. Saeed S. Ahmad M. Hameed Y. Rehman A. Abbas M. Asif R. Ahmed H. Hussain M.S. Rehman J.U. Atif M. Khan M.R. Multi-omics analysis identified TMED2 as a shared potential biomarker in six subtypes of human cancer. Int. J. Gen. Med. 2021 14 7025 7042 10.2147/IJGM.S327367 34707394
    [Google Scholar]
  60. Usman M. Hameed Y. Ahmad M. Iqbal M.J. Maryam A. Mazhar A. Naz S. Tanveer R. Saeed H. Bint-e-Fatima; Ashraf, A.; Hadi, A.; Hameed, Z.; Tariq, E.; Aslam, A.S. SHMT2 is associated with tumor purity, CD8+ T immune cells infiltration, and a novel therapeutic target in four different human cancers. Curr. Mol. Med. 2023 23 2 161 176 10.2174/1566524022666220112142409 35023455
    [Google Scholar]
  61. Fu Q. Yang F. Xiang T. Huai G. Yang X. Wei L. Yang H. Deng S. A novel microRNA signature predicts survival in liver hepatocellular carcinoma after hepatectomy. Sci. Rep. 2018 8 1 7933 10.1038/s41598‑018‑26374‑9 29785036
    [Google Scholar]
  62. Wang Z. Pan L. Guo D. Luo X. Tang J. Yang W. Zhang Y. Luo A. Gu Y. Pan Y. A novel five‐gene signature predicts overall survival of patients with hepatocellular carcinoma. Cancer Med. 2021 10 11 3808 3821 10.1002/cam4.3900 33934539
    [Google Scholar]
  63. Loh C.Y. Arya A. Naema A.F. Wong W.F. Sethi G. Looi C.Y. Signal transducer and activator of transcription (STATs) proteins in cancer and inflammation: functions and therapeutic implication. Front. Oncol. 2019 9 48 10.3389/fonc.2019.00048 30847297
    [Google Scholar]
  64. Brzezianska E. Domanska D. The JAK/STAT protein activation–role in cancer development and targeted therapy. Curr. Signal Transduct. Ther. 2012 7 3 187 201 10.2174/157436212802481619
    [Google Scholar]
  65. Dünnebier T. Bermejo J.L. Haas S. Fischer H.P. Pierl C.B. Justenhoven C. Brauch H. Baisch C. Gilbert M. Harth V. Spickenheuer A. Rabstein S. Pesch B. Brüning T. Ko Y.D. Hamann U. Polymorphisms in the UBC9 and PIAS3 genes of the SUMO-conjugating system and breast cancer risk. Breast Cancer Res. Treat. 2010 121 1 185 194 10.1007/s10549‑009‑0530‑y 19760037
    [Google Scholar]
  66. Gross M. Liu B. Tan J. French F.S. Carey M. Shuai K. Distinct effects of PIAS proteins on androgen-mediated gene activation in prostate cancer cells. Oncogene 2001 20 29 3880 3887 10.1038/sj.onc.1204489 11439351
    [Google Scholar]
  67. Polimeno L. Francavilla A. Piscitelli D. Fiore M.G. Polimeno R. Topi S. Haxhirexha K. Ballini A. Daniele A. Santacroce L. The role of PIAS3, p-STAT3 and ALR in colorectal cancer: New translational molecular features for an old disease. Eur. Rev. Med. Pharmacol. Sci. 2020 24 20 10496 10511 [PMID: 33155205
    [Google Scholar]
  68. Li X. Rasul A. Sharif F. Hassan M. PIAS family in cancer: From basic mechanisms to clinical applications. Front. Oncol. 2024 14 1376633 10.3389/fonc.2024.1376633 38590645
    [Google Scholar]
  69. Su S. Zhang Y. Liu P. Roles of ubiquitination and SUMOylation in DNA damage response. Curr. Issues Mol. Biol. 2020 35 1 59 84 10.21775/cimb.035.059 31422933
    [Google Scholar]
  70. Siveen K.S. Sikka S. Surana R. Dai X. Zhang J. Kumar A.P. Tan B.K. Sethi G. Bishayee A. Targeting the STAT3 signaling pathway in cancer: role of synthetic and natural inhibitors. Biochim. Biophys. Acta 2014 1845 2 136 154 [PMID: 24388873
    [Google Scholar]
  71. Nikitakis N. Siavash H. Sauk J. Targeting the STAT pathway in head and neck cancer: recent advances and future prospects. Curr. Cancer Drug Targets 2004 4 8 637 651 10.2174/1568009043332736 15578920
    [Google Scholar]
  72. Kadye R. Stoffels M. Fanucci S. Mbanxa S. Prinsloo E. STAT3 of addiction: adipose tissue, adipocytokine signalling and STAT3 as mediators of metabolic remodelling in the tumour microenvironment. Cells 2020 9 4 1043 10.3390/cells9041043 32331320
    [Google Scholar]
  73. Saydmohammed M. Joseph D. Syed V. Curcumin suppresses constitutive activation of STAT‐3 by up‐regulating protein inhibitor of activated STAT‐3 (PIAS‐3) in ovarian and endometrial cancer cells. J. Cell. Biochem. 2010 110 2 447 456 10.1002/jcb.22558 20235152
    [Google Scholar]
  74. Toropainen S. Malinen M. Kaikkonen S. Rytinki M. Jääskeläinen T. Sahu B. Jänne O.A. Palvimo J.J. SUMO ligase PIAS1 functions as a target gene selective androgen receptor coregulator on prostate cancer cell chromatin. Nucleic Acids Res. 2015 43 2 848 861 10.1093/nar/gku1375 25552417
    [Google Scholar]
  75. Huang L. Irshad S. Sultana U. Ali S. Jamil A. Zubair A. Sultan R. Abdel-Maksoud M.A. Mubarak A. Almunqedhi B.M. Almanaa T.N. Malik A. Alamri A. Kodous A.S. Mares M. Zaky M.Y. Saba Sajjad S. Hameed Y. Pan-cancer analysis of HS6ST2: Associations with prognosis, tumor immunity, and drug resistance. Am. J. Transl. Res. 2024 16 3 873 888 10.62347/NCPH5416 38586106
    [Google Scholar]
  76. Hameed Y. Decoding the significant diagnostic and prognostic importance of maternal embryonic leucine zipper kinase in human cancers through deep integrative analyses. J. Cancer Res. Ther. 2023 19 7 1852 1864 10.4103/jcrt.jcrt_1902_21 38376289
    [Google Scholar]
  77. Jiang F. Ahmad S. kanwal, S.; Hameed, Y.; Tang, Q. Key wound healing genes as diagnostic biomarkers and therapeutic targets in uterine corpus endometrial carcinoma: An integrated in silico and in vitro study. Hereditas 2025 162 1 5 10.1186/s41065‑025‑00369‑9 39833941
    [Google Scholar]
  78. Phé V. Cussenot O. Rouprêt M. Methylated genes as potential biomarkers in prostate cancer. BJU Int. 2010 105 10 1364 1370 10.1111/j.1464‑410X.2009.09167.x 20067451
    [Google Scholar]
  79. Abdel-Maksoud M.A. Ullah S. Nadeem A. Shaikh A. Zia M.K. Zakri A.M. Almanaa T.N. Alfuraydi A.A. Mubarak A. Hameed Y. Unlocking the diagnostic, prognostic roles, and immune implications of BAX gene expression in pan-cancer analysis. Am. J. Transl. Res. 2024 16 1 63 74 10.62347/TWOY1681 38322551
    [Google Scholar]
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Functional Characterization and Prognostic Value of PIAS Family Genes in Liver Hepatocellular Carcinoma
Bentham Science Publishers ; https://doi.org/10.2174/0115680096390042250929110047
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  • Article Type:     Research Article
Keywords: HCV ; hepatocellular carcinoma ; HepG2 cells ; LIHC ; PIAS1 ; TCGA datasets ; HBV

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