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image of Identification of Potential Biomarkers and Drugs for Papillary Thyroid Carcinoma Using Computational Analysis and Molecular Docking

Abstract

Background

Papillary thyroid carcinoma (PTC), the most common thyroid malignancy, presents with multiple variants. This study aimed to identify potential biomarkers and therapeutic candidates for PTC through computational analyses and molecular docking.

Methods

Gene expression data related to PTC were obtained from the TCGA-THCA and GEO datasets (GSE35570 and GSE33630) to identify differentially expressed genes (DEGs). Functional enrichment analysis was performed on the DEGs, followed by construction of a protein-protein interaction (PPI) network. Hub genes were identified using recursive feature elimination (RFE) and LASSO regression analyses. A nomogram incorporating these hub genes was developed, and its diagnostic performance was evaluated using receiver operating characteristic (ROC) curves. Furthermore, the relationship between hub genes and immune cell infiltration was investigated. Potential drug candidates targeting the hub genes were predicted and validated through molecular docking.

Results

Common DEGs across the three datasets were enriched in pathways such as ECM-receptor interaction, proteoglycans in cancer, and cell adhesion molecules. Significantly enriched GO terms included ‘binding,’ ‘receptor activity,’ ‘integral component of membrane,’ ‘cytoplasm,’ ‘cell adhesion,’ and ‘immune response.’ A PPI network was constructed by intersecting the common DEGs with PTC-related targets. Machine learning algorithms identified three hub genes: SRY-box transcription factor 4 (SOX4), cyclin D1 (CCND1), and lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1). These hub genes exhibited differential expression in PTC and were used to construct a reliable diagnostic model. Furthermore, molecular docking revealed stable binding between CCND1 and Tipifarnib, suggesting potential therapeutic relevance.

Discussion

While previous studies have applied bioinformatics and molecular docking in PTC research, this study uniquely integrates both approaches to identify the hub gene CCND1 and its potential targeting drug, Tipifarnib, as promising molecular markers and therapeutic candidates for PTC.

Conclusion

The hub gene CCND1 and its targeting drug candidate Tipifarnib may contribute to PTC treatment.

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2025-10-28
2025-12-16

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References

  1. Yao S. Zhang H. Papillary thyroid carcinoma with Hashimoto’s thyroiditis: Impact and correlation. Front. Endocrinol. 2025 16 1512417 10.3389/fendo.2025.1512417 40290312
    [Google Scholar]
  2. Du B. Wang S. Liu Y. Li Y. Li X. Metastatic papillary thyroid cancer mimicking hypopharyngeal carcinoma. Clin. Nucl. Med. 2023 48 1 90 91 10.1097/RLU.0000000000004468 36469069
    [Google Scholar]
  3. Xiao J. Zhang Y. Zhang M. Lan Y. Yan L. Luo Y. Tang J. Ultrasonography-guided radiofrequency ablation vs. surgery for the treatment of solitary T1bN0M0 papillary thyroid carcinoma: A comparative study. Clin. Endocrinol. 2021 94 4 684 691 10.1111/cen.14361 33128786
    [Google Scholar]
  4. Yu Y. Ouyang W. Huang Y. Huang H. Wang Z. Jia X. Huang Z. Lin R. Zhu Y. yalikun Y. Tan L. Li X. Zhao F. Chen Z. Li W. Liao J. Yao H. Long M. Artificial intelligence-based multi-modal multi-tasks analysis reveals tumor molecular heterogeneity, predicts preoperative lymph node metastasis and prognosis in papillary thyroid carcinoma: a retrospective study. Int. J. Surg. 2025 111 1 839 856 10.1097/JS9.0000000000001875 38990290
    [Google Scholar]
  5. Rogucki M. Sidorkiewicz I. Niemira M. Dzięcioł J.B. Buczyńska A. Adamska A. Siewko K. Kościuszko M. Maliszewska K. Wójcicka A. Supronik J. Szelachowska M. Reszeć J. Krętowski A.J. Popławska-Kita A. Expression profile and diagnostic significance of MicroRNAs in papillary thyroid cancer. Cancers 2022 14 11 2679 10.3390/cancers14112679 35681658
    [Google Scholar]
  6. Papaioannou M. Chorti A.G. Chatzikyriakidou A. Giannoulis K. Bakkar S. Papavramidis T.S. MicroRNAs in papillary thyroid cancer: What is new in diagnosis and treatment. Front. Oncol. 2022 11 755097 10.3389/fonc.2021.755097 35186709
    [Google Scholar]
  7. Ruiz-Pozo V.A. Cadena-Ullauri S. Guevara-Ramírez P. Paz-Cruz E. Tamayo-Trujillo R. Zambrano A.K. Differential microRNA expression for diagnosis and prognosis of papillary thyroid cancer. Front. Med. 2023 10 1139362 10.3389/fmed.2023.1139362 37089590
    [Google Scholar]
  8. You X. Zhang Y. Long Q. Liu Z. Feng Z. Zhang W. Teng Z. Zeng Y. Does single gene expression omnibus data mining analysis apply for only tumors and not mental illness? A preliminary study on bipolar disorder based on bioinformatics methodology. Medicine 2020 99 35 e21989 10.1097/MD.0000000000021989 32871949
    [Google Scholar]
  9. Mishra S. Singh S. Ashish A. Rai S. Singh R. Unveiling immune and signalling proteins in recurrent pregnancy loss: GEO2R analysis sheds light. Comput. Biol. Med. 2025 194 110535 10.1016/j.compbiomed.2025.110535 40494171
    [Google Scholar]
  10. Larriba E. de Juan Romero C. García-Martínez A. Quintanar T. Rodríguez-Lescure Á. Soto J.L. Saceda M. Martín-Nieto J. Barberá V.M. Identification of new targets for glioblastoma therapy based on a DNA expression microarray. Comput. Biol. Med. 2024 179 108833 10.1016/j.compbiomed.2024.108833 38981212
    [Google Scholar]
  11. Martins Rodrigues F. Terekhanova N.V. Imbach K.J. Clauser K.R. Esai Selvan M. Mendizabal I. Geffen Y. Akiyama Y. Maynard M. Yaron T.M. Li Y. Cao S. Storrs E.P. Gonda O.S. Gaite-Reguero A. Govindan A. Kawaler E.A. Wyczalkowski M.A. Klein R.J. Turhan B. Krug K. Mani D.R. Leprevost F.V. Nesvizhskii A.I. Carr S.A. Fenyö D. Gillette M.A. Colaprico A. Iavarone A. Robles A.I. Huang K. Kumar-Sinha C. Aguet F. Lazar A.J. Cantley L.C. Marigorta U.M. Gümüş Z.H. Bailey M.H. Getz G. Porta-Pardo E. Ding L. An E. Anurag M. Bavarva J. Birger C. Birrer M.J. Calinawan A.P. Ceccarelli M. Chan D.W. Chinnaiyan A.M. Cho H. Chowdhury S. Cieslik M.P. Zhou D.C. Day C. Domagalski M.J. Dou Y. Druker B.J. Edwards N. Ellis M.J. Foltz S.M. Francis A. Gonzalez Robles T.J. Gosline S.J.C. Hong R. Hostetter G. Hu Y. Hiltke T. Huang C. Huntsman E. Jaehnig E.J. Jewell S.D. Ji J. Jiang W. Katsnelson L. Ketchum K.A. Kolodziejczak I. Lei J.T. Liao Y. Lindgren C.M. Liu T. Ma W. McKerrow W. Newton C.J. Oldroyd R. Omenn G.S. Paulovich A.G. Petralia F. Reva B. Rodland K.D. Rodriguez H. Ruggles K.V. Rykunov D. Savage S.R. Schadt E.E. Schnaubelt M. Schraink T. Shi Z. Smith R.D. Song X. Song Y. Tan J. Thangudu R.R. Tignor N. Wang J.M. Wang P. Wang Y. Wen B. Wiznerowicz M. Yi X. Zhang B. Zhang H. Zhang X. Zhang Z. Heiman D.I. Johnson J.L. Wang L-B. Yao L. Thiagarajan M. Mesri M. Babur Ö. Pugliese P. Zhang Q. Payne S.H. Dhanasekaran S.M. Anand S. Satpathy S. Schürer S. Stathias V. Liang W-W. Liu W. Wu Y. Clinical proteomic tumor analysis consortium Precision proteogenomics reveals pan-cancer impact of germline variants. Cell 2025 188 9 2312 2335.e26 10.1016/j.cell.2025.03.026 40233739
    [Google Scholar]
  12. Matsuoka T. Yashiro M. Bioinformatics analysis and validation of potential markers associated with prediction and prognosis of gastric cancer. Int. J. Mol. Sci. 2024 25 11 5880 10.3390/ijms25115880 38892067
    [Google Scholar]
  13. Zeng X. Shi G. He Q. Zhu P. Screening and predicted value of potential biomarkers for breast cancer using bioinformatics analysis. Sci. Rep. 2021 11 1 20799 10.1038/s41598‑021‑00268‑9 34675265
    [Google Scholar]
  14. Liao M. Wang Z. Yao J. Xing H. Hao Y. Qiu B. Identification of potential biomarkers for papillary thyroid carcinoma by comprehensive bioinformatics analysis. Mol. Cell. Biochem. 2023 478 9 2111 2123 10.1007/s11010‑022‑04606‑x 36635603
    [Google Scholar]
  15. Wang R. Chen X. Yi D. Jiang C. Xu F. Qin J. Lee Y. Sang J. Shi X. Establishing a novel pyroptosis-related gene signature and predicting chemical drugs for papillary thyroid cancer. Curr. Pharm. Biotechnol. 2025 26 8 1232 1244 10.2174/0113892010325685241029113633 39492777
    [Google Scholar]
  16. Ritchie M.E. Phipson B. Wu D. Hu Y. Law C.W. Shi W. Smyth G.K. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 43 7 e47 10.1093/nar/gkv007 25605792
    [Google Scholar]
  17. Xie D. Huang L. Li C. Wu R. Zheng Z. Liu F. Cheng H. Identification of PANoptosis-related genes as prognostic indicators of thyroid cancer. Heliyon 2024 10 11 e31707 10.1016/j.heliyon.2024.e31707 38845990
    [Google Scholar]
  18. Song Z. Yu J. Wang M. Shen W. Wang C. Lu T. Shan G. Dong G. Wang Y. Zhao J. CHDTEPDB: Transcriptome expression profile database and interactive analysis platform for congenital heart disease. Congenit. Heart Dis. 2023 18 6 693 701 10.32604/chd.2024.048081
    [Google Scholar]
  19. Shi C. Hua M. Xu G. Therapeutic targets and signal transduction mechanisms of medicinal plant formula Gancao Xiexin decoction against ulcerative colitis: A network pharmacological study. Biocell 2023 47 6 1329 1344 10.32604/biocell.2023.028381
    [Google Scholar]
  20. Yu G. Wang L.G. Han Y. He Q.Y. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS 2012 16 5 284 287 10.1089/omi.2011.0118 22455463
    [Google Scholar]
  21. Szklarczyk D. Kirsch R. Koutrouli M. Nastou K. Mehryary F. Hachilif R. Gable A.L. Fang T. Doncheva N.T. Pyysalo S. Bork P. Jensen L.J. von Mering C. The STRING database in 2023: Protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023 51 D1 D638 D646 10.1093/nar/gkac1000 36370105
    [Google Scholar]
  22. Yan S. Han Z. Wang T. Wang A. Liu F. Yu S. Xu L. Shen H. Liu L. Lin Z. Na M. Exploring the immune-related molecular mechanisms underlying the comorbidity of temporal lobe Epilepsy and major depressive disorder through integrated data set analysis. Curr. Mol. Pharmacol. 2025 17 17 10.2174/0118761429380394250217093030 39976098
    [Google Scholar]
  23. Zhang Z. Zhao Y. Canes A. Steinberg D. Lyashevska O. AME big-data clinical trial collaborative group Predictive analytics with gradient boosting in clinical medicine. Ann. Transl. Med. 2019 7 7 152 10.21037/atm.2019.03.29 31157273
    [Google Scholar]
  24. Engebretsen S. Bohlin J. Statistical predictions with glmnet. Clin. Epigenetics 2019 11 1 123 10.1186/s13148‑019‑0730‑1 31443682
    [Google Scholar]
  25. Guan C. Ma F. Chang S. Zhang J. Interpretable machine learning models for predicting venous thromboembolism in the intensive care unit: An analysis based on data from 207 centers. Crit. Care 2023 27 1 406 10.1186/s13054‑023‑04683‑4 37875995
    [Google Scholar]
  26. Li M. Wei X. Zhang S.S. Li S. Chen S.H. Shi S.J. Zhou S.H. Sun D.Q. Zhao Q.Y. Xu Y. Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: Development and validation of a predictive nomogram model. BMC Pulm. Med. 2023 23 1 383 10.1186/s12890‑023‑02684‑1 37817172
    [Google Scholar]
  27. Robin X. Turck N. Hainard A. Tiberti N. Lisacek F. Sanchez J.C. Müller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011 12 1 77 10.1186/1471‑2105‑12‑77 21414208
    [Google Scholar]
  28. Craven K.E. Gökmen-Polar Y. Badve S.S. CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer. Sci. Rep. 2021 11 1 4691 10.1038/s41598‑021‑83913‑7 33633150
    [Google Scholar]
  29. Long S.W. Li S.H. Li J. He Y. Tan B. Jing H.H. Zheng W. Wu J. Identification of osteoporosis ferroptosis-related markers and potential therapeutic compounds based on bioinformatics methods and molecular docking technology. BMC Med. Genomics 2024 17 1 99 10.1186/s12920‑024‑01872‑0 38650009
    [Google Scholar]
  30. Kuleshov M.V. Jones M.R. Rouillard A.D. Fernandez N.F. Duan Q. Wang Z. Koplev S. Jenkins S.L. Jagodnik K.M. Lachmann A. McDermott M.G. Monteiro C.D. Gundersen G.W. Ma’ayan A. Enrichr: A comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016 44 W1 W90 W97 10.1093/nar/gkw377 27141961
    [Google Scholar]
  31. Yoo M. Shin J. Kim J. Ryall K.A. Lee K. Lee S. Jeon M. Kang J. Tan A.C. DSigDB: Drug signatures database for gene set analysis. Bioinformatics 2015 31 18 3069 3071 10.1093/bioinformatics/btv313 25990557
    [Google Scholar]
  32. Valathoor M.N. Venugopal S. Molecular docking and dynamic simulation approach to target penicillin- binding protein 1B (LpoB) of Salmonella typhimurium with Flavonoids. Curr. Pharm. Anal. 2024 20 8 849 862 10.2174/0115734129335204240919071902
    [Google Scholar]
  33. Raffaelli S.D. Shupak R.P. Winstead M. Hockaday J.J. Kim R.Y. A rare incidence of mandibular metastasis of papillary thyroid carcinoma: A case report and review of literature. J. Stomatol. Oral Maxillofac. Surg. 2023 124 6 101560 10.1016/j.jormas.2023.101560 37442344
    [Google Scholar]
  34. Yu J. Mai W. Cui Y. Kong L. Key genes and pathways predicted in papillary thyroid carcinoma based on bioinformatics analysis. J. Endocrinol. Invest. 2016 39 11 1285 1293 10.1007/s40618‑016‑0491‑z 27250077
    [Google Scholar]
  35. Petrini I. Cecchini R.L. Mascaró M. Ponzoni I. Carballido J.A. Papillary thyroid carcinoma: A thorough bioinformatic analysis of gene expression and clinical data. Genes 2023 14 6 1250 10.3390/genes14061250 37372430
    [Google Scholar]
  36. Liu Y. Gao S. Jin Y. Yang Y. Tai J. Wang S. Yang H. Chu P. Han S. Lu J. Ni X. Yu Y. Guo Y. Bioinformatics analysis to screen key genes in papillary thyroid carcinoma. Oncol. Lett. 2020 19 1 195 204 10.3892/ol.2019.11100 31897130
    [Google Scholar]
  37. Peng L. Zhang Z. Du W. Zhu J. Duan W. Proteomic and Phosphoproteomic analysis of thyroid papillary carcinoma: Identification of potential biomarkers for metastasis. J. Proteomics 2024 306 105260 10.1016/j.jprot.2024.105260 39029786
    [Google Scholar]
  38. Jiang H. He Y. Lan X. Xie X. Identification and validation of potential common biomarkers for papillary thyroid carcinoma and Hashimoto’s thyroiditis through bioinformatics analysis and machine learning. Sci. Rep. 2024 14 1 15578 10.1038/s41598‑024‑66162‑2 38971817
    [Google Scholar]
  39. Nguyen M. Panitch A. Proteoglycans and proteoglycan mimetics for tissue engineering. Am. J. Physiol. Cell Physiol. 2022 322 4 C754 C761 10.1152/ajpcell.00442.2021 35235426
    [Google Scholar]
  40. Espinoza-Sánchez N.A. Götte M. Role of cell surface proteoglycans in cancer immunotherapy. Semin. Cancer Biol. 2020 62 48 67 10.1016/j.semcancer.2019.07.012 31336150
    [Google Scholar]
  41. Kozlova I. Sytnyk V. Cell adhesion molecules as modulators of the epidermal growth factor receptor. Cells 2024 13 22 1919 10.3390/cells13221919 39594667
    [Google Scholar]
  42. Andriescu E.C. Căruntu I.D. Giuşcă S.E. Lozneanu L. Ciobanu Apostol D.G. Prognostic significance of cell-adhesion molecules in histological variants of papillary thyroid carcinoma. Rev. Roum. Morphol. Embryol. 2018 59 3 721 727 30534810
    [Google Scholar]
  43. Sainio A. Järveläinen H. Extracellular matrix-cell interactions: Focus on therapeutic applications. Cell. Signal. 2020 66 109487 10.1016/j.cellsig.2019.109487 31778739
    [Google Scholar]
  44. Sun T. Guan Q. Wang Y. Qian K. Sun W. Ji Q. Wu Y. Guo K. Xiang J. Identification of differentially expressed genes and signaling pathways in papillary thyroid cancer: A study based on integrated microarray and bioinformatics analysis. Gland Surg. 2021 10 2 629 644 10.21037/gs‑20‑673 33708546
    [Google Scholar]
  45. Zhang J. Sun P. Identification of GJC1 as a novel diagnostic marker for papillary thyroid carcinoma using weighted gene co-expression network analysis and machine learning algorithm. Discov. Oncol. 2025 16 1 339 10.1007/s12672‑025‑02137‑7 40095160
    [Google Scholar]
  46. Kilicarslan S. Hiz-Cicekliyurt M.M. Identification of potential biomarkers of papillary thyroid carcinoma. Endocrine 2024 87 2 758 771 10.1007/s12020‑024‑04068‑9 39400774
    [Google Scholar]
  47. Nasim S. Bichsel C. Dayneka S. Mannix R. Holm A. Vivero M. Alexandrescu S. Pinto A. Greene A.K. Ingber D.E. Bischoff J. MRC1 and LYVE1 expressing macrophages in vascular beds of GNAQ p.R183Q driven capillary malformations in Sturge Weber syndrome. Acta Neuropathol. Commun. 2024 12 1 47 10.1186/s40478‑024‑01757‑4 38532508
    [Google Scholar]
  48. Karinen S. Hujanen R. Salo T. Salem A. The prognostic influence of lymphatic endothelium–specific hyaluronan receptor 1 in cancer: A systematic review. Cancer Sci. 2022 113 1 17 27 10.1111/cas.15199 34775672
    [Google Scholar]
  49. Agarwal S. Gupta S. Raj R. Identification of potential targetable genes in papillary, follicular, and anaplastic thyroid carcinoma using bioinformatics analysis. Endocrine 2024 86 1 255 267 10.1007/s12020‑024‑03836‑x 38676768
    [Google Scholar]
  50. Xu J. Lin D.I. Oncogenic c-terminal cyclin D1 (CCND1) mutations are enriched in endometrioid endometrial adenocarcinomas. PLoS One 2018 13 7 e0199688 10.1371/journal.pone.0199688 29969496
    [Google Scholar]
  51. Liang W. Sun F. Identification of key genes of papillary thyroid cancer using integrated bioinformatics analysis. J. Endocrinol. Invest. 2018 41 10 1237 1245 10.1007/s40618‑018‑0859‑3 29520684
    [Google Scholar]
  52. Moreno C.S. SOX4: The unappreciated oncogene. Semin. Cancer Biol. 2020 67 Pt 1 57 64 10.1016/j.semcancer.2019.08.027 31445218
    [Google Scholar]
  53. Luo Q. Guo F. Fu Q. Sui G. hsa_circ_0001018 promotes papillary thyroid cancer by facilitating cell survival, invasion, G1/S cell cycle progression, and repressing cell apoptosis via crosstalk with miR-338-3p and SOX4. Mol. Ther. Nucleic Acids 2021 24 591 609 10.1016/j.omtn.2021.02.023 33898108
    [Google Scholar]
  54. Ho A.L. Brana I. Haddad R. Bauman J. Bible K. Oosting S. Wong D.J. Ahn M.J. Boni V. Even C. Fayette J. Flor M.J. Harrington K. Hong D.S. Kim S-B. Licitra L. Nixon I. Saba N.F. Hackenberg S. Specenier P. Worden F. Balsara B. Leoni M. Martell B. Scholz C. Gualberto A. Tipifarnib in head and neck squamous cell carcinoma with HRAS mutations. J. Clin. Oncol. 2021 39 17 1856 1864 10.1200/JCO.20.02903 33750196
    [Google Scholar]
  55. Hong D.S. Sebti S.M. Newman R.A. Blaskovich M.A. Ye L. Gagel R.F. Moulder S. Wheler J.J. Naing A. Tannir N.M. Ng C.S. Sherman S.I. El Naggar A.K. Khan R. Trent J. Wright J.J. Kurzrock R. Phase I trial of a combination of the multikinase inhibitor sorafenib and the farnesyltransferase inhibitor tipifarnib in advanced malignancies. Clin. Cancer Res. 2009 15 22 7061 7068 10.1158/1078‑0432.CCR‑09‑1241 19903778
    [Google Scholar]
  56. Santos-Beneit F. Raškevičius V. Skeberdis V.A. Bordel S. A metabolic modeling approach reveals promising therapeutic targets and antiviral drugs to combat COVID-19. Sci. Rep. 2021 11 1 11982 10.1038/s41598‑021‑91526‑3 34099831
    [Google Scholar]
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Identification of Potential Biomarkers and Drugs for Papillary Thyroid Carcinoma Using Computational Analysis and Molecular Docking
Bentham Science Publishers ; https://doi.org/10.2174/0109298673422926251009064453
/content/journals/cmc/10.2174/0109298673422926251009064453
/content/journals/cmc/10.2174/0109298673422926251009064453
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  • Article Type:     Research Article
Keywords: Computational analysis ; tipifarnib ; papillary thyroid carcinoma ; cyclin D1 ; differentially expressed genes ; molecular docking

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