Acta NaturaeActa NaturaeActa Naturae2075-8251Acta Naturae Ltd2511210.32607/actanaturae.25112Research ArticlesЭкспериментальные статьиResearch ArticleA DNA Replication Stress-Based Prognostic Model for Lung AdenocarcinomaA DNA Replication Stress-Based Prognostic Model for Lung AdenocarcinomaShiShuai
China
susiec1pr@outlook.comWenGuangrui
China
rillarul7@outlook.comLeiChengqiang
China
caspessinnp3@outlook.comChangJianbo
China
angilapape6i@outlook.comYinXiaoqiang
China
kylekoschoqwg@outlook.comLiuXiangyong
China
russpyh9hilty@outlook.comHuangShouling
China
cqslhuang@163.comThe People’s Hospital of Dazu District301020231531001101807202331072023Copyright ©; 2023, Shi S., Wen G., Lei C., Chang J., Yin X., Liu X., Huang S.Copyright ©; 2023, Shi S., Wen G., Lei C., Chang J., Yin X., Liu X., Huang S.2023Shi S., Wen G., Lei C., Chang J., Yin X., Liu X., Huang S.Shi S., Wen G., Lei C., Chang J., Yin X., Liu X., Huang S.https://creativecommons.org/licenses/by/4.0https://actanaturae.ru/2075-8251/article/view/25112

Tumor cells endure continuous DNA replication stress, which opens the way to cancer development. Despite previous research, the prognostic implications of DNA replication stress on lung adenocarcinoma (LUAD) have yet to be investigated. Here, we aimed to investigate the potential of DNA replication stress-related genes (DNARSs) in predicting the prognosis of individuals with LUAD. Differentially expressed genes (DEGs) originated from the TCGA-LUAD dataset, and we constructed a 10-gene LUAD prognostic model based on DNARSs-related DEGs (DRSDs) using Cox regression analysis. The receiver operating characteristic (ROC) curve demonstrated excellent predictive capability for the LUAD prognostic model, while the Kaplan-Meier survival curve indicated a poorer prognosis in a high-risk (HR) group. Combined with clinical data, the Riskscore was found to be an independent predictor of LUAD prognosis. By incorporating Riskscore and clinical data, we developed a nomogram that demonstrated a capacity to predict overall survival and exhibited clinical utility, which was validated through the calibration curve, ROC curve, and decision curve analysis curve tests, confirming its effectiveness in prognostic evaluation. Immune analysis revealed that individuals belonging to the low-risk (LR) group exhibited a greater abundance of immune cell infiltration and higher levels of immune function. We calculated the immunopheno score and TIDE scores and tested them on the IMvigor210 and GSE78220 cohorts and found that individuals categorized in the LR group exhibited a higher likelihood of deriving therapeutic benefits from immunotherapy intervention. Additionally, we predicted that patients classified in the HR group would demonstrate enhanced sensitivity to Docetaxel using anti-tumor drugs. To summarize, we successfully developed and validated a prognostic model for LUAD by incorporating DNA replication stress as a key factor.

Tumor cells endure continuous DNA replication stress, which opens the way to cancer development. Despite previous research, the prognostic implications of DNA replication stress on lung adenocarcinoma (LUAD) have yet to be investigated. Here, we aimed to investigate the potential of DNA replication stress-related genes (DNARSs) in predicting the prognosis of individuals with LUAD. Differentially expressed genes (DEGs) originated from the TCGA-LUAD dataset, and we constructed a 10-gene LUAD prognostic model based on DNARSs-related DEGs (DRSDs) using Cox regression analysis. The receiver operating characteristic (ROC) curve demonstrated excellent predictive capability for the LUAD prognostic model, while the Kaplan-Meier survival curve indicated a poorer prognosis in a high-risk (HR) group. Combined with clinical data, the Riskscore was found to be an independent predictor of LUAD prognosis. By incorporating Riskscore and clinical data, we developed a nomogram that demonstrated a capacity to predict overall survival and exhibited clinical utility, which was validated through the calibration curve, ROC curve, and decision curve analysis curve tests, confirming its effectiveness in prognostic evaluation. Immune analysis revealed that individuals belonging to the low-risk (LR) group exhibited a greater abundance of immune cell infiltration and higher levels of immune function. We calculated the immunopheno score and TIDE scores and tested them on the IMvigor210 and GSE78220 cohorts and found that individuals categorized in the LR group exhibited a higher likelihood of deriving therapeutic benefits from immunotherapy intervention. Additionally, we predicted that patients classified in the HR group would demonstrate enhanced sensitivity to Docetaxel using anti-tumor drugs. To summarize, we successfully developed and validated a prognostic model for LUAD by incorporating DNA replication stress as a key factor.

DNA replication stresslung adenocarcinomaprognostic modelimmunotherapy responseanti-tumor drug predictionDNA replication stresslung adenocarcinomaprognostic modelimmunotherapy responseanti-tumor drug predictionSung H., Ferlay J., Siegel R.L., Laversanne M., Soerjomataram I., Jemal A., Bray F. // CA Cancer J. Clin. 2021. V. 71. № 3. P. 209–249.Chen J.W., Dhahbi J. // Sci. Rep. 2021. V. 11. № 1. P. 13323.Hao C.C., Xu C.Y., Zhao X.Y., Luo J.N., Wang G., Zhao L.H., Ge X., Ge X.F. // J. Exp. Clin. Cancer Res. 2020. V. 39. № 1. P. 256.Saxena S., Zou L. // Mol. Cell. 2022. V. 82. № 12. P. 2298–2314.Maiorano D., El Etri J., Franchet C., Hoffmann J.S. // Int. J. Mol. Sci. 2021. V. 22. № 8. P. 3924.Bianco J.N., Bergoglio V., Lin Y.L., Pillaire M.J., Schmitz A.L., Gilhodes J., Lusque A., Mazieres J., Lacroix-Triki M., Roumeliotis T.I., et al. // Nat. Commun. 2019. V. 10. № 1. Р. 910.Ubhi T., Brown G.W. // Cancer Res. 2019. V. 79. № 8. P. 1730–1739.Allera-Moreau C., Rouquette I., Lepage B., Oumouhou N., Walschaerts M., Leconte E., Schilling V., Gordien K., Brouchet L., Delisle M.B., et al. // Oncogenesis. 2012. V. 1. № 10. P. e30.Baillie K.E., Stirling P.C. // Trends Cancer. 2021. V. 7. № 5. P. 430–446.Zuo S., Wei M., Wang S., Dong J., Wei J. // Front Immunol. 2020. V. 11. P. 1218.Sun S., Guo W., Wang Z., Wang X., Zhang G., Zhang H., Li R., Gao Y., Qiu B., Tan F., et al. // Cancer Med. 2020. V. 9. № 16. P. 5960–5975.Hu M., Li Y., Lu Y., Wang M., Li Y., Wang C., Li Q., Zhao H.// PeerJ. 2021. V. 9. P. e11306.Huang R.H., Hong Y.K., Du H., Ke W.Q., Lin B.B., Li Y.L. // J. Transl. Med. 2023. V. 21. № 1. P. 20.14 Dreyer S.B., Upstill-Goddard R., Paulus-Hock V., Paris C., Lampraki E.M., Dray E., Serrels B., Caligiuri G., Rebus S., Plenker D., et al. // Gastroenterology. 2021. V. 160. № 1. P. 362–377.Bala A.V., Galsky M.D., Rosenberg J.E., Powles T., Petrylak D.P., Bellmunt J., Loriot Y., Necchi A., Hoffman-Censits J., Perez-Gracia J.L., et al. // Lancet. 2017. V. 389. № 10064. P. 67–76.Hugo W., Zaretsky J.M., Sun L., Song C., Moreno B.H., Hu-Lieskovan S., Berent-Maoz B., Pang J., Chmielowski B., Cherry G., et al. // Cell. 2016. V. 165. № 1. P. 35–44.Robinson M.D., McCarthy D.J., Smyth G.K. // Bioinformatics. 2010. V. 26. № 1. P. 139–140.Friedman J., Hastie T., Tibshirani R. // J. Stat. Softw. 2010. V. 33. № 1. P. 1–22.Blanche P., Dartigues J.F., Jacqmin-Gadda H. // Stat. Med. 2013. V. 32. № 30. P. 5381–5397.Huang C., Liu Z., Xiao L., Xia Y., Huang J., Luo H., Zong Z., Zhu Z. // Front. Oncol. 2019. V. 9. P. 1159.Hanzelmann S., Castelo R., Guinney J. // Bioinformatics. 2013. V. 14. P. 7.Qu X., Zhao X., Lin K., Wang N., Li X., Li S., Zhang L., Shi Y. // Front. Immunol. 2022. V. 13. P. 994019.Lv W., Tan Y., Zhou X., Zhang Q., Zhang J., Wu Y. // Front. Immunol. 2022. V. 13. P. 989928.Sun Y., Cheng Z., Liu S. // Mol. Med. 2022. V. 28. № 1. P. 128.Ragu S., Matos-Rodrigues G., Lopez B.S. // Genes (Basel). 2020. V. 11. № 4. P. 409.Wang Q., Wu G., Fu L., Li Z., Wu Y., Zhu T., Yu G. // Mutation Research. 2023. V. 826. P. 111811.Ning G., Lu C., Chen Y., Jiang M., Si P., Zhang R. //Anticancer Drugs. 2023. V. 34. № 7. P. 866–876.Wang S., Cai W., Li J., An W., Zheng H., Liao M. // Biochem. Genet. 2022. V. 60. № 6. P. 1934–1945.He J., Wang Z., Wang Y., Zou T., Li X.P., Chen J. // Dis. Markers. V. 2022. https://doi.org/10.1155/2022/3306189.Hong M.J., Park J.E., Lee S.Y., Lee J., Choi J.E., Kang H.G., Do S.K., Jeong J.Y., Shin K.M., Lee W.K., et al. // J. Cancer. 2022. V. 13. № 15. 3701–3709.Panneerselvam J., Srivastava A., Mehta M., Chen A., Zhao Y.D., Munshi A., Ramesh R. // Cancers (Basel). 2019. V. 11. № 12. P. 1879.Sakurai E., Ishizawa H., Kiriyama Y., Michiba A., Hoshikawa Y., Tsukamoto T. // Int. J. Mol. Sci. 2022. V. 23. № 12. P. 6679.Wang W., Dong M., Cui J., Xu F., Yan C., Ma C., Yi L., Tang W., Dong J., Wei Y. // Mol. Med. Rep. 2019. V. 20. № 2. P. 1629–1636.Wu Y., Jamal M., Xie T., Sun J., Song T., Yin Q., Li J., Pan S., Zeng X., Xie S., et al. // Cancer Sci. 2019. V. 110. № 9. P. 2734–2747.Fu Y., Wei X.D., Guo L., Wu K., Le J., Ma Y., Kong X., Tong Y., Wu H. // Front. Oncol. 2022. V. 12. P. 904887.Kowash R.R., Akbay E.A. // Front. Immunol. 2023. V. 14. P. 1130358.Zhang H., Cao Y., Tang J., Wang R. // Biomed. Res. Int. 2022. V. 2022. P. 9944847.Song X., Wang S., Hong X., Li X., Zhao X., Huai C., Chen H., Gao Z., Qian J., Wang J., et al. // Sci. Rep. 2017. V. 7. № 1. P. 11785.Wang M., Liu H., Liu Z., Yi X., Bickeboller H., Hung R.J., Brennan P., Landi M.T., Caporaso N., Christiani D.C., et al. // Carcinogenesis. 2016. V. 37. № 9. P. 888–896.Attili I., Tarantino P., Passaro A., Stati V., Curigliano G., de Marinis F. // Lung Cancer. 2021. V. 154. P. 151–160.Capeloa T., Benyahia Z., Zampieri L.X., Blackman M., Sonveaux P. // Semin. Cell Dev. Biol. 2020. V. 98. P. 181–191.Borghaei H., Gettinger S., Vokes E.E., Chow L.Q.M., Burgio M.A., de Castro Carpeno J., Pluzanski A., Arrieta O., Frontera O.A., Chiari R., et al. // J. Clin. Oncol. 2021. V. 39. № 7. P. 723–733.Buric A.J., Dickerhoff J., Yang D. // Molecules. 2021. V. 26. № 14. P. 4132.Tu Q., Liu X., Yao X., Li R., Liu G., Jiang H., Li,K., Chen Q., Huang X., Chang Q., et al. // J. Exp. Clin. Cancer Res. 2022. V. 41. № 1. P. 274.Tateishi K., Tsubaki M., Takeda T., Yamatomo Y., Imano M., Satou T., Nishida S. // J. BUON. 2021. V. 26. № 2. P. 606–612.Heslop K.A., Rovini A., Hunt E.G., Fang D., Morris M.E., Christie C.F., Gooz M.B., DeHart D.N., Dang Y., Lemasters J.J., et al. // Biochem. Pharmacol. 2020. V. 171. P. 113728.