MicroRNA expression profile and TNM staging system predict survival in patients with lung adenocarcinoma

Guohong Xin; Xiaoci Cao; Wujie Zhao; Pintian Lv; Gang Qiu; Yaxing Li; Bin Wang; Baoshuan Fang; Yitao Jia; Guohong Xin; Xiaoci Cao; Wujie Zhao; Pintian Lv; Gang Qiu; Yaxing Li; Bin Wang; Baoshuan Fang; Yitao Jia

doi:10.3934/mbe.2020409

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 6: 8074-8083. doi: 10.3934/mbe.2020409

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Research article

MicroRNA expression profile and TNM staging system predict survival in patients with lung adenocarcinoma

Department of Oncology, Hebei Provincial Hospital, No. 348, West Heping Road, Shijiazhuang, Hebei 050051, China

Received: 08 August 2020 Accepted: 25 October 2020 Published: 12 November 2020

ObjectThe current study was performed to construct a model with microRNA (miRNA/miR) expression profile and TNM staging system for prognosis predicting in patients with lung adenocarcinoma (LUAD).

MethodsDifferentially expressed miRNAs were identified from miRNA data of LUAD in The Cancer Genome Atlas (TCGA) database. Potential prognostic miRNAs and TNM classification parameters, screened out by Cox proportional hazards regression analysis, were included in the prognostic model. The prognostic model was visualized with a nomogram, and tested by calculating the C-index and drawing the calibration curve in the training set and validating set, respectively. Finally, the prognostic miRNAs were analyzed with bioinformatics tools.

ResultsA total of 194 differentially expressed miRNAs were identified between LUAD tissues and matched normal tissues, including 99 up-regulated and 95 down-regulated miRNAs. miRNA index (miR.index), constructed with nine miRNAs (hsa-let-7i, hsa-mir-1976, hsa-mir-199a-1, hsa-mir-31, hsa-mir-3940, hsa-mir-450a-2, hsa-mir-4677, hsa-mir-548v and hsa-mir-6803), was an independent prognostic indicator for the survival of patients with LUAD. Bioinformatics analysis suggests that the selected miRNAs are involved in the development and progress of LUAD.

ConclusionThe prognostic model constructed with nine miRNA expression profile and TNM classification parameters can predict the survival in patients with LUAD, and the predictive power of the model are warranted for further validations.
- bioinformatics analysis,
- lung adenocarcinoma,
- microRNA,
- survival
Citation: Guohong Xin, Xiaoci Cao, Wujie Zhao, Pintian Lv, Gang Qiu, Yaxing Li, Bin Wang, Baoshuan Fang, Yitao Jia. MicroRNA expression profile and TNM staging system predict survival in patients with lung adenocarcinoma[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 8074-8083. doi: 10.3934/mbe.2020409

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Abstract

ObjectThe current study was performed to construct a model with microRNA (miRNA/miR) expression profile and TNM staging system for prognosis predicting in patients with lung adenocarcinoma (LUAD).

MethodsDifferentially expressed miRNAs were identified from miRNA data of LUAD in The Cancer Genome Atlas (TCGA) database. Potential prognostic miRNAs and TNM classification parameters, screened out by Cox proportional hazards regression analysis, were included in the prognostic model. The prognostic model was visualized with a nomogram, and tested by calculating the C-index and drawing the calibration curve in the training set and validating set, respectively. Finally, the prognostic miRNAs were analyzed with bioinformatics tools.

ResultsA total of 194 differentially expressed miRNAs were identified between LUAD tissues and matched normal tissues, including 99 up-regulated and 95 down-regulated miRNAs. miRNA index (miR.index), constructed with nine miRNAs (hsa-let-7i, hsa-mir-1976, hsa-mir-199a-1, hsa-mir-31, hsa-mir-3940, hsa-mir-450a-2, hsa-mir-4677, hsa-mir-548v and hsa-mir-6803), was an independent prognostic indicator for the survival of patients with LUAD. Bioinformatics analysis suggests that the selected miRNAs are involved in the development and progress of LUAD.

ConclusionThe prognostic model constructed with nine miRNA expression profile and TNM classification parameters can predict the survival in patients with LUAD, and the predictive power of the model are warranted for further validations.

References

[1]	F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, A. Jemal, Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, Ca-Cancer J. Clin., 68 (2018), 394-424. doi: 10.3322/caac.21492
[2]	C. Allemani, T. Matsuda, V. Di Carlo, R. Harewood, M. Matz, M. Nikšić, et al., Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): Analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries, Lancet, 391 (2018), 1023-1075. doi: 10.1016/S0140-6736(17)33326-3
[3]	E. Zlotorynski, Insights into the kinetics of microRNA biogenesis and turnover, Nat. Rev. Mol. Cell. Biol., 20 (2019), 511.
[4]	N. Treiber, T. Treiber, G. Meister, Regulation of microRNA biogenesis and its crosstalk with other cellular pathways, Nat. Rev. Mol. Cell Biol., 20 (2019), 5-20.
[5]	D. P. Bartel, MicroRNAs: Genomics, biogenesis, mechanism, and function, Cell, 116 (2004), 281-297. doi: 10.1016/S0092-8674(04)00045-5
[6]	J. Winter, S. Jung, S. Keller, R. I. Gregory, S. Diederichs, Many roads to maturity: MicroRNA biogenesis pathways and their regulation, Nat. Cell Biol., 11 (2009), 228-234. doi: 10.1038/ncb0309-228
[7]	R. Rupaimoole, F. J. Slack, MicroRNA therapeutics: Towards a new era for the management of cancer and other diseases, Nat. Rev. Drug Discov., 16 (2017), 203-222. doi: 10.1038/nrd.2016.246
[8]	G. A. Calin, C. M. Croce, MicroRNA signatures in human cancers, Nat. Rev. Cancer, 6 (2006), 857-866. doi: 10.1038/nrc1997
[9]	D. A. Clump, C. R. Pickering, H. D. Skinner, Predicting outcome in head and neck cancer: MiRNAs with potentially big effects, Clin. Cancer Res., 25 (2019), 1441-1442. doi: 10.1158/1078-0432.CCR-18-3078
[10]	J. Hess, K. Unger, C. Maihoefer, L. Schuttrumpf, L. Wintergerst, T. Heider, et al., A Five-MicroRNA signature predicts survival and disease control of patients with head and neck cancer negative for HPV infection, Clin. Cancer Res., 25 (2019), 1505-1516. doi: 10.1158/1078-0432.CCR-18-0776
[11]	P. Ulivi, E. Petracci, G. Marisi, S. Baglivo, R. Chiari, M. Billi, et al., Prognostic role of circulating miRNAs in early-stage non-small cell lung cancer, J. Clin. Med., 8 (2019), 131. doi: 10.3390/jcm8020131
[12]	Y. Zhang, J. A. Roth, H. Yu, Y. Ye, K. Xie, H. Zhao, et al., A 5-microRNA signature identified from serum microRNA profiling predicts survival in patients with advanced stage non-small cell lung cancer, Carcinog., 40 (2019), 643-650. doi: 10.1093/carcin/bgy132
[13]	H. Yan, S. Xin, J. Ma, H. Wang, H. Zhang, J. Liu, A three microRNA-based prognostic signature for small cell lung cancer overall survival, J. Cell. Biochem., 120 (2018), 8723-8730.
[14]	X. Li, Z. An, P. Li, H. Liu, A prognostic model for lung adenocarcinoma patient survival with a focus on four miRNAs, Oncol. Lett., 14 (2017), 2991-2995. doi: 10.3892/ol.2017.6481
[15]	Y. Lin, Y. Lv, R. Liang, C. Yuan, J. Zhang, D. He, et al., Four-miRNA signature as a prognostic tool for lung adenocarcinoma, Onco. Targets Ther., 11 (2018), 29-36.
[16]	S. S. Yerukala, S. Y. Ho, Identifying the miRNA signature associated with survival time in patients with lung adenocarcinoma using miRNA expression profiles, Sci. Rep., 7 (2017), 7507. doi: 10.1038/s41598-017-07739-y
[17]	M. Raponi, L. Dossey, T. Jatkoe, X. Wu, G. Chen, H. Fan, et al., MicroRNA classifiers for predicting prognosis of squamous cell lung cancer, Cancer Res., 69 (2009), 5776-5783. doi: 10.1158/0008-5472.CAN-09-0587
[18]	R. Bajaj, D. Doval, R. Tripathi, T. Sridhar, A. Korlimarla, K. D. Choudhury, et al., Prognostic role of microRNA 182 and microRNA 18a in locally advanced triple negative breast cancer, Ann. Oncol., 30 (2019), 19.
[19]	H. Li, J. Liu, J. Chen, H. Wang, L. Yang, F. Chen, et al., A serum microRNA signature predicts trastuzumab benefit in HER2-positive metastatic breast cancer patients, Nat. Commun., 9 (2018), 1614. doi: 10.1038/s41467-018-03537-w
[20]	S. Di Cosimo, V. Appierto, S. Pizzamiglio, P. Tiberio, M. V. Iorio, F. Hilbers, et al., Plasma miRNA levels for predicting therapeutic response to neoadjuvant treatment in HER2-positive breast cancer: Results from the NeoALTTO trial, Clin. Cancer Res., 25 (2019), 3887-3895. doi: 10.1158/1078-0432.CCR-18-2507
[21]	S. Shiino, J. Matsuzaki, A. Shimomura, J. Kawauchi, S. Takizawa, H. Sakamoto, et al., Serum miRNA-based prediction of axillary lymph node metastasis in breast cancer, Clin. Cancer Res., 25 (2019), 1817-1827. doi: 10.1158/1078-0432.CCR-18-1414
[22]	L. Yu, D. Wu, H. Gao, J. J. Balic, A. Tsykin, T. S. Han, et al., Clinical utility of a STAT3-Regulated miRNA-200 family signature with prognostic potential in early gastric cancer, Clin. Cancer Res., 24 (2018), 1459-1472. doi: 10.1158/1078-0432.CCR-17-2485
[23]	P. Wang, W. Li, B. Zhai, X. Jiang, H. Jiang, C. Zhang, et al., Integrating high-throughput microRNA and mRNA expression data to identify risk mRNA signature for pancreatic cancer prognosis, J. Cell. Biochem., 121 (2020), 3090-3098. doi: 10.1002/jcb.29576
[24]	X. Zhang, H. Zhang, B. Shen, X. F. Sun, Novel MicroRNA biomarkers for colorectal cancer early diagnosis and 5-Fluorouracil chemotherapy resistance but not prognosis: A study from databases to AI-Assisted verifications, Cancers (Basel), 12 (2020), 341. doi: 10.3390/cancers12020341
[25]	R. Kandimalla, F. Gao, T. Matsuyama, T. Ishikawa, H. Uetake, N. Takahashi, et al., Genome-wide discovery and identification of a novel miRNA signature for recurrence prediction in stage II and III colorectal cancer, Clin. Cancer Res., 24 (2018), 3867-3877. doi: 10.1158/1078-0432.CCR-17-3236
[26]	L. Schmidt, J. Fredsoe, H. Kristensen, S. H. Strand, A. Rasmussen, S. Hoyer, et al., Training and validation of a novel 4-miRNA ratio model (MiCaP) for prediction of postoperative outcome in prostate cancer patients, Ann. Oncol., 29 (2018), 2003-2009. doi: 10.1093/annonc/mdy243
[27]	J. S. Nahand, S. Taghizadeh-Boroujeni, M. Karimzadeh, S. Borran, M. H. Pourhanifeh, M. Moghoofei, et al., MicroRNAs: New prognostic, diagnostic, and therapeutic biomarkers in cervical cancer, J. Cell. Physiol., 234 (2019), 17064-17099. doi: 10.1002/jcp.28457
[28]	B. Liang, Y. Li, T. Wang, A three miRNAs signature predicts survival in cervical cancer using bioinformatics analysis, Sci. Rep., 7 (2017), 5624. doi: 10.1038/s41598-017-18338-2
[29]	X. H. Yin, Y. H. Jin, Y. Cao, Y. Wong, H. Weng, C. Sun, et al., Development of a 21-miRNA signature associated with the prognosis of patients with bladder cancer, Front. Oncol., 9 (2019), 729. doi: 10.3389/fonc.2019.00729
[30]	M. E. Ritchie, B. Phipson, Di Wu, Y. Hu, C. W. Law, W. Shi, et al., {Limma} powers differential expression analyses for {RNA}-sequencing and microarray studies, Nucleic. Acids. Res., 43 (2015), 47. doi: 10.1093/nar/gkv007
[31]	R Core Team, R: A Language and Environment for Statistical Computing, Vienna, Austria, 2020.
[32]	G. Yang, Y. Zhang, J. Yang, A Five-microRNA signature as prognostic biomarker in colorectal cancer by bioinformatics analysis, Front. Oncol., 9 (2019), 1207. doi: 10.3389/fonc.2019.01207
[33]	Y. Luo, C. Zhang, F. Tang, J. Zhao, C. Shen, C. Wang, et al., Bioinformatics identification of potentially involved microRNAs in Tibetan with gastric cancer based on microRNA profiling, Cancer Cell Int., 15 (2015), 115. doi: 10.1186/s12935-015-0266-1
[34]	Y. Lv, J. Duanmu, X. Fu, T. Li, Q. Jiang, Identifying a new microRNA signature as a prognostic biomarker in colon cancer, PLoS. One, 15 (2020), 228575.
[35]	Therneau, M. Terry, A Package for Survival Analysis in R, 2020.
[36]	A. Kassambara, M. Kosinski, P. Biecek, Survminer: Drawing Survival Curves using 'ggplot2', 2019.
[37]	F. E. H. Jr, Rms: Regression Modeling Strategies, 2019.

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