Journal of Shanghai Jiao Tong University (Medical Science) >

2021 , Vol. 41 >Issue 3: 285 - 296

DOI: https://doi.org/10.3969/j.issn.1674-8115.2021.03.001

Innovative research team achievement column

Construction of prognostic risk score model of colorectal cancer gene signature based on transcriptome dysregulation

  • Ru-juan BAO ,
  • Hui-fang CHEN ,
  • Yu DONG ,
  • You-qiong YE ,
  • Bing SU
Expand
  • Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Immunology, Shanghai 200025, China

Received date: 2020-11-16

  Online published: 2021-04-06

Supported by

Innovative Research Team of High-Level Local Universities in Shanghai(SSMU-ZDCX20180300)

Fold

Abstract

Objective

·To construct colorectal cancer (CRC) prognostic risk score model, analyze the significant differences of cancer hallmark signaling pathway or biological process among CRC patients with different scores, and predict the immunotherapy effect of the model on other cancer patients.

Methods

·Eight independent CRC microarray datasets and two CRC RNA-seq datasets were collected from a public database. Differentially expressed genes (DEGs) in each CRC dataset were screened. Based on DEGs with intersection from different datasets, univariate Cox regression model was used to screen the genes associated with adverse prognosis. LASSO regression and multivariate Cox regression models were used to construct CRC prognostic risk score model. According to the risk scores, the patients were divided into high risk group and low risk group. The area under the curve (AUC) of receiver operator characteristic curve and Kaplan-Meier (KM) survival analysis were used to evaluate the model performance. Multivariate Cox regression model was used to analyze whether risk score was an independent prognostic factor for CRC. Gene set enrichment analysis (GSEA) was used to analyze the differences of cancer hallmark gene sets-related pathways between the CRC patients in the high risk group and low risk group. KM survival analysis and chi-square test were used to predict the immunotherapy effect of other cancer patients, so as to evaluate the application value of CRC prognostic risk score model.

Results

·Univariate Cox regression analysis showed that 16 genes associated with adverse prognosis were obtained from DEGs with intersection from different datasets. Based on this, a CRC prognostic risk score model containing 8 gene signatures was constructed. In the training set (AUCmax=0.788) and internal/external validation sets (AUCmean>0.600), the model displayed moderate accuracy, and the patients in the low risk group of all the above sets had significantly higher survival rate than those in the high risk group. Multivariate Cox regression analysis showed that risk score was an independent prognostic factor for CRC. GSEA results showed that cancer hallmark gene sets-related pathways were significantly enriched in CRC patients of the high risk group. KM survival analysis and chi-square test showed that other cancer patients in the low risk group had higher survival rate and better immunotherapy effect.

Conclusion

·The CRC risk score prognosis model containing 8 gene signatures is successfully constructed, which can provide reference for improving the prognosis of CRC patients and predicting the immunotherapy effect on other cancer patients.

Key words： colorectal cancer (CRC); LASSO regression; Cox regression model; gene signature; prognosis model

Cite this article

Ru-juan BAO , Hui-fang CHEN , Yu DONG , You-qiong YE , Bing SU . Construction of prognostic risk score model of colorectal cancer gene signature based on transcriptome dysregulation[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2021 , 41(3) : 285 -296 . DOI: 10.3969/j.issn.1674-8115.2021.03.001

References

1 Testa U, Pelosi E, Castelli G. Colorectal cancer: genetic abnormalities, tumor progression, tumor heterogeneity, clonal evolution and tumor-initiating cells[J]. Med Sci (Basel), 2018, 6(2): 31.
2 Bosch LJW, Carvalho B, Fijneman RJA, et al. Molecular tests for colorectal cancer screening[J]. Clin Color Cancer, 2011, 10(1): 8-23.
3 Galon J, Costes A, Sanchez-Cabo F, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome[J]. Science, 2006, 313(5795): 1960-1964.
4 Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy[J]. Nat Rev Cancer, 2012, 12(4): 252-264.
5 Golshani G, Zhang Y. Advances in immunotherapy for colorectal cancer: a review[J]. Therap Adv Gastroenterol, 2020, 13: 1756284820917527.
6 Salem H, Attiya G, El-Fishawy N. Classification of human cancer diseases by gene expression profiles[J]. Appl Soft Comput, 2017, 50: 124-134.
7 Xue J, Schmidt SV, Sander J, et al. Transcriptome-based network analysis reveals a spectrum model of human macrophage activation[J]. Immunity, 2014, 40(2): 274-288.
8 Wang JD, Zhou HS, Tu XX, et al. Prediction of competing endogenous RNA coexpression network as prognostic markers in AML[J]. Aging (Albany NY), 2019, 11(10): 3333-3347.
9 Lu Y, Beeghly-Fadiel A, Wu L, et al. A transcriptome-wide association study among 97, 898 women to identify candidate susceptibility genes for epithelial ovarian cancer risk[J]. Cancer Res, 2018, 78(18): 5419-5430.
10 Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository[J]. Nucleic Acids Res, 2002, 30(1): 207-210.
11 Sabates-Bellver J, van der Flier LG, de Palo M, et al. Transcriptome profile of human colorectal adenomas[J]. Mol Cancer Res, 2007, 5(12): 1263-1275.
12 Matsuyama T, Ishikawa T, Mogushi K, et al. MUC12 mRNA expression is an independent marker of prognosis in stage Ⅱ and stage Ⅲ colorectal cancer[J]. Int J Cancer, 2010, 127(10): 2292-2299.
13 Skrzypczak M, Goryca K, Rubel T, et al. Modeling oncogenic signaling in colon tumors by multidirectional analyses of microarray data directed for maximization of analytical reliability[J]. PLoS One, 2010, 5(10): e13091.
14 Uddin S, Ahmed M, Hussain A, et al. Genome-wide expression analysis of middle eastern colorectal cancer reveals FOXM1 as a novel target for cancer therapy[J]. Am J Pathol, 2011, 178(2): 537-547.
15 Galamb O, Wichmann B, Sipos F, et al. Dysplasia-carcinoma transition specific transcripts in colonic biopsy samples[J]. PLoS One, 2012, 7(11): e48547.
16 Tsukamoto S, Ishikawa T, Iida S, et al. Clinical significance of osteoprotegerin expression in human colorectal cancer[J]. Clin Cancer Res, 2011, 17(8): 2444-2450.
17 de Sousa E Melo F, Colak S, Buikhuisen J, et al. Methylation of cancer-stem-cell-associated Wnt target genes predicts poor prognosis in colorectal cancer patients[J]. Cell Stem Cell, 2011, 9(5): 476-485.
18 Marisa L, de Reyniès A, Duval A, et al. Gene expression classification of colon cancer into molecular subtypes: characterization, validation, and prognostic value[J]. PLoS Med, 2013, 10(5): e1001453.
19 Smith JJ, Deane NG, Wu F, et al. Experimentally derived metastasis gene expression profile predicts recurrence and death in patients with colon cancer[J]. Gastroenterology, 2010, 138(3): 958-968.
20 Ritchie ME, Phipson B, Wu D, et al. Limma Powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Res, 2015, 43(7): e47.
21 Kolde R, Laur S, Adler P, et al. Robust rank aggregation for gene list integration and meta-analysis[J]. Bioinformatics, 2012, 28(4): 573-580.
22 Yu GC, Wang LG, Han YY, et al. clusterProfiler: an R package for comparing biological themes among gene clusters[J]. Omics, 2012, 16(5): 284-287.
23 Walter W, Sánchez-Cabo F, Ricote M. GOplot: an R package for visually combining expression data with functional analysis[J]. Bioinformatics, 2015, 31(17): 2912-2914.
24 Lin HQ, Zelterman D. Modeling survival data: extending the Cox model[J]. Technometrics, 2002, 44(1): 85-86.
25 Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent[J]. J Stat Softw, 2010, 33(1): 1-22.
26 Heagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker[J]. Biometrics, 2000, 56(2): 337-344.
27 Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles[J]. Proc Natl Acad Sci U S A, 2005, 102(43): 15545-15550.
28 Mariathasan S, Turley SJ, Nickles D, et al. TGF-β attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells[J]. Nature, 2018, 554(7693): 544-548.
29 Riaz N, Havel JJ, Makarov V, et al. Tumor and microenvironment evolution during immunotherapy with nivolumab[J]. Cell, 2017, 171(4): 934-949.e16.
30 Siegel RL, Miller KD, Goding Sauer A, et al. Colorectal cancer statistics, 2020[J]. CA Cancer J Clin, 2020, 70(3): 145-164.
31 Chen L, Lu D, Sun K, et al. Identification of biomarkers associated with diagnosis and prognosis of colorectal cancer patients based on integrated bioinformatics analysis[J]. Gene, 2019, 692: 119-125.
32 Zuo S, Dai G, Ren X. Identification of a 6-gene signature predicting prognosis for colorectal cancer[J]. Cancer Cell Int, 2019, 19: 6.
33 Pagès F, Mlecnik B, Marliot F, et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study[J]. Lancet, 2018, 391(10135): 2128-2139.
34 Zhao X, Liu J, Liu S, et al. Construction and validation of an immune-related prognostic model based on TP53 status in colorectal cancer[J]. Cancers (Basel), 2019, 11(11): 1722.
35 Wei C, Yang CG, Wang SY, et al. Crosstalk between cancer cells and tumor associated macrophages is required for mesenchymal circulating tumor cell-mediated colorectal cancer metastasis[J]. Mol Cancer, 2019, 18(1): 64.
36 Nie S, Huang YQ, Shi MY, et al. Protective role of ABCG2 against oxidative stress in colorectal cancer and its potential underlying mechanism[J]. Oncol Rep, 2018, 40(4): 2137-2146.
37 Waugh DJJ, Wilson C. The interleukin-8 pathway in cancer[J]. Clin Cancer Res, 2008, 14(21): 6735-6741.
38 Ackermann A, Lafferton B, Plotz G, et al. Expression and secretion of the pro-inflammatory cytokine IL-8 is increased in colorectal cancer cells following the knockdown of non-erythroid spectrin αⅡ[J]. Int J Oncol, 2020, 56(6): 1551-1564.
39 Song GH, Xu SF, Zhang H, et al. TIMP1 is a prognostic marker for the progression and metastasis of colon cancer through FAK-PI3K/AKT and MAPK pathway[J]. J Exp Clin Cancer Res, 2016, 35(1): 148.
40 Vigil D, Martin TD, Williams F, et al. Aberrant overexpression of the Rgl2 Ral small GTPase-specific guanine nucleotide exchange factor promotes pancreatic cancer growth through Ral-dependent and Ral-independent mechanisms[J]. J Biol Chem, 2010, 285(45): 34729-34740.
41 Ding BS, Lou WY, Xu L, et al. Analysis the prognostic values of solute carrier (SLC) family 39 genes in gastric cancer[J]. Am J Transl Res, 2019, 11(1): 486-498.
42 Ruan W, Zhu S, Wang H, et al. IGFBP-rP1, a potential molecule associated with colon cancer differentiation[J]. Mol Cancer, 2010, 9: 281.
43 Liu HN, Ni SJ, Wang HB, et al. Charactering tumor microenvironment reveals stromal-related transcription factors promote tumor carcinogenesis in gastric cancer[J]. Cancer Med, 2020, 9(14): 5247-5257.
44 Badic B, Hatt M, Durand S, et al. Radiogenomics-based cancer prognosis in colorectal cancer[J]. Sci Rep, 2019, 9(1): 9743.
45 Morris AR, Bos A, Diosdado B, et al. Alternative cleavage and polyadenylation during colorectal cancer development[J]. Clin Cancer Res, 2012, 18(19): 5256-5266.
46 Lei R, Feng LC, Hong D. ELFN1-AS1 accelerates the proliferation and migration of colorectal cancer via regulation of miR-4644/TRIM44 axis[J]. Cancer Biomarkers, 2020, 27(4): 433-443.
47 Jiang YY, Zhan HX. Communication between EMT and PD-L1 signaling: new insights into tumor immune evasion[J]. Cancer Lett, 2020, 468: 72-81.
48 Malek R, Wang H, Taparra K, et al. Therapeutic targeting of epithelial plasticity programs: focus on the epithelial-mesenchymal transition[J]. Cells Tissues Organs, 2017, 203(2): 114-127.
49 Zhu LY, Fu X, Chen X, et al. M2 macrophages induce EMT through the TGF-β/Smad2 signaling pathway[J]. Cell Biol Int, 2017, 41(9): 960-968.
50 Yoon C, Till J, Cho SJ, et al. KRAS activation in gastric adenocarcinoma stimulates epithelial-to-mesenchymal transition to cancer stem-like cells and promotes metastasis[J]. Mol Cancer Res, 2019, 17(9): 1945-1957.
51 Friedlander P, Wassmann K, Christenfeld AM, et al. Whole-blood RNA transcript-based models can predict clinical response in two large independent clinical studies of patients with advanced melanoma treated with the checkpoint inhibitor, tremelimumab[J]. J Immunother Cancer, 2017, 5(1): 67.
Options
Outlines

/

〈 〉