Development of an Immune Prognostic Model for Clear Cell Renal Cell
                    Carcinoma Based on Tumor Microenvironment

Munan Wang; Qianqian Song; Zhijie Song; Yuduan Xie

doi:10.1055/a-2079-2826

Hormone and Metabolic Research, Table of Contents

Horm Metab Res 2023; 55(06): 402-412
DOI: 10.1055/a-2079-2826

Original Article: Endocrine Care

Development of an Immune Prognostic Model for Clear Cell Renal Cell Carcinoma Based on Tumor Microenvironment

Authors

Munan Wang

¹College of Traditional Chinese Medicine, Chongqing Medical University, Chongqing, China
Qianqian Song

¹College of Traditional Chinese Medicine, Chongqing Medical University, Chongqing, China
Zhijie Song

²School of Integrated Traditional Chinese and Western Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
Yuduan Xie

³Laboratory Department, Wangjing Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, China

Abstract

Immune infiltration remains at a high level in clear cell renal cell carcinoma (ccRCC). It has been confirmed that immune cell infiltration in tumor microenvironment (TME) is intimately bound up with the progression and the clinical outcome of ccRCC. The prognostic model, developed based on different immune subtypes of ccRCC, has a predictive value in patients’ prognosis. RNA sequencing data, somatic mutation data of ccRCC and clinical information were acquired from the cancer genome atlas (TCGA) database. The key immune-related genes (IRGs) were selected and by univariate Cox, LASSO, and multivariate Cox regression analyses. Then the ccRCC prognostic model was developed. The applicability of this model was verified in the independent dataset GSE29609. Thirteen IRGs including CCL7, ATP6V1C2, ATP2B3, ELAVL2, SLC22A8, DPP6, EREG, SERPINA7, PAGE2B, ADCYAP1, ZNF560, MUC20, and ANKRD30A were finally selected and a 13-IRGs prognostic model was developed. Survival analysis demonstrated that when compared with the low-risk group, patients in the high-risk group had a lower overall survival (p<0.05). AUC values based on the 13-IRGs prognostic model used to predict 3- and 5-year survival of ccRCC patients were greater than 0.70. And risk score was an independent prognostic factor (p<0.001). In addition, nomogram could accurately predict ccRCC patient’s prognosis. This 13-IRGs model can effectively evaluate the prognosis of ccRCC patients, and also provide guidance for the treatment and prognosis of ccRCC patients.

Key words

TCGA - clear cell renal cell carcinoma - immune microenvironment - prognosis - signature

Full Text

References

References
1 Capitanio U, Bensalah K, Bex A. et al. Epidemiology of renal cell carcinoma. Eur Urol 2019; 75: 74-84
2 Sanchez DJ, Simon MC. Genetic and metabolic hallmarks of clear cell renal cell carcinoma. Biochim Biophys Acta Rev Cancer 2018; 1870: 23-31
3 Ljungberg B, Albiges L, Abu-Ghanem Y. et al. European association of urology guidelines on renal cell carcinoma: the 2022 update. Eur Urol 2022; 82: 399-410
4 Senbabaoglu Y, Gejman RS, Winer AG. et al. Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol 2016; 17: 231
5 Janiszewska AD, Poletajew S, Wasiutynski A. Spontaneous regression of renal cell carcinoma. Contemp Oncol (Pozn) 2013; 17: 123-127
6 Considine B, Hurwitz ME. Current status and future directions of immunotherapy in renal cell carcinoma. Curr Oncol Rep 2019; 21: 34
7 Yang F, Yu Y. [Tumor microenvironment--the critical element of tumor metastasis]. Zhongguo Fei Ai Za Zhi 2015; 18: 48-54
8 Chevrier S, Levine JH, Zanotelli VRT. et al. An immune atlas of clear cell renal cell carcinoma. Cell 2017; 169: 736-749 e18
9 Su S, Akbarinejad S, Shahriyari L. Immune classification of clear cell renal cell carcinoma. Sci Rep 2021; 11: 4338
10 Jensen HK, Donskov F, Nordsmark M. et al. Increased intratumoral FOXP3-positive regulatory immune cells during interleukin-2 treatment in metastatic renal cell carcinoma. Clin Cancer Res 2009; 15: 1052-1058
11 Adotevi O, Pere H, Ravel P. et al. A decrease of regulatory T cells correlates with overall survival after sunitinib-based antiangiogenic therapy in metastatic renal cancer patients. J Immunother 2010; 33: 991-998
12 Azizi E, Carr AJ, Plitas G. et al. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 2018; 174: 1293-308 e36
13 Yoshihara K, Shahmoradgoli M, Martinez E. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 2013; 4: 2612
14 Borgan Ø. Modeling survival data: Extending the Cox model. Therneau TM, Grambsch PM (eds). Springer-Verlag; New York: 2000
15 Ritchie ME, Phipson B, Wu D. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43: e47
16 Yu G, Wang LG, Han Y. et al. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 2012; 16: 284-287
17 Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Software 2010; 33: 1-22
18 Blanche P, Dartigues JF, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med 2013; 32: 5381-5397
19 Huang C, Liu Z, Xiao L. et al. Clinical significance of serum CA125, CA19-9, CA72-4, and fibrinogen-to-lymphocyte ratio in gastric cancer with peritoneal dissemination. Front Oncol 2019; 9: 1159
20 Iranzo J, Martincorena I, Koonin EV. Cancer-mutation network and the number and specificity of driver mutations. Proc Natl Acad Sci U S A 2018; 115: E6010-E6019
21 Ma J, Li M, Chai J. et al. Expression of RSK4, CD44 and MMP-9 is upregulated and positively correlated in metastatic ccRCC. Diagn Pathol 2020; 15: 28
22 Susek KH, Karvouni M, Alici E. et al. The role of CXC chemokine receptors 1-4 on immune cells in the tumor microenvironment. Front Immunol 2018; 9: 2159
23 Guerder S, Flavell RA. T-cell activation. Two for T. Curr Biol 1995; 5: 866-868
24 Lima LG, Monteiro RQ. Activation of blood coagulation in cancer: implications for tumour progression. Biosci Rep 2013; 33
25 Jung DW, Che ZM, Kim J. et al. Tumor-stromal crosstalk in invasion of oral squamous cell carcinoma: a pivotal role of CCL7. Int J Cancer 2010; 127: 332-344
26 Wang Y, Jing Y, Ding L. et al. Epiregulin reprograms cancer-associated fibroblasts and facilitates oral squamous cell carcinoma invasion via JAK2-STAT3 pathway. J Exp Clin Cancer Res 2019; 38: 274
27 Zhao WS, Yan WP, Chen DB. et al. Genome-scale CRISPR activation screening identifies a role of ELAVL2-CDKN1A axis in paclitaxel resistance in esophageal squamous cell carcinoma. Am J Cancer Res 2019; 9: 1183-1200
28 Hou S, Xie X, Zhao J. et al. Downregulation of miR-146b-3p inhibits proliferation and migration and modulates the expression and location of sodium/iodide symporter in dedifferentiated thyroid cancer by potentially targeting MUC20. Front Oncol 2020; 10: 566365
29 Klemm F, Maas RR, Bowman RL. et al. Interrogation of the microenvironmental landscape in brain tumors reveals disease-specific alterations of immune cells. Cell 2020; 181: 1643-60 e17
30 Nowotarski SL, Woster PM, Casero RA. Polyamines and cancer: implications for chemotherapy and chemoprevention. Expert Rev Mol Med 2013; 15: e3
31 Latour YL, Gobert AP, Wilson KT. The role of polyamines in the regulation of macrophage polarization and function. Amino Acids 2020; 52: 151-160
32 Liu R, Li X, Ma H. et al. Spermidine endows macrophages anti-inflammatory properties by inducing mitochondrial superoxide-dependent AMPK activation, Hif-1alpha upregulation and autophagy. Free Radic Biol Med 2020; 161: 339-350
33 Facciabene A, Peng X, Hagemann IS. et al. Tumour hypoxia promotes tolerance and angiogenesis via CCL28 and T(reg) cells. Nature 2011; 475: 226-230

Supplementary Material

Supplementary Material (PDF)