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The Identification of Gamma-Glutamyl Hydrolase in Uterine Corpus Endometrial Carcinoma: a Predictive Model and Machine Learning

  • Gynecologic Oncology: Original Article
  • Published:
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Abstract

Background

Poor neoplastic differentiation contributes to the rapid progression of uterine corpus endometrial carcinoma (UCEC). Thus, it is essential to identify candidate genes, clarifying the carcinogenesis and progression of UCEC.

Methods

We screened genes that affect differentiation and prognosis in UCEC. Least absolute selection and shrinkage operator (LASSO) regression, univariate Cox, and multivariate Cox proportional risk regression analyses were performed to screen out γ-glutamyl hydrolase (GGH) as the candidate gene. The clinical value of GGH on prognosis was evaluated. The relationship between GGH and immune infiltration was assessed by CIBERSORT, EPIC, ssGSEA, unsupervised clustering and immunohistochemistry (IHC). Additionally, we investigated the effect of GGH knockdown in vitro.

Results

Among the GGH, CDKN2A, and SIX1 genes, the impact of GGH was predominant on immune infiltration in UCEC. A nomogram containing GGH and other clinical features showed good predictive performance via curve analysis (DCA). In the functional analysis, GGH affected differentiation, tumour proliferation, and immune regulation. The immunosuppressive components were enriched in the GGH-high group, with poor immunotherapy efficacy. The study suggests that GGH may influence the progression of UCEC by regulating the glycolytic process.

Conclusions

GGH is closely associated with various immune cell infiltrations. Our study demonstrates the prognostic role of GGH in carcinogenesis in UCEC.

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Data Availability

The datasets supporting the conclusions of this article are included within the article (and its additional files). The data analyzed in this study are available in public databases. TCGA and GTEx data were obtained from the UCSC XENA database (UCSC XENA: https://xenabrowser.net/datapages/, TCGA: http://www.cancer.gov/, GTEx: https://gtexportal.org/). Validation data and immunotherapy data were obtained from the GEO and TIDE databases (GEO: https://www.ncbi.nlm.nih.gov/geo/, TIDE: http://tide.dfci.harvard.edu/). The methylation data were obtained from MEXPRESS (http://mexpress.be).

Code Availability

The codes are available in the Supplementary Materials.

Abbreviations

GGH :

Gamma-glutamyl hydrolase

UCEC :

Uterine corpus endometrial carcinoma

GO :

Gene ontology

KEGG :

Kyoto encyclopedia of genes and genomes

OS :

Overall survival

DFS :

Disease free survival

PFS :

Progression free survival

DSS :

Disease specific survival

TCGA :

The cancer genome atlas

GTEx :

Genotype-tissue expression

AUC :

Area under curve

ROC :

Receiver operating characteristic

WGCNA :

Weighted gene coexpression network

Lasso :

Least absolute selection and shrinkage operator

DNMT :

DNA methyltransferase

SNP :

Single nucleotide polymorphisms

KM :

Kaplan-Meier

GEO :

Gene expression omnibus

PD :

Progressive disease

SD :

Stable disease

PR :

Partial response

CR :

Complete response

OCLR :

One-class logistic regression

mRNAsi :

Gene expression-based stemness index

DEGs :

Differentially expressed genes

GSEA :

Gene set enrichment analysis FDR: false discovery rate

ssGSEA :

Single sample GSEA

TMB :

Tumor mutation burden

MSI :

Microsatellite instability

TIDE :

Tumor immune dysfunction and exclusion

TMB-H :

Tumor mutational burden-High

MMR :

Mismatch repair

TME :

Tumor microenvironment

ICIs :

Immune checkpoint inhibitors

ORR :

Objective response rate

DCs :

Dendritic cells

iDCs :

Immature dendritic cells

aDCs :

Activated dendritic cells

NKs :

Natural killer cells

NK CD56dim cells :

Natural killer CD56dim cells

NK CD56bright cells :

Natural killer CD56bright cells

Th1 :

T helper 1

Th2 :

T helper 2

5-FU :

5-Fluorouracil

CTX :

Cyclophosphamide

Tγδ :

T gamma delta

Tcm :

T central memory

Tem :

T effector memory

Tfh cells :

T follicular helper

ICB :

Immune checkpoint blockade

ACC :

Adrenocortical carcinoma

BLCA :

Bladder urothelial carcinoma

BRCA :

Breast invasive carcinoma

CESC :

Cervical squamous cell carcinoma and endocervical adenocarcinoma

CHOL :

Cholangiocarcinoma

COAD :

Colon adenocarcinoma

DLBC :

Lymphoid neoplasm diffuse large B-cell lymphoma

ESCA :

Esophageal carcinoma

GBM :

Glioblastoma multiforme

HNSC :

Head and Neck squamous cell carcinoma

KICH :

Kidney Chromophobe

KIRC :

Kidney renal clear cell carcinoma

KIRP :

Kidney renal papillary cell carcinoma

LAML :

Acute myeloid leukemia

LGG :

Brain lower grade glioma

LIHC :

Liver hepatocellular carcinoma

LUAD :

Lung adenocarcinoma

LUSC :

Lung squamous cell carcinoma

MESO :

Mesothelioma

OV :

Ovarian serous cystadenocarcinoma

PAAD :

Pancreatic adenocarcinoma

PCPG :

Pheochromocytoma and Paraganglioma

PRAD :

Prostate adenocarcinoma

READ :

Rectum adenocarcinoma

SARC :

Sarcoma

SKCM :

Skin cutaneous melanoma

STAD :

Stomach adenocarcinoma

TGCT :

Testicular germ cell tumors

THCA :

Thyroid carcinoma

THYM :

Thymoma

UCS :

Uterine Carcinosarcoma

UVM :

Uveal Melanoma

References

  1. Crosbie EJ, Kitson SJ, McAlpine JN, Mukhopadhyay A, Powell ME, Singh N. Endometrial cancer. Lancet (London, England). 2022;399(10333):1412–28.

    Article  PubMed  Google Scholar 

  2. Hendrickson M, Ross J, Eifel P, Martinez A, Kempson R. Uterine papillary serous carcinoma: a highly malignant form of endometrial adenocarcinoma. Am J Surg Pathol. 1982;6(2):93–108.

    Article  CAS  PubMed  Google Scholar 

  3. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72(1):7–33.

    Article  PubMed  Google Scholar 

  4. Panetta JC, Wall A, Pui CH, Relling MV, Evans WE. Methotrexate intracellular disposition in acute lymphoblastic leukemia: a mathematical model of gamma-glutamyl hydrolase activity. Clin Cancer Res. 2002;8(7):2423–9.

    CAS  PubMed  Google Scholar 

  5. Kim SE, Hinoue T, Kim MS, Sohn KJ, Cho RC, Cole PD, et al. γ-Glutamyl hydrolase modulation significantly influences global and gene-specific DNA methylation and gene expression in human colon and breast cancer cells. Genes Nutr. 2015;10(1):444.

    Article  PubMed  Google Scholar 

  6. Shubbar E, Helou K, Kovács A, Nemes S, Hajizadeh S, Enerbäck C, et al. High levels of γ-glutamyl hydrolase (GGH) are associated with poor prognosis and unfavorable clinical outcomes in invasive breast cancer. BMC Cancer. 2013;13:47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Rots MG, Pieters R, Peters GJ, Noordhuis P, van Zantwijk CH, Kaspers GJL, et al. Role of folylpolyglutamate synthetase and folylpolyglutamate hydrolase in methotrexate accumulation and polyglutamylation in childhood leukemia. Blood. 1999;93(5):1677–83.

    Article  CAS  PubMed  Google Scholar 

  8. Vivian J, Rao AA, Nothaft FA, Ketchum C, Armstrong J, Novak A, et al. Toil enables reproducible, open source, big biomedical data analyses. Nat Biotechnol. 2017;35(4):314–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sun D, Wang J, Han Y, Dong X, Ge J, Zheng R, et al. TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acids Res. 2021;49(D1):D1420–30.

    Article  CAS  PubMed  Google Scholar 

  10. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics (Oxford, England). 2016;32(18):2847–9.

    CAS  PubMed  Google Scholar 

  11. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6):565–74.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Walter W, Sánchez-Cabo F, Ricote M. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics (Oxford, England). 2015;31(17):2912–4.

    CAS  PubMed  Google Scholar 

  15. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Langfelder P, Horvath S. Fast R functions for robust correlations and hierarchical clustering. J Stat Softw. 2012;46(11):i11-37.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Csárdi G, Nepusz T. The igraph software package for complex network research. InterJournal, Complex Systems. 2006;1695(5):1–9.

    Google Scholar 

  19. Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28(11):1747–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4.

    Article  PubMed  Google Scholar 

  21. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):l1.

    Article  Google Scholar 

  22. Koch A, De Meyer T, Jeschke J, Van Criekinge W. MEXPRESS: visualizing expression, DNA methylation and clinical TCGA data. BMC Genomics. 2015;16(1):636.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Koch A, Jeschke J, Van Criekinge W, van Engeland M, De Meyer T. MEXPRESS update 2019. Nucleic Acids Res. 2019;47(W1):W561–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics (Oxford, England). 2011;27(12):1739–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Thienpont B, Steinbacher J, Zhao H, D’Anna F, Kuchnio A, Ploumakis A, et al. Tumour hypoxia causes DNA hypermethylation by reducing TET activity. Nature. 2016;537(7618):63–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Danaher P, Warren S, Lu R, Samayoa J, Sullivan A, Pekker I, et al. Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA). J Immunother Cancer. 2018;6(1):63.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Wei J, Huang K, Chen Z, Hu M, Bai Y, Lin S, et al. Characterization of glycolysis-associated molecules in the tumor microenvironment revealed by pan-cancer tissues and lung cancer single cell data. Cancers. 2020;12(7):1788–810.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN, et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell. 2018;173(2):338-54.e15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.

    Article  PubMed  Google Scholar 

  30. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity. 2013;39(4):782–95.

    Article  CAS  PubMed  Google Scholar 

  32. Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018;24(10):1550–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Lánczky A, Győrffy B. Web-based survival analysis tool tailored for medical research (KMplot): Development and implementation. J Med Internet Res. 2021;23(7):e27633.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics (Oxford, England). 2010;26(12):1572–3.

    CAS  PubMed  Google Scholar 

  35. Makker V, MacKay H, Ray-Coquard I, Levine DA, Westin SN, Aoki D, et al. Endometrial cancer. Nat Revi Dis Prim. 2021;7(1):88.

    Article  Google Scholar 

  36. Ventriglia J, Paciolla I, Pisano C, Cecere SC, Di Napoli M, Tambaro R, et al. Immunotherapy in ovarian, endometrial and cervical cancer: State of the art and future perspectives. Cancer Treat Rev. 2017;59:109–16.

    Article  CAS  PubMed  Google Scholar 

  37. Melling N, Rashed M, Schroeder C, Hube-Magg C, Kluth M, Lang D, et al. High-level γ-glutamyl-hydrolase (GGH) expression is linked to poor prognosis in ERG negative prostate cancer. Int J Mol Sci. 2017;18(2):286–300.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Garnis C, Buys TP, Lam WL. Genetic alteration and gene expression modulation during cancer progression. Mol Cancer. 2004;3:9.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Vanderstraeten A, Tuyaerts S, Amant F. The immune system in the normal endometrium and implications for endometrial cancer development. J Reprod Immunol. 2015;109:7–16.

    Article  CAS  PubMed  Google Scholar 

  40. Kondratiev S, Sabo E, Yakirevich E, Lavie O, Resnick MB. Intratumoral CD8+ T lymphocytes as a prognostic factor of survival in endometrial carcinoma. Clin Cancer Res. 2004;10(13):4450–6.

    Article  PubMed  Google Scholar 

  41. Warburg O. On the origin of cancer cells. Science (New York, NY). 1956;123(3191):309–14.

    Article  CAS  Google Scholar 

Download references

Funding

This work was supported by the grant from Anhui Natural Science Foundation Youth Program (2008085QH424). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. Cheng Zhu, Yishan Du, Qian Huai and Nana Fang contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China

    Cheng Zhu, Qian Huai, Wentao Xu, Xingyu Li, Yanyan Zhang, Xu Zhang, Hanren Dai, Xiaolei Li, Hua Wang & Ying Dai

  2. Department of Geriatrics, Affiliated Provincial Hospital of Anhui Medical University, Hefei, 230001, China

    Yishan Du

  3. Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, 230032, China

    Nana Fang & Hua Wang

  4. Department of Breast Surgery, Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, 230036, China

    Jing Yang

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Contributions

Cheng Zhu: Conceptualization, Methodology, Software, Visualization, Writing—original draft. Yishan Du, Qian Huai and Nana Fang: Validation, Software, Methodology. Jing Yang: Validation, Writing—review & editing. Xingyu Li, Wentao Xu and Yanyan Zhang: Writing—review & editing. Xu Zhang and Hanren Dai: Validation, Methodology. Xiaolei Li, Hua Wang and Ying Dai: Conceptualization, Methodology, Supervision.

Corresponding authors

Correspondence to Xiaolei Li, Hua Wang or Ying Dai.

Ethics declarations

Ethics Approval

This study was in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University (approval number: PJ2023-01-15). Written informed consent was obtained from the patients.

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Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Figure 1 The main process of this study. (JPG 3050 KB)

43032_2023_1363_MOESM2_ESM.jpg

Supplementary Figure 2 Enrichment analysis of differential genes in different degrees of differentiation in UCEC. (a) Volcano plot of DEGs between highly differentiated and poorly differentiated patients. (b) GO/KEGG enrichment analysis of upregulated genes in poorly differentiated patients. (c) GO/KEGG enrichment analysis of upregulated genes in highly differentiated patients. (d–g) GSEA enrichment analysis of all genes in highly differentiated and poorly differentiated patients. (JPG 548 KB)

43032_2023_1363_MOESM3_ESM.jpg

Supplementary Figure 3 Prognostic analysis of genetic signature models for supplementary materials. (a) Risk score, survival time and expression profile heat map for 3-gene prognosis. (b) Prognostic model for KM survival analysis. (c) Time-dependent ROC analysis of predictive models. (JPG 388 KB)

43032_2023_1363_MOESM4_ESM.jpg

Supplementary Figure 4 Association of GGH, CDKN2A and SIX1 with immune infiltration. (a) Correlation of GGH expression with stromal scores. (b) Correlation of SIX1 expression with stromal scores. (c) Correlation of CDKN2A expression with stromal scores. (d) Heat map of immune infiltration in GGH high expression versus low expression groups (CIBERSORT algorithm). (e) Heat map of immune infiltration in SIX1 high expression versus low expression groups (CIBERSORT algorithm). (f) Heat map of immune infiltration in SIX1 high expression versus low expression groups (CIBERSORT algorithm). (g) Heat map of immune infiltration in CDKN2A high expression versus low expression groups (CIBERSORT algorithm). (i) Immune infiltration abundance of each endometrial cancer sample in TCGA (CIBERSORT algorithm). (JPG 869 KB)

43032_2023_1363_MOESM5_ESM.jpg

Supplementary Figure 5 Relationship between GGH expression and clinical characteristics of patients with endometrial cancer. (a) clinical stage, (b) histological type, (c) histological grade, (d) race, (e) Tumor Invasion, (f) BMI, (g) age, and (h) Menopause status. (JPG 559 KB)

43032_2023_1363_MOESM6_ESM.jpg

Supplementary Figure 6 Single-Cell Analysis of GGH in Cancers. (a) Heat map of GGH expression in different single cell datasets. (b) Distribution of different cell types and GGH expression in the GSE149652 BLCA dataset. (c) Distribution of different cell types and GGH expression in the GSE139555 CRC dataset. (d)Distribution of different cell types and GGH expression in the GSE149771 CRC dataset. (e) Distribution of different cell types and GGH expression in the GSE118056 MCC dataset. (f) Distribution of different cell types and GGH expression in the GSE121636 KIRC dataset. (g) Distribution of different cell types and GGH expression in the GSE150430 NPC dataset. (JPG 482 KB)

43032_2023_1363_MOESM7_ESM.jpg

Supplementary Figure 7 Prognostic analysis of GGH in pan-cancer. (a-c) Forest plots showing the prognostic analysis between GGH expression levels and pan-cancer PFS (a), DFS (b) and DSS (c). (d) PFS in patients with high versus low GGH expression in endometrial cancer. (e) DFS of patients with high versus low GGH expression in endometrial cancer. (f) DSS in patients with high versus low GGH expression in endometrial cancer. (g) Time-dependent ROC curves of PFS in patients with endometrial cancer. (h) Time-dependent ROC curves between DFS in patients with endometrial cancer. (i)Time-dependent ROC curves of DSS in patients with endometrial cancer. (JPG 841 KB)

43032_2023_1363_MOESM8_ESM.jpg

Supplementary Figure 8 Relationship between GGH expression and prognosis in subgroups with different clinical features of endometrial cancer. (a-o) GGH expression was used to predict survival in subgroups with different clinical characteristics. (a) Clinical stage: Stage I, (b) Clinical stage: Stage II & Stage III & Stage IV, (c) Age:<=60, (d) Age:>60, (e) BMI:<=30, (f) BMI:>30, (g) Histological type: Endometrioid, (h) Histological type: Serous, (i) Histologic grade: Gl, (j) Histologic grade: G2, (k) Histologic grade: G3, (l) Tumor invasion (%) :>=50, (m) Tumor invasion (%) :<50, (n)Diabetes: Yes, (o) Diabetes: No. (JPG 699 KB)

43032_2023_1363_MOESM9_ESM.jpg

Supplementary Figure 9 Utility evaluation of nomogram. (a) ROC curve of residual tumor. (b) ROC curve of radiation therapy. (c) ROC curve of clinical stage. (d) ROC curves of nomogram (Residual tumor, radiation therapy, clinical stage and GGH expression). (e–f) DCA to assess the clinical utility of nomogram (1, 3 and 5 year). (JPG 410 KB)

43032_2023_1363_MOESM10_ESM.jpg

Supplementary Figure 10 Analysis of mutation data of GGH in UCEC. (a) Waterfall plot showing the data of the 30 most common mutated genes and mutations in GGH in UCEC. (b) Cohort summary map showing the distribution of variants according to mutation classification, type and SNV category. The bottom indicates the mutation load of each sample, the type of variant classification. The bar graph shows the top ten mutated genes. (c) Mutation types and structural domains of GGH. (d) 3D structure diagram of GGH. (JPG 370 KB)

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Supplementary Figure 11 WGCNA construction and biological function enrichment analysis. (a) Sample clustering analysis. (b-c) Network topology analysis for different soft threshold powers. (d) Gene dendrograms and module colors. (e) Correlation of 33 modules with different degrees of differentiation. (f) Scatter plot of the dark green module containing GGH. (g) Visual network diagram of the dark green module containing GGH. (h) GO analysis of GGH and its highly related genes. (i) Analysis of GGH and its highly related gene KEGG. (JPG 726 KB)

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Supplementary Figure 12 Relationship between GGH expression in different immune cell subpopulations and prognosis of patients with endometrial cancer. (a) Enriched CD8+ T-cells. (b) Decreased CD8+ T-cells. (c) Enriched CD4+ memory T-cells. (d) Decreased CD4+ memory T-cells. (e) Enriched Regulatory T-cells. (f) Decreased Regulatory T-cells. (g) Enriched Regulatory T-cells. (h) Enriched Natural killer T-cells. (i) Enriched B-cells. (j) Decreased B-cells. (k) Enriched Basophils. (l) Decreased Basophils. (m)Enriched Eosinophils. (n)Decreased Eosinophils. (o) Enriched Macrophages. (p)Decreased Macrophages. (q) Enriched Type 1 T-helper cells. (r) Decreased Type 1 T-helper cells. (s) Enriched Type 2 T-helper cells. (t) Decreased Type 2 T-helper cells. (JPG 621 KB)

43032_2023_1363_MOESM13_ESM.jpg

Supplementary Figure 13 GEO database to validate the correlation between GGH and immune infiltration. (a) PCA analysis of the GSE17025 versus GSE106191 datasets before batch effect correction. (b) PCA analysis of GSE17025 versus GSE106191 dataset after batch effect correction. (c-j) Correlation of GGH expression with immune infiltration (MCPcounter algorithm). (c) T cells, (d) CD8+T cells, (e) Cytotoxic lymphocytes, (f) NK cells, (j) Myeloid dendritic cells, (h) Neutrophils, (i) B lineage, (j) Endothelial cells. (k) Correlation of GGH expression with immunostimulating factors. (l) Correlation of GGH expression with immunosuppressive factors. (JPG 683 KB)

43032_2023_1363_MOESM14_ESM.jpg

Supplementary Figure 14 Relationship between GGH expression and immune infiltration in pan-cancer. (a) Correlation of GGH expression with immune cell infiltration in pan-cancer (xCELL and EPIC algorithms). (b) Radar plot showing the relationship between GGH expression and MSI. (c) Radar plot showing the relationship between GGH expression and TMB. (d) Correlation between GGH expression and pan-cancer chemokines/receptors. (e) Correlation between GGH expression and pan-cancer MHC/Immunoinhibitor/Immunostimulator. (f) Correlation of GGH expression with TIDE score in pan-cancer. (JPG 1257 KB)

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Supplementary Figure 15 Correlation analysis of GGH with key genes in tumor-associated pathways. (a) G2M checkpoint, (b) Cellular response to hypoxia, (c) MYC targets, (d) P53 pathway, (e) Collagen formation, (f) EMT, (g) PI3K/AKT/mTOR pathway, (h) Proliferation, (i) TGFB, (j)DNA replication, (k) Inflammatory response, (l) ECM, (m) Angiogenesis, (n) DNA repair, (o) Degradation of ECM, (p) Genes up-regulated by reactive oxigen species (ROS). (JPG 1135 KB)

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Supplementary Table 2 (XLSX 42 KB)

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Zhu, C., Du, Y., Huai, Q. et al. The Identification of Gamma-Glutamyl Hydrolase in Uterine Corpus Endometrial Carcinoma: a Predictive Model and Machine Learning. Reprod. Sci. 31, 532–549 (2024). https://doi.org/10.1007/s43032-023-01363-0

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