Expression Characteristics of KRT18 in Ovarian Cancer and Its Association with Patient Survival and Tumor Immune Microenvironment

doi:10.12280/gjfckx.20251496

Abstract

Abstract:

Objective: To systematically analyze the expression profile of keratin 18 (KRT18) in ovarian cancer and its association with patient survival and tumor immune microenvironment based on multi-omics databases. Methods: Transcriptomic data from The Cancer Genome Atlas (TCGA), GTEx, and GEO (GSE66957) databases were integrated to analyze differential expression of KRT18 mRNA and gene. Protein-level validation was performed using the CPTAC proteomics database and immunohistochemistry results from HPA database. Survival analysis was conducted via the Kaplan-Meier Plotter database. The correlation between KRT18 gene expression and tumor-infiltrating immune cells was analyzed using TIMER 3.0. Functional enrichment and protein-protein interaction network analyses were conducted using gene set enrichment analysis (GSEA) and the STRING database. Expression distribution of KRT18 gene expression across different cellular subpopulations was assessed using single-cell transcriptomic data from TISCH2 database. Results: Both KRT18 mRNA, gene, and protein expression levels were significantly higher in ovarian cancer tissues compared to normal ovarian tissues (all P<0.05). Survival analysis showed no statistically significant differences in overall survival and progression free survival between the high- and low-expression groups of the KRT18 gene (all P>0.05). KRT18 gene expression was negatively correlated with the infiltration levels of M1- and M2-type macrophages, neutrophils, na?ve CD8+T cells, memory B cells, and immature plasma cells, and positively correlated with the infiltration level of na?ve B cells (all P<0.05). GSEA indicated that KRT18-related pathways were mainly enriched in antigen processing and presentation-related biological processes, as well as small GTPase signaling pathways. Single-cell transcriptomic analysis showed relatively higher expression of the KRT18 gene in malignant tumor cells and fibroblasts. Conclusions: KRT18 is stably overexpressed in ovarian cancer, and its expression level correlated with the composition of the immune microenvironment, suggesting its potential involvement in ovarian carcinogenesis and progression.

Key words: Keratin-18, Ovarian neoplasms, Carcinoma, Computational biology, Immunity, Cellular microenvironment, Single-cell analysis

WANG Ju-peng, REN Li, MA Ming-kun, ZHAO Ran, WEN Xue-hong. Expression Characteristics of KRT18 in Ovarian Cancer and Its Association with Patient Survival and Tumor Immune Microenvironment[J]. Journal of International Obstetrics and Gynecology, 2026, 53(2): 211-219.

Figures/Tables 8

References 28

[1]	Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3):229-263. doi: 10.3322/caac.21834.
[2]	Siegel RL, Kratzer TB, Giaquinto AN, et al. Cancer statistics, 2025[J]. CA Cancer J Clin, 2025, 75(1):10-45. doi: 10.3322/caac.21871.
[3]	Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022[J]. J Natl Cancer Cent, 2024, 4(1):47-53. doi: 10.1016/j.jncc.2024.01.006.
[4]	Lheureux S, Gourley C, Vergote I, et al. Epithelial ovarian cancer[J]. Lancet, 2019, 393(10177):1240-1253. doi: 10.1016/S0140-6736(18)32552-2. pmid: 30910306
[5]	Colombo N, Sessa C, du Bois A, et al. ESMO-ESGO consensus conference recommendations on ovarian cancer: pathology and molecular biology, early and advanced stages, borderline tumours and recurrent disease?[J]. Ann Oncol, 2019, 30(5):672-705. doi: 10.1093/annonc/mdz062. pmid: 31987337
[6]	Isozaki Y, Sakai K, Kohiro K, et al. The Rho-guanine nucleotide exchange factor Solo decelerates collective cell migration by modulating the Rho-ROCK pathway and keratin networks[J]. Mol Biol Cell, 2020, 31(8):741-752. doi: 10.1091/mbc.E19-07-0357. pmid: 32049581
[7]	Chen B, Xu X, Lin DD, et al. KRT18 Modulates Alternative Splicing of Genes Involved in Proliferation and Apoptosis Processes in Both Gastric Cancer Cells and Clinical Samples[J]. Front Genet, 2021, 12:635429. doi: 10.3389/fgene.2021.635429.
[8]	Menz A, Weitbrecht T, Gorbokon N, et al. Diagnostic and prognostic impact of cytokeratin 18 expression in human tumors: a tissue microarray study on 11,952 tumors[J]. Mol Med, 2021, 27(1):16. doi: 10.1186/s10020-021-00274-7. pmid: 33588765
[9]	Al Haddad M, El-Mezayen HA, El-Kassas M, et al. Clinical Utility of Cytokeratins for Accurate Diagnosis of Hepatocellular Carcinoma Among Hepatitis C Virus High-Risk Patients[J]. Asian Pac J Cancer Prev, 2024, 25(4):1325-1332. doi: 10.31557/APJCP.2024.25.4.1325.
[10]	Nishimoto M, De Velasco MA, Yamamoto Y, et al. Immunohistochemical Analysis of Androgen Receptor Expression Predicts the Prognosis of Metastatic Castration-Sensitive Prostate Cancer Patients Receiving Abiraterone Acetate[J]. Prostate, 2025, 85(7):631-637. doi: 10.1002/pros.24865.
[11]	Elalfy H, Besheer T, Arafa MM, et al. Caspase-Cleaved Cytokeratin 18 Fragment M30 as a Potential Biomarker of Macrovascular Invasion in Hepatocellular Carcinoma[J]. J Gastrointest Cancer, 2018, 49(3):260-267. doi: 10.1007/s12029-017-9937-6. pmid: 28361205
[12]	Hendawy SR, Mansour M, Hamed M, et al. Serum Cytokeratin 18 as a Potential Early Marker for Chemotherapy Response in Breast Cancer Patients: A Prospective Study[J]. Asian Pac J Cancer Prev, 2023, 24(3):969-975. doi: 10.31557/APJCP.2023.24.3.969.
[13]	Kang YJ, Pan L, Liu Y, et al. GEPIA3: Enhanced drug sensitivity and interaction network analysis for cancer research[J]. Nucleic Acids Res, 2025, 53(W1):W283-W290. doi: 10.1093/nar/gkaf423.
[14]	Cui H, Zhao G, Lu Y, et al. TIMER3: an enhanced resource for tumor immune analysis[J]. Nucleic Acids Res, 2025, 53(W1):W534-W541. doi: 10.1093/nar/gkaf388. pmid: 40337924
[15]	Yan J, Yang A, Tu S. The relationship between keratin 18 and epithelial-derived tumors: as a diagnostic marker, prognostic marker, and its role in tumorigenesis[J]. Front Oncol, 2024, 14:1445978. doi: 10.3389/fonc.2024.1445978.
[16]	Gong TT, Guo S, Liu FH, et al. Proteomic characterization of epithelial ovarian cancer delineates molecular signatures and therapeutic targets in distinct histological subtypes[J]. Nat Commun, 2023, 14(1):7802. doi: 10.1038/s41467-023-43282-3.
[17]	林洁, 席晨光, 王玉湘, 等. CD44及CK18在卵巢浆液性癌中的表达及临床意义[J]. 现代妇产科进展, 2016, 25(4):245-248. doi: 10.13283/j.cnki.xdfckjz.2016.04.002.
[18]	Li M, Ma Y, Dai T, et al. Advance in therapies targeting tumor-associated macrophages in ovarian cancer[J]. Front Immunol, 2025, 16:1677839. doi: 10.3389/fimmu.2025.1677839.
[19]	Peng M, Zhu Y, Hu Y, et al. Advances in the regulation of macrophage polarization by the tumor microenvironment[J]. Discov Oncol, 2025, 16(1):1487. doi: 10.1007/s12672-025-03258-9. pmid: 40770163
[20]	Xu C, Chen J, Tan M, et al. The role of macrophage polarization in ovarian cancer: from molecular mechanism to therapeutic potentials[J]. Front Immunol, 2025, 16:1543096. doi: 10.3389/fimmu.2025.1543096.
[21]	Spear S, Le Saux O, Mirza HB, et al. PTEN Loss Shapes Macrophage Dynamics in High-Grade Serous Ovarian Carcinoma[J]. Cancer Res, 2024, 84(22):3772-3787. doi: 10.1158/0008-5472.CAN-23-3890.
[22]	Vledder A, Paijens ST, Loiero D, et al. B cells critical for outcome in high grade serous ovarian carcinoma[J]. Int J Cancer, 2024, 155(12):2265-2276. doi: 10.1002/ijc.35149. pmid: 39175107
[23]	Kasikova L, Rakova J, Hensler M, et al. Tertiary lymphoid structures and B cells determine clinically relevant T cell phenotypes in ovarian cancer[J]. Nat Commun, 2024, 15(1):2528. doi: 10.1038/s41467-024-46873-w. pmid: 38514660
[24]	Chen W, Hu Z, Guo Z. Targeting CD24 in Cancer Immunotherapy[J]. Biomedicines, 2023, 11(12):3159. doi: 10.3390/biomedicines11123159.
[25]	Valdivia A, Avalos AM, Leyton L. Thy-1 (CD90)-regulated cell adhesion and migration of mesenchymal cells: insights into adhesomes, mechanical forces, and signaling pathways[J]. Front Cell Dev Biol, 2023, 11:1221306. doi: 10.3389/fcell.2023.1221306.
[26]	Liu C, Chen S, Zhang Y, et al. Mechanisms of Rho GTPases in regulating tumor proliferation, migration and invasion[J]. Cytokine Growth Factor Rev, 2024, 80:168-174. doi: 10.1016/j.cytogfr.2024.09.002.
[27]	Elhaw AT, Tang PW, Cheng YY, et al. RHOV is a Detachment-Responsive Rho GTPase Necessary for Ovarian Cancer Peritoneal Metastasis[J]. bioRxiv[Preprint], 2025 Aug 13: 2025.08.12.669944. doi: 10.1101/2025.08.12.669944.
[28]	Zhang M, Chen Z, Wang Y, et al. The Role of Cancer-Associated Fibroblasts in Ovarian Cancer[J]. Cancers(Basel), 2022, 14(11):2637. doi: 10.3390/cancers14112637.

分析方法	方向	通路名称	NES	P	FDR
GO-BP	激活	外源性肽抗原的加工与呈递	2.528	0.001 3	0.017 3
		经MHCⅡ类分子介导的外源性肽抗原的加工与呈递	2.509	0.001 4	0.017 3
		外源性抗原的加工与呈递	2.469	0.001 3	0.017 3
		经MHCⅡ类分子介导的肽或多糖抗原的加工与呈递	2.460	0.001 4	0.017 3
		小GTPase介导的信号转导调控	2.390	0.001 0	0.017 3
	抑制	嗅觉感知	-3.886	0.041 7	0.120 3
		γ-氨基丁酸信号通路	-2.241	0.003 6	0.022 2
		味觉感知中化学刺激检测	-2.154	0.004 2	0.024 4
		神经元命运决定	-2.093	0.002 9	0.019 3
		G蛋白偶联受体信号通路（偶联环核苷酸第二信使）	-2.015	0.004 9	0.027 3
KEGG	激活	系统性红斑狼疮	2.351	0.001 2	0.008 5
		移植物抗宿主病	2.314	0.001 3	0.008 5
		利什曼原虫感染	2.310	0.001 2	0.008 5
		同种异体移植排斥反应	2.294	0.001 3	0.008 5
		细胞外基质-受体相互作用	2.289	0.001 2	0.008 5
	抑制	嗅觉转导	-4.174	0.034 5	0.077 9
		青少年发病的成人型糖尿病	-2.285	0.003 8	0.017 0
		亚油酸代谢	-2.261	0.003 9	0.017 0
		神经活性配体-受体相互作用	-2.188	0.022 7	0.057 8
		视黄醇的新陈代谢	-1.780	0.009 3	0.031 0

分析方法	方向	通路名称	NES	P	FDR
GO-BP	激活	外源性肽抗原的加工与呈递	2.528	0.001 3	0.017 3
		经MHCⅡ类分子介导的外源性肽抗原的加工与呈递	2.509	0.001 4	0.017 3
		外源性抗原的加工与呈递	2.469	0.001 3	0.017 3
		经MHCⅡ类分子介导的肽或多糖抗原的加工与呈递	2.460	0.001 4	0.017 3
		小GTPase介导的信号转导调控	2.390	0.001 0	0.017 3
	抑制	嗅觉感知	-3.886	0.041 7	0.120 3
		γ-氨基丁酸信号通路	-2.241	0.003 6	0.022 2
		味觉感知中化学刺激检测	-2.154	0.004 2	0.024 4
		神经元命运决定	-2.093	0.002 9	0.019 3
		G蛋白偶联受体信号通路（偶联环核苷酸第二信使）	-2.015	0.004 9	0.027 3
KEGG	激活	系统性红斑狼疮	2.351	0.001 2	0.008 5
		移植物抗宿主病	2.314	0.001 3	0.008 5
		利什曼原虫感染	2.310	0.001 2	0.008 5
		同种异体移植排斥反应	2.294	0.001 3	0.008 5
		细胞外基质-受体相互作用	2.289	0.001 2	0.008 5
	抑制	嗅觉转导	-4.174	0.034 5	0.077 9
		青少年发病的成人型糖尿病	-2.285	0.003 8	0.017 0
		亚油酸代谢	-2.261	0.003 9	0.017 0
		神经活性配体-受体相互作用	-2.188	0.022 7	0.057 8
		视黄醇的新陈代谢	-1.780	0.009 3	0.031 0