Impact and Predictive Value of Glycolipid Transfer Protein on the Prognosis of Patients with Locally Advanced Cervical Cancer Treated with Neoadjuvant Chemotherapy Combined with Immunotherapy Followed by Radiotherapy

doi:10.12280/gjfckx.20260322

Abstract

Abstract:

Objective: To investigate the role of glycolipid transfer protein (GLTP) in neoadjuvant chemotherapy combined with immunotherapy followed by radiotherapy for locally advanced cervical cancer and its prognostic value. Methods: mRNA and clinical data of 306 cervical cancer patients from The Cancer Genome Atlas (TCGA) and 300 patients from the GSE44001 dataset were screened and downloaded. Weighted gene co-expression network analysis (WGCNA) was used to identify prognosis- and immune-related modules in cervical cancer patients, and the target gene GLTP was selected. Batch effect correction was performed to adjust for batch differences in gene expression data of locally advanced cervical cancer from TCGA and GSE44001, followed by dataset integration. The expression of GLTP mRNA in cervical cancer tissues versus normal tissues was analyzed using TNMplot and GEPIA websites. 41 patients with locally advanced cervical cancer [FIGO stage (2018 edition) ⅠB3-ⅣA] diagnosed and treated at The First Affiliated Hospital of Hebei North University from January 2021 to January 2022 were enrolled to analyze the relationship between GLTP expression and clinicopathological characteristics, as well as prognostic factors. The IOBR software package (MCP-counter method) was employed to analyze the association of GLTP with immune cells and immune checkpoint-related molecules. Results: GLTP was identified as both an immune-related module gene and a prognosis-related gene in cervical cancer from the TCGA database through WGCNA. Analysis via TNMplot and GEPIA showed that GLTP mRNA expression was significantly higher in cervical cancer tissues than that in normal tissues (all P<0.05). Immunohistochemical staining revealed a higher positive-expression rate of GLTP in cervical cancer tissues compared with normal tissues (68.29% vs. 43.90%, P=0.026). Significant differences in PD-L1 expression and objective response were observed between GLTP-positive and GLTP-negative patients (all P<0.05). Multivariate COX regression analysis indicated that patients with an effective objective response (HR=6.096, 95%CI: 2.242-16.573, P<0.001) and positive expression of GLTP (HR=9.115, 95%CI: 3.006-27.639, P<0.001) had a higher cancer-specific survival (CSS) rate after treatment. The overall CSS rate was better in GLTP-positive patients than in GLTP-negative patients (Log-rank P<0.001). Receiver operator characteristic curve analysis showed that GLTP had a certain predictive value for cervical cancer prognosis (the area under the curve is 0.742). Using a cut-off value of 6.67, 266 patients were divided into high- and low-GLTP-expression groups. Spearman correlation analysis revealed a negative correlation between GLTP expression and immune score (r=-0.19, P<0.001). The high-GLTP-expression group exhibited lower numbers of T cells, CD4+T cells, cytotoxic T lymphocytes, natural killer cells, as well as lower expression of immune checkpoint related molecules TIGIT, CD160, LAG3, CD200R1, CD276, CTLA4, CD27, CD40LG, CD244, CD200, CD48 and ICOS, compared with the low-GLTP-expression group (all P<0.05). Conversely, expression of TNFRSF25 and TNFRSF9 were higher in the high-GLTP-expression group (all P<0.05). Conclusions: GLTP is highly expressed in cervical cancer tissues and influences the prognosis of patients receiving neoadjuvant chemotherapy combined with immunotherapy followed by radiotherapy, suggesting its potential as a predictive biomarker for immunotherapy efficacy.

Key words: Uterine cervical neoplasms, Carcinoma, Chemotherapy, adjuvant, Immunotherapy, Radiotherapy, Glycolipid transfer protein, Immune-related markers

LIU Xiao-yu, LU Xiu-rong, LI Ya-ru, ZHAO Wei-wei, ZHANG Fan, CHEN Jiang-ping. Impact and Predictive Value of Glycolipid Transfer Protein on the Prognosis of Patients with Locally Advanced Cervical Cancer Treated with Neoadjuvant Chemotherapy Combined with Immunotherapy Followed by Radiotherapy[J]. Journal of International Obstetrics and Gynecology, 2026, 53(5): 569-578.

Figures/Tables 6

References 27

[1]	Jiang D, Niu Z, Tan X, et al. The mortalities of female-specific cancers in China and other countries with distinct socioeconomic statuses: A longitudinal study[J]. J Adv Res, 2023, 49:127-139. doi: 10.1016/j.jare.2022.09.002.
[2]	Vergote I, Ray-Coquard I, Lorusso D, et al. Investigational drugs for recurrent or primary advanced metastatic cervical cancer: what is in the clinical development pipeline?[J]. Expert Opin Investig Drugs, 2023, 32(3):201-211. doi: 10.1080/13543784.2023.2179483.
[3]	Li S, Gao K, Yao D. Comprehensive Analysis of angiogenesis associated genes and tumor microenvironment infiltration characterization in cervical cancer[J]. Heliyon, 2024, 10(12):e33277. doi: 10.1016/j.heliyon.2024.e33277.
[4]	Li K, Chen J, Hu Y, et al. Neoadjuvant chemotherapy plus camrelizumab for locally advanced cervical cancer (NACI study): a multicentre, single-arm, phase 2 trial[J]. Lancet Oncol, 2024, 25(1):76-85. doi: 10.1016/S1470-2045(23)00531-4. pmid: 38048802
[5]	Li W, Huang Y, Xiao M, et al. PBRM1 presents a potential ctDNA marker to monitor response to neoadjuvant chemotherapy in cervical cancer[J]. iScience, 2024, 27(3):109160. doi: 10.1016/j.isci.2024.109160.
[6]	Nurmi H, Backman A, Halin J, et al. Glycolipid transfer protein knockout disrupts vesicle trafficking to the plasma membrane[J]. J Biol Chem, 2023, 299(4):104607. doi: 10.1016/j.jbc.2023.104607.
[7]	Liu BJ, Li FF, Xie YX, et al. miR-196a Upregulation Contributes to Gefitinib Resistance through Inhibiting GLTP Expression[J]. Int J Mol Sci, 2022, 23(3):1785. doi: 10.3390/ijms23031785.
[8]	Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis[J]. BMC Bioinformatics, 2008, 9:559. doi: 10.1186/1471-2105-9-559. pmid: 19114008
[9]	Wu C, Gong S, Duan Y, et al. A tumor microenvironment-based prognostic index for osteosarcoma[J]. J Biomed Sci, 2023, 30(1):23. doi: 10.1186/s12929-023-00917-3. pmid: 37055822
[10]	Taylor A, Rockall AG, Reznek RH, et al. Mapping pelvic lymph nodes: guidelines for delineation in intensity-modulated radiotherapy[J]. Int J Radiat Oncol Biol Phys, 2005, 63(5):1604-1612. doi: 10.1016/j.ijrobp.2005.05.062.
[11]	Gay HA, Barthold HJ, O'Meara E, et al. Pelvic normal tissue contouring guidelines for radiation therapy: a Radiation Therapy Oncology Group consensus panel atlas[J]. Int J Radiat Oncol Biol Phys, 2012, 83(3):e353-362. doi: 10.1016/j.ijrobp.2012.01.023.
[12]	Chino J, Annunziata CM, Beriwal S, et al. Radiation Therapy for Cervical Cancer: Executive Summary of an ASTRO Clinical Practice Guideline[J]. Pract Radiat Oncol, 2020, 10(4):220-234. doi: 10.1016/j.prro.2020.04.002. pmid: 32473857
[13]	Sun Y, Yang H, Wang J, et al. Cadonilimab plus chemotherapy with or without bevacizumab as first-line treatment for advanced cervical cancer: subgroup analyses from the COMPASSION-16 phase 3 trial[J]. Nat Commun, 2026, 17(1):3931. doi: 10.1038/s41467-026-69965-1.
[14]	Vafina G, Akhiyarova G, Korobova A, et al. The Long-Distance Transport of Jasmonates in Salt-Treated Pea Plants and Involvement of Lipid Transfer Proteins in the Process[J]. Int J Mol Sci, 2024, 25(13):7486. doi: 10.3390/ijms25137486.
[15]	Zeng D, Ye Z, Shen R, et al. IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures[J]. Front Immunol, 2021, 12:687975. doi: 10.3389/fimmu.2021.687975.
[16]	Ma Y, Yang Z, Liu J, et al. CD48 suppresses proliferation and migration as an immune-related prognostic signature in the cervical cancer immune microenvironment[J]. Carcinogenesis, 2024, 45(1/2):57-68. doi: 10.1093/carcin/bgad039.
[17]	Liu B, Xu Y, Hu B, et al. Immune landscape and heterogeneity of cervical squamous cell carcinoma and adenocarcinoma[J]. Aging(Albany NY), 2024, 16(1):568-592. doi: 10.18632/aging.205397.
[18]	Song B, Zheng Y, Chi H, et al. Revealing the roles of glycosphingolipid metabolism pathway in the development of keloid: a conjoint analysis of single-cell and machine learning[J]. Front Immunol, 2023, 14:1139775. doi: 10.3389/fimmu.2023.1139775.
[19]	Shi YL, Liu MB, Wu HT, et al. GLTP Is a Potential Prognostic Biomarker and Correlates with Immunotherapy Efficacy in Cervical Cancer[J]. Dis Markers, 2022, 2022:9109365. doi: 10.1155/2022/9109365.
[20]	Martincuks A, Zhang C, Austria T, et al. Targeting PARG induces tumor cell growth inhibition and antitumor immune response by reducing phosphorylated STAT3 in ovarian cancer[J]. J Immunother Cancer, 2024, 12(4):e007716. doi: 10.1136/jitc-2023-007716.
[21]	Hsia YJ, Lin ZM, Tseng TS, et al. Alpha-Lipoic Acid Inhibits IFN-γ-Induced PD-L1 Expression in Prostate Cancer Cells and Enhances T-Cell-Mediated Anti-Tumor Cytotoxicity[J]. Antioxidants(Basel), 2026, 15(4):413. doi: 10.3390/antiox15040413.
[22]	Cha J, Kim CG, Sim NS, et al. 4-1BB+ Tregs and inhibitory progenitor exhausted T cells confer resistance to anti-PD-L1 and anti-CTLA-4 combination therapy[J]. Cell Rep Med, 2025, 6(10):102408. doi: 10.1016/j.xcrm.2025.102408.
[23]	Geuijen C, Tacken P, Wang LC, et al. A human CD137×PD-L1 bispecific antibody promotes anti-tumor immunity via context-dependent T cell costimulation and checkpoint blockade[J]. Nat Commun, 2021, 12(1):4445. doi: 10.1038/s41467-021-24767-5. pmid: 34290245
[24]	Yang J, Ding X, Fang Z, et al. Association of CD8+TILs co-expressing granzyme A and interferon-γ with colon cancer cells in the tumor microenvironment[J]. BMC Cancer, 2024, 24(1):869. doi: 10.1186/s12885-024-12605-y.
[25]	Zhao W, Wang SQ, Lv H, et al. Current research status and challenges of neoadjuvant immunotherapy for Esophageal squamous cell carcinoma[J]. Biochim Biophys Acta Rev Cancer, 2026, 1881(2): 189572. doi: 10.1016/j.bbcan.2026.189572.
[26]	Guo C, Qu X, Tang X, et al. Spatiotemporally deciphering the mysterious mechanism of persistent HPV-induced malignant transition and immune remodelling from HPV-infected normal cervix, precancer to cervical cancer: Integrating single-cell RNA-sequencing and spatial transcriptome[J]. Clin Transl Med, 2023, 13(3):e1219. doi: 10.1002/ctm2.1219. pmid: 36967539
[27]	Liu L, Wang A, Liu X, et al. Blocking TIGIT/CD155 signalling reverses CD8+ T cell exhaustion and enhances the antitumor activity in cervical cancer[J]. J Transl Med, 2022, 20(1):280. doi: 10.1186/s12967-022-03480-x. pmid: 35729552

参数	GLTP阳性（n=28）	GLTP阴性（n=13）	t或χ²	P
年龄（岁）	48.25±9.97	51.92±10.89	-1.067	0.293
FIGO分期			-	0.541*
ⅠB3	4（14.29）	1（7.69）
ⅡA2/ⅡB	9（32.14）	2（15.38）
Ⅲ	12（42.86）	7（53.85）
ⅣA	3（10.71）	3（23.08）
病理分级			-	0.647*
G1	5（17.86）	2（15.38）
G2	18（64.28）	7（53.85）
G3	5（17.86）	4（30.77）
病理类型			0.000	1.000
鳞癌	21（75.00）	10（76.92）
腺癌	7（25.00）	3（23.08）
淋巴结转移			0.588	0.443
无	22（78.57）	8（61.54）
有	6（21.43）	5（38.46）
PD-L1表达			4.011	0.045
CPS≥10	18（64.29）	4（30.77）
CPS<10	10（35.71）	9（69.23）
HPV亚型			-	0.479*
16/18型	25（89.29）	10（76.92）
52/58 型	2（7.14）	2（15.38）
其他亚型	1（3.57）	1（7.70）
客观缓解			4.958	0.026
有效	19（67.86）	4（30.77）
无效	9（32.14）	9（69.23）

参数	GLTP阳性（n=28）	GLTP阴性（n=13）	t或χ²	P
年龄（岁）	48.25±9.97	51.92±10.89	-1.067	0.293
FIGO分期			-	0.541*
ⅠB3	4（14.29）	1（7.69）
ⅡA2/ⅡB	9（32.14）	2（15.38）
Ⅲ	12（42.86）	7（53.85）
ⅣA	3（10.71）	3（23.08）
病理分级			-	0.647*
G1	5（17.86）	2（15.38）
G2	18（64.28）	7（53.85）
G3	5（17.86）	4（30.77）
病理类型			0.000	1.000
鳞癌	21（75.00）	10（76.92）
腺癌	7（25.00）	3（23.08）
淋巴结转移			0.588	0.443
无	22（78.57）	8（61.54）
有	6（21.43）	5（38.46）
PD-L1表达			4.011	0.045
CPS≥10	18（64.29）	4（30.77）
CPS<10	10（35.71）	9（69.23）
HPV亚型			-	0.479*
16/18型	25（89.29）	10（76.92）
52/58 型	2（7.14）	2（15.38）
其他亚型	1（3.57）	1（7.70）
客观缓解			4.958	0.026
有效	19（67.86）	4（30.77）
无效	9（32.14）	9（69.23）

临床参数	单因素分析		多因素分析
临床参数	HR（95%CI）	P	HR（95%CI）	P
年龄（<53岁 vs. ≥53岁）	1.583（0.785~3.194）	0.200	0.998（0.401~2.483）	0.996
FIGO分期（ⅠB3~ⅡA2/ⅡB vs. Ⅲ~ⅣA）	1.451（0.718~2.933）	0.300	1.563（0.672~3.638）	0.300
病理分级（G1 vs. G2~G3）	0.968（0.332~2.822）	0.952	1.761（0.494~6.276）	0.383
病理类型（腺癌 vs. 鳞癌）	0.830（0.371~1.858）	0.651	0.837（0.312~2.247）	0.724
淋巴结转移（无 vs. 有）	1.367（0.588~3.180）	0.468	1.065（0.350~3.243）	0.911
PD-L1表达（CPS<10 vs. CPS≥10 ）	0.899（0.447~1.811）	0.766	0.787（0.262~2.364）	0.669
HPV亚型（52型/58型/其他 vs. 16/18型）	1.044（0.364~2.998）	0.936	1.837（0.518~6.519）	0.347
客观缓解（有效 vs. 无效）	4.917（1.955~12.369）	<0.001	6.096（2.242~16.573）	<0.001
GLTP表达水平（阳性 vs. 阴性）	5.344（1.402~20.374）	0.014	9.115（3.006~27.639）	<0.001

临床参数	单因素分析		多因素分析
临床参数	HR（95%CI）	P	HR（95%CI）	P
年龄（<53岁 vs. ≥53岁）	1.583（0.785~3.194）	0.200	0.998（0.401~2.483）	0.996
FIGO分期（ⅠB3~ⅡA2/ⅡB vs. Ⅲ~ⅣA）	1.451（0.718~2.933）	0.300	1.563（0.672~3.638）	0.300
病理分级（G1 vs. G2~G3）	0.968（0.332~2.822）	0.952	1.761（0.494~6.276）	0.383
病理类型（腺癌 vs. 鳞癌）	0.830（0.371~1.858）	0.651	0.837（0.312~2.247）	0.724
淋巴结转移（无 vs. 有）	1.367（0.588~3.180）	0.468	1.065（0.350~3.243）	0.911
PD-L1表达（CPS<10 vs. CPS≥10 ）	0.899（0.447~1.811）	0.766	0.787（0.262~2.364）	0.669
HPV亚型（52型/58型/其他 vs. 16/18型）	1.044（0.364~2.998）	0.936	1.837（0.518~6.519）	0.347
客观缓解（有效 vs. 无效）	4.917（1.955~12.369）	<0.001	6.096（2.242~16.573）	<0.001
GLTP表达水平（阳性 vs. 阴性）	5.344（1.402~20.374）	0.014	9.115（3.006~27.639）	<0.001