
==== Front
Discov Oncol
Discov Oncol
Discover Oncology
2730-6011
Springer US New York

39264467
1328
10.1007/s12672-024-01328-y
Analysis
Identifying hub genes for chemo-radiotherapy sensitivity in cervical cancer: a bi-dataset in silico analysis
http://orcid.org/0000-0001-8152-0574
Wang Yanhong 1
http://orcid.org/0000-0002-8541-301X
Ouyang Yi 2
http://orcid.org/0000-0002-8521-7465
Cao Xinping 2
http://orcid.org/0000-0001-8931-5786
Cai Qunrong 249211337@qq.com

3
1 https://ror.org/03wnxd135 grid.488542.7 0000 0004 1758 0435 Department of Radiotherapy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000 Fujian China
2 grid.488530.2 0000 0004 1803 6191 State Key Laboratory of Oncology in South China, Department of Radiotherapy, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510600 Guangdong China
3 https://ror.org/03wnxd135 grid.488542.7 0000 0004 1758 0435 Department of Radiotherapy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000 Fujian China
12 9 2024
12 9 2024
12 2024
15 43427 3 2024
9 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Purpose

To identify the hub genes that associated with chemo-radiotherapy sensitivity for cervical cancer and to explore the relationship between hub genes and various cellular processes and potential mechanism of cervical cancer.

Methods

The gene expression data of 21 patients with CESC and the mRNA expression profiles of 296 patients with CESC were obtained from the Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) databases, respectively. The potential functions and regulatory mechanisms of differentially expressed genes (DEGs) were identified using GO and KEGG enrichment analyses. Hub genes were identified using random survival forest analysis. The relationship between hub genes and various cellular processes was comprehensively analyzed. The expression of hub genes was assessed using clinical data extracted from the Human Protein Atlas (HPA) database.

Results

A total of 139 and 13 DEGs were found to be upregulated and downregulated, respectively, in CESC. The six hub genes, namely, SELP, PIM2, CCL19, SDS, NRP1, and SF3A2, were significantly correlated with immune cell infiltration, chemotherapy sensitivity, disease-related genes, and enriched signaling pathways (all p-value < 0.05). A nomogram and calibration curve were generated using the six hub genes to predict prognosis with high accuracy. A regulatory network comprising TFs (ZBTB3) and mRNAs (NRP1/PIM2/SELP) and several competitive endogenous RNA (ceRNA) networks comprising mRNAs, miRNAs, and lncRNAs were constructed. Data from HPA indicated that the protein expression of the six hub genes differed significantly between patients with CESC and healthy individuals.

Conclusion

Upregulation of SELP, PIM2, CCL19, SDS, NRP1, and SF3A2 is associated with radiotherapy sensitivity and is involved in various cellular processes in CESC. These six genes may serve as biomarkers for predicting the radiotherapy response and prognosis in patients with CESC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-024-01328-y.

Keywords

Cervical cancer
Chemo-radiotherapy sensitivity
Hub genes
Signaling pathways
Nomogram
Fujian Provincial Natural Science Foundation Projects2020J01210 Wang Yanhong issue-copyright-statement© Springer Science+Business Media, LLC 2024
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pmcBackground

Cervical and endocervical cancer (CESC) was the fourth most common cancer in women and the fourth leading cause of cancer-related deaths in women worldwide, with 604,127 new cases and 341,831 deaths reported in 2020 [1]. The treatment of cervical cancer depends on the disease stage at diagnosis. For patients with early-stage cervical cancer (International Federation of Gynecology and Obstetrics [FIGO] stages IB–IIA), radical radiotherapy is considered an effective alternative to radical surgery, with comparable disease-free survival (DFS) and 5-year overall survival (OS) [2]. Although radical radiotherapy is effective in the management of early-stage cervical cancer, local or pelvic recurrence may occur in 16.7% of cases [2]. Radical radiotherapy is recommended as the preferred treatment strategy for locally advanced cervical cancer (FIGO stages IB3–IVA) [3–5]. At the end of radiotherapy, < 70% of patients with locally advanced cervical cancer achieve a complete response (CR), whereas only 61.8% of patients exhibit locoregional control (LRC) at the last follow-up [3]. To date, substantial efforts have been made to develop strategies for predicting the response of patients with CESC to radiotherapy and improving chemo-radiotherapy sensitivity. For instance, concurrent radiotherapy and cisplatin-based chemotherapy has been used to increase the CR and LRC rates by 10.2% and 8.4%, respectively, in patients with locally advanced cervical cancer. Despite the concurrent use of chemotherapy and radiotherapy, some patients do not successfully achieve a CR or satisfactory LRC, eventually developing tumor recurrence or metastasis [6].

Radiotherapy leads to DNA damage in tumor cells directly by inducing double-strand breaks (DSBs) or indirectly by generating reactive oxygen species and free radicals. Administration of cisplatin during radiotherapy can enhance radiosensitivity by suppressing the ability of tumor cells to repair DNA damage. However, the main challenge associated with the use of radiotherapy is the resistance of tumor cells to radiation. Studies have shown that the Wnt/β-catenin signaling pathway plays a role in enhancing DNA damage repair. Upregulated NEK2 can trigger the activation of Wnt1, thereby activating the Wnt/β-catenin signaling pathway and consequently resulting in the development of radiotherapy resistance in cervical cancer [7]. The PIK3CA–E545K–SIRT4 axis controls the response of cervical cancer cells to radiation therapy by regulating glutamine metabolism [8]. Other mechanisms involved in the response to DNA damage that contributes to radiotherapy resistance include the activation of DNA damage sensors, early events in signal transduction, and induction of cell cycle arrest [9]. Clinical factors such as FIGO stages; human papillomavirus (HPV) status; histological and biomolecular markers including DNA methylation, hypoxia, and metabolism; tumor microenvironment (TME); cancer stem cells; microRNAs; and lncRNAs can be used to predict radiotherapy response [10]. However, most existing biomarkers lack both sensitivity and specificity.

Immunotherapy has positively transformed the management of cancer. Radiation induces cellular inflammation, resulting in the activation of tumor-specific effector T cells. Immunotherapy can enhance this effect by decreasing the suppressive influence on TME, leading to a synergistic antitumor immune response [11]. Therefore, the combination of radiotherapy and immunotherapy can enhance antitumor immunity by converting an immune-resistant tumor to an immune-responsive tumor. The phase I/II NiCOL trial reported an overall response rate (ORR) of 93.8% at 2 months following brachytherapy in patients with stage IB3–IVA cervical cancer who had undergone concurrent and maintenance nivolumab therapy with chemoradiotherapy [12]. However, the exact reason for the superior effects of the combination of radiotherapy and immunotherapy in CESC remains elusive.

Identifying patients with cervical cancer at a high risk of weight gain owing to CRT remains challenging. Moreover, the molecular mechanisms contributing to radiotherapy resistance in cervical cancer remain elusive. In this study, differentially expressed genes (DEGs) between patients with cervical cancer with a CR to radiotherapy and those with a non-CR were identified and comprehensively analyzed. Additionally, the relationship between key genes and immune cell infiltration and the mechanisms underlying this relationship were investigated. The findings of this study reveal promising biomarkers for predicting the radiotherapy response and prognosis in patients with cervical cancer undergoing chemoradiotherapy.

Methods

Datasets and data collection

The mRNA expression data of patients with CESC were extracted from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) and The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) databases. Given that concurrent chemoradiotherapy is the established therapeutic modality for locally advanced cervical cancer, we selected a cohort of patients who underwent this treatment regimen for our study. The GSE56363 dataset from GEO was downloaded and annotated using the GPL4133 platform. It comprised 21 patients with cervical cancer, including 12 patients with a CR and 9 patients with a non-CR to radical radiotherapy. The clinical and mRNA expression data of 296 CESC tissue samples and 3 normal tissue samples were extracted from TCGA (TCGA-CESC cohort). All data were extracted from online public resources and processed to identify DEGs.

Analysis of DEGs using functional enrichment methods

The Metascape database (https://metascape.org) was used to investigate functional interactions within DEGs. In Gene Ontology (GO) analysis, a minimum overlap of 3 genes and a p-value of ≤ 0.01 indicated statistically significant results. The STRING database (https://cn.string-db.org) and Cytoscape software were used to generate a protein–protein interaction (PPI) network of DEGs.

Identification of hub genes using random survival forest analysis

The “RandomForestSRC” package was used to identify key genes associated with survival in the TCGA-CESC cohort. A random survival forest algorithm was used to evaluate the predictive importance of the key genes, with the Monte Carlo simulation involving 1000 iterations. Genes with relative importance values of > 0.4 were considered final hub genes.

Relationship between immune cell infiltration and hub genesThe diagnosis, prognosis, and treatment of malignant tumors are greatly influenced by the TME, which comprises tumor-associated fibroblasts, immune cells, extracellular matrix, cancer cells, and various other components. To identify the potential molecular mechanisms through which the hub genes promote the development of cervical cancer, we examined the correlation between the expression of hub genes and the abundance of tumor-infiltrating immune cells. The CIBERSORT algorithm was used to determine the proportion of 22 types of tumor-infiltrating immune cells in two groups (normal and tumor) in the TCGA-CESC cohort. The TISIDB database was used to evaluate the relationship between the expression of immune cell-related genes and hub genes via Pearson correlation analysis.

Drug sensitivity analysis

The established therapeutic regimen for advanced cervical cancer is concurrent radiotherapy with cisplatin-based chemotherapy [5]. The Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/) was used to assess the sensitivity of chemotherapeutic medications based on the expression of hub genes. The “pRRophetic” R package was used to predict the sensitivity of each tumor sample to chemotherapy, and regression analysis was used to estimate the half-maximal inhibitory concentration (IC50) of each chemotherapeutic drugThe training set was subjected to tenfold cross-validation to assess the predictive accuracy of hub genes. Standard parameters, including the “combat” parameter, were used to eliminate batch effects and calculate the average gene expression across replicates.

Identification of disease-related genes

The GeneCards database (https://www.genecards.org/) provides data on genes, genetic variants, related diseases, and phenotypes, serving as an extensive resource for gene-based investigation [13]. Genes associated with the development of CESC were obtained from the GeneCards database. The expression of these genes was compared between the control and CESC groups. Subsequently, the correlation between six hub genes and genes associated with the development of CESC was examined.

Gene set variation analysis and gene set enrichment analysis

Gene set variation analysis (GSVA) is an unsupervised and nonparametric method used to assess the enrichment of gene sets in the transcriptome. It offers insights into the biological functions of genes by converting gene-level modifications to pathway-level modifications. To compare biological functions between different groups, gene sets were downloaded from the Molecular Signatures Database (https://www.gsea-msigdb.org/gsea/msigdb) and GSVA was performed to evaluate each gene set.

Furthermore, gene set enrichment analysis (GSEA) (http://www.broadinstitute.org/gsea) was used to detect genes that exhibited significant differences in expression between the low- and high-expression groups based on the expression profiles of patients with CESC. The gene sets had a size of 100–200 genes. A p-value of < 0.05 and a false discovery rate (FDR) of 0.25 indicated significant enrichment in a gene set after 10,000 permutations.

Development of a nomogram and calibration curve in the TCGA-CESC cohort

Nomograms have emerged as a valuable tool for predicting the prognosis of cancer owing to their ability to integrate statistical predictive models into a singular numerical estimate of the probability of an event, such as mortality or relapse, which is specifically customized to individual patient attributes [14]. In this study, a nomogram was constructed based on the expression patterns of six hub genes (SELP, PIM2, CCL19, SDS, NRP1, and SF3A2) and clinical factors including age, race, histological type, and FIGO stage in the TCGA-CESC cohort. Calibration curves were plotted to evaluate the performance of the nomogram in predicting 1-, 3-, and 5-year overall survival.

Prediction of transcription factors and construction of a ceRNA network of hub genes

The “RcisTarget” R package was used to predict transcription factors (TFs). All calculations were conducted using motifs, and the normalized enrichment scores (NESs) of motifs were depended on the total number of motifs in the database. In addition to motif annotations provided by the source data, additional annotation files were created using motif similarity and gene sequence. To assess the increased expression of every motif in a gene set, the area under the curve (AUC) value of each motif pair was calculated based on the recovery curves of gene sets for motif arrangement. Subsequently, the NES of each motif was computed based on the distribution of AUC values within the gene set. A file named “Rcistarget-hg19-tss-centered-10 kb-7species.mc9nr.feather” was used for gene-motif ranking.

CESC-related microRNAs (miRNAs) were obtained from the Human MicroRNA Disease Database (HMDD) (http://www.cuilab.cn/hmdd). Subsequently, mRNA–miRNA regulatory pairs involving the mRNAs of the six hub genes were obtained from the miRWalk database (http://mirwalk.umm.uni-heidelberg.de/). Only disease-associated mRNA–miRNA regulatory pairs were retained. Finally, long noncoding RNAs (lncRNAs) interacting with screened miRNAs were predicted using the ENCORI database (http://starbase.sysu.edu.cn/index.php).

Genome-wide association study

The Gene Atlas database (http://geneatlas.roslin.ed.ac.uk/) is a comprehensive repository that documents the relationship between numerous traits and genetic variants from the UK Biobank cohort. The pathogenic loci of the six hub genes in CESC were identified by analyzing genome-wide association study (GWAS) datasets from the Gene Atlas database.

Validation of the protein expression of hub genes using data from the Human Protein Atlas

The Human Protein Atlas (HPA) database (https://www.proteinatlas.org/) is an online resource that provides information regarding the localization and expression of proteins in both healthy and cancerous human tissues. To validate the protein expression of the six hub genes, the immunohistochemical (IHC) data of cervical cancer and normal cervical tissues were extracted from the HPA database.

Results

Identification of DEGs and their functional enrichment analysis

The “limma” package was used to identify DEGs in the GEO dataset GSE56363 based on the screening criteria of p-values of < 0.05 and log2FC values of 1.5. A total of 152 DEGs were identified between patients with CESC with a CR and those with a non-CR to radiotherapy. Of the 152 DEGs, 13 genes were downregulated, whereas 139 genes were upregulated. Figure 1A and B show a volcano plot and heatmap demonstrating DEGs, respectively. Online Resource Table 1 shows all identified DEGs.Fig. 1 Volcano plot and heatmap of differentially expressed genes (DGEs) between patients with CESC with a complete response (CR) and those with a non-complete response (non-CR) to radiotherapy A Volcano plot of DEGs. Grey represents non-significant differentially expressed genes, green represents genes with log2FC values of > 1.5 or < − 1.5 but p-values of > 0.05, blue represents genes with p-values of < 0.05 but log2FC values of > − 1.5 and < 1.5, and red represents genes with log2FC values of > 1.5 or < − 1.5 and p-values of < 0.05. B Heatmap of DEGs between patients with CESC with a CR (aquamarine) and those with a non-CR (pink-orange) to radiotherapy. Blue represents a low expression level, whereas red represents a high expression level

GO and KEGG enrichment analyses were performed to investigate the biological functions and pathways of DEGs. The results indicated that the DEGs were primarily enriched in pathways related to the extracellular matrix, components of the extracellular matrix that provide elasticity, and heparin binding (Online Resource Fig. 1). The results of PPI analysis showed that the interactions among DEGs were both intricate and compact (Online Resource Fig. 2).

Random survival forest analysis of DEGs in the TCGA-CESC cohort and identification of six hub genes

Random survival forest analysis revealed seven genes associated with the prognosis of CESC, namely, SELP, PIM2, CCL19, SDS, PRR3, NRP1, and SF3A2 (Fig. 2A and B). Kaplan–Meier analysis showed that all genes except PRR3 were significantly associated with survival (Fig. 2D–I). In particular, higher expression of SELP, PIM2, CCL19, SDS, and SF3A2 (p < 0.05 for all) and lower expression of NRP1 (p = 0.041) were significantly associated with longer OS. The expression of six hub genes was significantly higher in the CR group than in the non-CR group in the GSE56363 dataset (all p < 0.05, Fig. 2C).Fig. 2 Random survival forest analysis of DEGs in the TCGA-CESC cohort A Random survival forest analysis of DEGs. B Seven final genes with variable relative importance values of ≥ 0.4. C The expression of six hub genes was significantly higher in the CR group than in the non-CR group (*p-values of < 0.05, **p-values of < 0.01, and ***p-values of < 0.001). D Kaplan–Meier analysis showed that higher expression of SELP, PIM2 E, CCL19 F, SDS G, and SF3A2 I was significantly associated with longer overall survival, whereas higher expression of NRP1 H was associated with shorter OS (p < 0.05 for all)

Relationship between hub genes and tumor-infiltrating immune cells

Figure 3A and B show the distribution of 22 types of tumor-infiltrating immune cells in each sample and the relationship among these cells, respectively. As shown in Fig. 3C, the proportion of M0 macrophages, follicular helper T cells, and M1 macrophages was significantly higher in patients with CESC than in healthy individuals. The expression of PIM2, SDS, and SELP was significantly correlated with the abundance of tumor-infiltrating immune cells (Fig. 3D). In particular, PIM2 expression was positively correlated with the proportion of plasma cells, CD8 T cells, activated memory CD4 T cells and negatively correlated with the proportion of resting memory CD4 T cells and activated dendritic cells. SDS expression was positively correlated with the proportion of activated memory CD4 T cells and M1 macrophages and negatively correlated with the proportion of resting memory CD4 T cells and activated mast cells. SELP expression was positively correlated with the proportion of resting mast cells (Fig. 3D).Fig. 3 Relationship between hub genes and tumor-infiltrating immune cells in the TCGA-CESC cohort A Proportion of immune cells in the control and CESC groups. Red, control group; dark red, CESC group. B Interaction analysis of 22 types of immune cells in CESC (Pearson correlation coefficients are shown for significant correlations).C Comparison of immune cells between the control and CESC groups (*p-values of < 0.05, **p-values of < 0.01, and ns represents p-values of > 0.05 between the two groups). D Bubble map demonstrating the correlation between hub genes and tumor-infiltrating immune cells (Pearson correlation coefficients are shown for significant correlations)

Furthermore, the relationship between hub genes and various immune-related genes, including chemokine-, immunosuppression-, MHC-, immunostimulation-, and receptor-related genes, was evaluated using the TISIDB database. As shown in Online Resource Fig. 3, the expression of the six hub genes was either positively or negatively correlated with that of various immune-related gens.

Response to chemotherapeutic drugs based on the expression of hub genes

Common chemotherapeutic drugs, including cisplatin, docetaxel, fluorouracil, and paclitaxel, were selected for drug sensitivity analysis. The expression of SELP, SDS, and CCL19 was significantly correlated with lower IC50 values of the aforementioned drugs, whereas the expression of NRP1 was significantly correlated with higher IC50 values of the drugs (p < 0.05 for all) (Online Resource Fig. 4). On the contrary, PIM2 did not affect the sensitivity to fluorouracil (p = 0.11), and SF3A2 did not affect the sensitivity to docetaxel (p = 0.33).

Relationship between the six hub genes and disease-related genes

The expression of numerous disease-related genes was significantly different between the control and CESC groups. These genes included RAD51, UHRF1, FANCI, BRCA2, PDGFRA, POLE, CDKN2A, ERBB3, IDH2, TGFBR2, HTATIP2, SOX5, SOX9, NRAS, FGFR3, SMAD4, HRAS, ATR, PTEN, PIK3R1, PTCH1, and ERBB2 (Fig. 4A). Pearson correlation analysis indicated a strong positive correlation between the expression of the six hub genes and that of the screened disease-related genes. As shown in Fig. 4B, the expression of SELP and NRP1 was positively correlated with that of PIK3R1, PDGFRA, SOX5, and TGFBR2; the expression of PIM2, SDS, and SF3A2 was positively correlated with that of ATR, IDH2, CDKN2A, POLE, and RAD51; and the expression of CCL19 was positively correlated with that of FGFR3 and PDGFRA.Fig. 4 Relationship between hub genes and disease-related genes A Differences in the expression of multiple disease-related genes between the control and CESC groups (*p-values of < 0.05 and **p-values of < 0.01). B Bubble map demonstrating the correlation between the six hub genes and disease-related genes (Pearson correlation coefficients are shown for significant correlations)

Signaling pathways associated with the six hub genes

Furthermore, we identified specific signal transduction pathways associated with the six hub genes and evaluated the potential effects of the genes on the identified pathways during CESC development. Figure 5A–C and Online resource Fig. 5A–C show enriched pathways identified through GSVA of the six hub genes. The results indicated that upregulated SELP was primarily enriched in pathways associated with abnormal vascular physiology and CD8 + α-β T cell differentiation. Upregulated PIM2 was primarily enriched in pathways associated with the regulation of cell death, stress-activated protein kinase signaling cascade, double-strand break sites, and PRP19 complex. Upregulated CCL19 was involved in the NF-kappa B pathway-induced kinase activity, whereas downregulated CCL19 was involved in the negative regulation of the cellular response to hypoxia. Upregulated SDS was primarily enriched in pathways associated with the regulation of mast cell activation involved in immune response, regulation of mononuclear cell migration, leukocyte activation involved in the inflammatory response, and granzyme-mediated programmed cell death signaling. Upregulated NRP1 was associated with protein localization to the cell leading edge. Downregulated SF3A2 was primarily enriched in pathways associated with the regulation of cilium-dependent cell motility, forelimb morphogenesis, and ERBB4 signaling, whereas upregulated SF3A2 was associated with the regulation of cysteine endopeptidase activity involved in the apoptotic signaling pathway.Fig. 5 GSVA and GSEA of the six hub genes A GSVA of SELP. B GSVA of PIM2. C GSVA of SDS. D GSEA of SELP. E GSEA of PIM2. F GSEA of SDS

Furthermore, GSEA showed that upregulated SELP, PIM2, CCL19, SDS, and SF3A2 were enriched in pathways associated with allograft rejection and graft-versus-host disease (Fig. 5D–F and Online resource Fig. 5D–F).

Establishment of a nomogram and calibration curve to predict the OS of patients with CESC

Cox analysis revealed that clinical variables and the expression of the six hub genes played distinct roles in the CESC scoring process. As the total score of these characteristics increases, the probability of 1-, 3-, and 5-year OS decreases (Fig. 6A). The calibration curve showed that the predicted survival rates were in good agreement with the actual outcomes (Fig. 6B).Fig. 6 Nomogram for predicting the overall survival of patients with CESC A A nomogram was constructed based on the expression of the six hub genes and clinical factors. B Calibration curve of the nomogram for predicting 1-, 3-, and 5-year OS in the TCGA-CESC cohort

Regulatory network analysis and ceRNA network analysis of hub genes

The six hub genes, namely, SELP, PIM2, CCL19, SDS, NRP1, and SF3A2, were found to be regulated by various TFs. To investigate the regulatory mechanisms of these genes, motif enrichment analysis was performed on predicted TFs (Fig. 7A). The jaspar_MA0067.1 motif had the highest NES at 7.25 (Fig. 7B), followed by cisbp_M189 with an NES of 7.23 and predrem_nrMotif423 with an NES of 7.15 (Fig. 7C and D). NRP1, PIM2, and SELP were enriched in jaspar_MA0067.1, with no upstream TFs. A portion of the enriched motifs and their respective TFs for the hub genes are shown in Fig. 7E.Fig. 7 Regulatory network analysis of hub genes A Enrichment analysis of transcription factors of hub genes using the “RciTarget” package. B The jaspar_MA0067.1 motif. C The cisbp_M1895 motif. D The predrem_nrMotif423 motif. E Enriched motifs and corresponding TFs of hub genes

A total of 331 CESC-related miRNAs were identified using the HMDD database, and a total of 1140 mRNA–miRNA pairs for the six hub genes were identified using the miRWalk database. Only mRNA–miRNA pairs involving CESC-related miRNAs were retained, resulting in the inclusion of 15 miRNAs and 4 mRNAs (Fig. 8A). Furthermore, a total of 301 miRNA–lncRNA pairs, involving 9 miRNAs and 212 lncRNAs, were identified using the ENCORI database. The results showed that SELP was regulated by hsa-miR-107 and hsa-miR-665; PIM2 was regulated by hsa-miR-665, hsa-miR-4428, and hsa-miR-320c; CCL19 was regulated by hsa-miR-326 and hsa-miR-543; and NRP1 was regulated by hsa-miR-449a, hsa-miR-613, and hsa-miR-1270. Complex ceRNA networks involving miRNAs and lncRNAs were constructed using the Cytoscape software (Fig. 8B).Fig. 8 ceRNA network analysis of hub genes A A total of 366 miRNAs related to CESC were obtained from HMDD, and 1140 miRNAs related to the six hub genes were obtained from the miRWalk database. B ceRNA networks involving mRNAs, miRNAs, and lncRNAs

GWAS analysis of hub genes

Because the Gene Atlas database does not contain information on cervical cancer, we used the C51-C58 malignant neoplasms of female genital organs as a trait for GWAS analysis in this study (Fig. 9A, B). The pathogenic single nucleotide polymorphisms (SNPs) corresponding to SELP, CCL19, SDS, NRP1, and SF3A2 genes are shown in Fig. 9C–G. The results showed that the pathogenic SNPs of SELP, CCL19, SDS, NRP1, and SF3A2 were located on chromosomes 1, 9, 12, 10, and 19, respectively. Data on PIM2 were not found in the Gene Atlas database.Fig. 9 Results of GWAS analysis A Q-Q plot of GWAS analysis. B Manhattan plot of the GWAS analysis. C The pathogenic region of SELP was located on chromosome 1. D The pathogenic region of CCL19 was located on chromosome 9. E The pathogenic region of SDS was located on chromosome 12. F The pathogenic region of NRP1 was located on chromosome 10. G The pathogenic region of SF3A2 was located on chromosome 19

Validation of the expression of the six hub genes in clinical samples

Figure 10 shows the results of immunohistochemical staining for SELP, PIM2, CCL19, SDS, NRP1, and SF3A2 in cervical tissues obtained from the HPA database. SELP was not expressed in normal cervical tissues, and its expression was either undetected or at a moderate level in cervical cancer tissues. PIM2 was not expressed in normal cervical tissues, and its expression was either undetected or low in cervical cancer tissues. CCL19 was expressed in neither normal cervical tissues nor cervical cancer tissues. SDS showed moderate expression in normal cervical tissues, whereas its expression was either undetected or at a low-to-moderate level in cervical cancer tissues. NRP1 exhibited low expression in normal cervical tissues, and its expression was either undetected or at a low-to-moderate level in cervical cancer tissues. SF3A2 showed moderate expression in normal cervical tissues and moderate-to-high expression in some parts of cervical cancer tissues. The varying expression patterns of the hub genes may account for the innate biological differences and differences in the response to radiotherapy among patients with CESC.Fig. 10 Protein expression of SELP, PIM2, CCL19, SDS, NRP1, and SF3A2 in normal cervical tissues and cervical cancer tissues from the HPA database

Discussion

Several studies have demonstrated the clinical validity of multi-gene expression models of tumor radiosensitivity [15]. An in-depth understanding of the mechanisms driving radiotherapy sensitivity is crucial for improving the treatment of cervical cancer. To date, no studies have investigated the TF–miRNA–mRNA regulatory network involved in radiotherapy resistance in patients with CESC using clinical specimens. The workflow of this study is presented in Online Resource Fig. 6.

In this study, the limma package was used to identify DEGs between patients with CESC with a CR and those with a non-CR to radiotherapy in the GEO dataset GSE56363. The results showed that 13 genes were upregulated and 139 genes were downregulated in the CR group. Functional enrichment analysis showed that the DEGs were significantly enriched in several biological pathways related to cancer. Additionally, PPI analysis showed that the interactions among DEGs were complex. Random survival forest analysis revealed seven prognosis-associated genes, namely, SELP, PIM2, CCL19, SDS, PRR3, NRP1, and SF3A2, in the TCGA-CESC cohort. SELP, PIM2, CCL19, SDS, NRP1, and SF3A2 were found to be significantly associated with the OS of patients with CESC. Therefore, these six genes were identified as hub genes. The expression of the six hub genes was significantly correlated with the proportion of tumor-infiltrating immune cells, expression of immune-related genes, and sensitivity to chemotherapeutic drugs. In addition, the expression of the six hub genes was associated with that of numerous CESC-related genes, such as BRCA2, POLE, IDH2, TGFBR2, PTEN, PIK3R, and ERBB2. GSVA and GSEA showed that the hub genes might influence various signaling pathways associated with the development and progression of CESC. For example, SELP was found to be involved in a pathway related to abnormal vascular physiology, and PIM2 was found to be involved in pathways associated with the regulation of cell death, regulation of the stress-activated protein kinase signaling cascade, and double-strand break sites. Furthermore, a nomogram integrating hub genes and clinical factors was established to predict the prognosis of patients with CESC. The calibration curve showed that the nomogram exhibited significant accuracy in predicting the OS of patients with CESC. Regulatory network analysis revealed that various TFs regulated the six hub genes. Subsequently, a regulatory network involving TFs (ZBTB3) and mRNAs (NRP1/PIM2/SELP) and ceRNA networks involving mRNAs, miRNAs, and lncRNAs were constructed (Fig. 8B). GWAS analysis showed that the pathogenic sites of SELP, CCL19, SDS, NRP1, and SF3A2 were located on chromosomes 1, 9, 12, 10, and 19, respectively. The HPA database was used to validate the expression of the six hub genes in cervical cancer. The findings showed that the expression patterns of the six hub genes were significantly different among cervical cancer tissues, indicating potential differences in chemo-radiotherapy sensitivity among patients with cervical cancer. Our data suggested that these six hub genes may predict the sensitivity of cervical cancer following chemo-radiotherapy, and might identify gene targets that could be used to enhance the efficacy of chemo-radiotherapy in cervical cancer.

Clinicopathological factors including age, race, marital status, tumor size, primary site surgery, FIGO stage, histology are related to the prognosis of cervical cancer [16, 17]. In our study, through nomogram analysis, the clinicopathological factors age, race, FIGO stage, histology and expression of six hub genes were identified as independent clinical prognostic factors. Kaplan–Meier analysis showed that the expression of each hub gene was significantly correlated with survival. These data suggest thet these six hub genes are of great significance and could be used as a diagnostic indicator for the prognostic of chemo-radiotherapy.

Immune cells within the tumor microenvironment are associated with tumor radiosensitivity [18, 19]. Our analysis showed that PIM2 was positively correlated with plasm cells, CD8 T cells, CD4 T cells activated, and negatively correlated with CD4 T cell resting. SDS also positively correlated with CD4 T cells activated, Macrophages M1 and M2 (Fig. 3D). GSVA enrichment analysis showed that hub genes were primarily enriched in pathways associated with CD8 + α-β T cell differentiation, regulation of mast cell activation involved in immune response, regulation of mononuclear cell migration, leukocyte activation involved in the inflammatory response (Fig. 5A and C). Furthermore, the expression of the six hub genes was either positively or negatively correlated with that of various immune-related gens, including chemokine-, immunosuppression-, MHC-, immunostimulation-, and receptor-related genes (Online Resource Fig. 3). All abovementioned result indicated that these six hub genes may reflect the immune cell infiltration status of tumors and provide value to disease prognosis.

A clinical trial investigating the efficacy of chemoradiation plus immunotherapy in locally advanced CESC showed that the ORR was higher in patients treated with chemoradiation plus immunotherapy than in those treated with traditional CCRT. In particular, the ORR of patients treated with chemoradiation plus immunotherapy was 93.8% at 2 months after brachytherapy [12]. However, the mechanism of action of concurrent radiotherapy and immunotherapy remains unclear. In this study, the hub genes involved in radiotherapy response exhibited a notable association with tumor-infiltrating immune cells and immune-related genes. This correlation potentially elucidates the fundamental mechanism of action of concurrent radiotherapy and immunotherapy.

SELP is related to immune system and DNA repair and replication, and shows a strong correlation with the prognosis in gastric cancer and ovarian cancer. PIM2 is commonly upregulated in various human malignancies and has been associated with multiple important aspects of cancer, such as cell viability and growth, programmed cell death, infiltration, and metastasis [20]. In ovarian cancer cells, the overexpression of PIM2 triggered cell resistance to DNA damage agents, such as platinum-based chemotherapy [21]. Given that radiotherapy also relies on causing DNA damage to kill tumor cells, this may be one of the potential reasons for the association between PIM2 and sensitivity to radiotherapy. In our GSVA analysis, PIM2 was enriched in the pathway of associated with the regulation of cell death [22], stress-activated protein kinase signaling cascade [23], double-strand break sites [24], and PRP19 complex [25]. These pathways have been previously shown to impact radiosensitivity. CCL19 is involved in malignant transformation of cervical cancer [26] and is an indicator of prognosis for HPV-positive cervical cancer [27]. In patients with advanced colorectal cancer who exhibit a favorable response to the combination of SBRT and PD-L1 inhibitor, CCL19 expression was significantly upregulated [28], which is similar to the findings of our study. As showed in GSVA analysis, downregulated CCL19 was involved in the negative regulation of the cellular response to hypoxia. Hypoxia can confer radioresistance through cellular and tumor micro-environment adaptations [29]. NRP1 was upregulated in tumor tissues and was significantly associated with cell proliferation, migration, and invasion and unfavorable survival outcomes of cervical cancer [30, 31]. NRP1 was proved to associated with radiosensitivity of esophageal squamous cell carcinoma and non-small cell lung carcinoma [32, 33]. The role of SDS in tumorigenesis and radiation sensitivity remains unclear. Recent research showed that SF3A2 was frequently overexpressed in TNBC tissues and accelerated TNBC progression and cisplatin resistance through regulation of both extrinsic and intrinsic apoptosis [34], which maybe the potential mechanism for radiosensitivity. Cervical cancer cells have a high mutation frequency, with a significantly higher somatic mutation burden than that observed in paired adjacent paracancerous cervical cells [35]. Our analysis also showed that the expression of numerous disease-related genes was significantly different between the normal cervical tissue and cervical cancer tissue (Fig. 4A). Cancer cells exhibit inherited differences [36]. In addition, antitumor drug sensitivity can be significantly different even among cells with closely related genotypes within the same tumor [37]. In this study, IHC data extracted from the HPA database showed that the expression of the six hub genes varied in each cervical cancer tissue sample and pancancerous tissue (Fig. 10), suggesting that these genes may be related to CESC development. This was validated by Pearson correlation analysis between hub genes and disease-related genes (Fig. 4B).

MiRNAs and lncRNAs play crucial roles in gene regulation and cancer biology [38]. miRNAs, which are small noncoding RNAs, interact with mRNAs to regulate their expression. The lncRNA–miRNA regulatory network, in which lncRNAs function as ceRNAs to regulate gene expression, is involved in tumor proliferation and progression [39]. The regulatory networks, involving miRNAs and lncRNAs, constructed in this study enhance the overall understanding of the regulation of radiosensitivity and prognosis in CESC, thereby providing insights into the roles and mechanisms of the six hub genes.

This study has some limitations that should be acknowledged. First, the datasets obtained from TCGA and GEO databases were relatively small in size. Second, experimental validation was not performed in vivo or in vitro. Despite these limitations, the preliminary findings of this study offer valuable insights into genes associated with radiotherapy response in cervical cancer. In future studies, we will investigate the role of the six hub genes, namely, SELP, PIM2, SDS, CCL19, NRP1, and SF3A2, in radiotherapy-resistant CESC. In particular, we will examine the effects of overexpression or silencing of these genes on the cell cycle, cell growth, colony formation, programmed cell death, and metastasis in human cervical cell lines and animal models after radiation treatment. In addition, we will thoroughly investigate the most pertinent signaling pathways and comprehensive regulatory networks involving TFs, mRNAs, and mRNAs. We recognize that our study's limitations extend beyond the ones mentioned. For instance, the use of only two datasets limits the generalizability of our findings. Including additional eligible datasets could potentially alter the screened hub genes and consequently change the enriched GO results and pathways. Furthermore, it is possible that other genes not identified in this study due to the algorithms used may have a critical effect on the radiosensitivity of cervical cancer. Future research could explore these possibilities and validate our findings through experimental methods, such as in vivo or in vitro studies.

Conclusions

In conclusion, the six hub genes identified in this study, namely, SELP, PIM2, CCL19, SDS, NRP1, and SF3A2, are associated with radiotherapy response in CESC. These hub genes were found to be correlated with tumor-infiltrating immune cells, immune-related genes, chemotherapy sensitivity, and CESC-related genes. The biological pathways associated with these genes were identified and a regulatory network involving transcription factors (TFs) and mRNAs and a ceRNA network involving mRNAs, miRNAs, and lncRNAs were constructed to assess the regulatory mechanisms of the hub genes. In addition, a predictive nomogram incorporating clinicopathological features and hub genes was established to predict the OS of patients with CESC at 1, 3, and 5 years. The predictive accuracy of the nomogram was validated based on a calibration curve. Altogether, this exploratory and comprehensive study provides novel insights into the development of radiotherapy resistance in CESC and proposes promising biomarkers and molecular mechanisms that may affect radiotherapy response and prognosis in CESC. In future studies, we will investigate the specific effects of the six hub genes on the biological characteristics of cervical cancer cells when exposed to radiation both in vivo and in vitro. Additionally, we will investigate the potential signaling pathways and molecular mechanisms related to radiotherapy sensitivity regulated by the hub genes.

Supplementary Information

Additional file 1: Figure S1. Functional enrichment analysis of DEGs using Metascape

Additional file 2: Figure S2. Protein–protein interaction network of DEGs visualized using Cytoscape. Circles represent genes, and lines represent the interaction between genes

Additional file 3: Figure S3. Correlation between the expression of hub genesand various immune-related genes obtained from the TISIDB database a Correlation of hub genes with chemokine-related genes. b Correlation of hub genes with immunostimulation-related genes. c Correlation of hub genes with MHC-related genes. d Correlation of hub genes with immunosuppression-related genes. e Correlation of hub genes with receptor-related genes.

Additional file 4: Figure S4. Sensitivity analysis of cisplatin, docetaxel, fluorouracil, and paclitaxel based on the expression of hub genes using the GDSC database a SELP. b PIM2. c CCL19. d SDS. e NRP1. f SF3A2.

Additional file 5: Figure S5. A GSVA of NRP1. B GSVA of CCL19. C GSVA of SF3A2. E GSEA of NRP1. F GSEA of CCL19. G GSEA of SF3A2.

Additional file 6: Figure S6. Flowchart of the present study

Additional file 7

Acknowledgements

We would like to thank KetengEdit (www.ketengedit.com) for its linguistic assistance during the preparation of this manuscript.

Author contributions

Conceptualization: [all authors]; Methodology: [Yanhong Wang]; Formal analysis and investigation: [Yanhong Wang, Yi Ouyang]; Writing—original draft preparation: [Yanhong Wang, Yi Ouyang]; Writing—review and editing: [Xinping Cao, Qunrong Cai]; Funding acquisition: [Yanhong Wang]; Supervision: [Xinping Cao, Qunrong Cai].

Funding

This research was funded by Fujian Provincial Natural Science Foundation Projects, grant number 2020J01210.

Data availability

The data used and analyzed during the current study are available from public databases, which were recorded detail in the Methods part.

Code availability

The software code used in this study will be made available after publication, which could be acquired from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Ethical review and approval were waived for this study because that the data supporting the findings of this study are available from the TCGA and GEO databases, which are publicly available and deidentified.

Patient consent

Patient consent was waived the data supporting the findings of this study are available from the TCGA and GEO databases, which are publicly available and deidentified.

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Yanhong Wang and Yi Ouyang should be considered joint first authors.
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