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Method Article

Tropomodulin 3 Overexpression as a Marker for Platinum Resistance and Immune Infiltration in Ovarian Cancer

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DOI:

10.3791/65841

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August 2nd, 2024

 ,  ,  ,  ,  ,  ,  ,  , 
1Department of Clinic, School of Medicine, Yangzhou Polytechnic College, 2Department of Nuclear Medicine, Huangshi Central Hospital, Affiliated Hospital of Hubei Institute of Technology, 3School of Biology and Engineering, Guizhou Medical University, 4Hemorheology Center, Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Peking University Health Science Center

* These authors contributed equally

In This Article

Summary

Tropomodulin 3 (TMOD3) has been increasingly studied in tumors in recent years. This study is the first to report that TMOD3 is highly expressed in ovarian cancer and is closely associated with platinum resistance and immune infiltration. These results could help improve the therapeutic outcomes for ovarian cancer.

Abstract

The cytoskeleton plays an important role in platinum resistance in ovarian cancer. Tropomodulin 3 (TMOD3) is critical in the development of many tumors, but its role in the drug resistance of ovarian cancer remains unexplored. By analyzing data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, this study compared TMOD3 expression in ovarian cancer and normal tissues, and examined the expression of TMOD3 after platinum treatment in platinum-sensitive and platinum-resistant ovarian cancers. The Kaplan-Meier method was used to assess the effect of TMOD3 on overall survival (OS) and progression-free survival (PFS) in ovarian cancer patients. microRNAs (miRNAs) targeting TMOD3 were predicted using TargetScan and analyzed using the TCGA database. Tumor Immune Estimation Resource (TIMER) and an integrated repository portal for tumor-immune system interactions (TISIDB) were used to determine the relationship between TMOD3 expression and immune infiltration. TMOD3 coexpression networks in ovarian cancer were explored using LinkedOmics, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and The Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics. The results showed that TMOD3 was highly expressed in ovarian cancer and was associated with the grading, staging, and metastasis of ovarian cancer. TMOD3 expression was significantly reduced in platinum-treated ovarian cancer cells and patients. However, TMOD3 expression was higher in platinum-resistant ovarian cancer cells and tissues compared to platinum-sensitive ones. Higher TMOD3 expression was significantly associated with lower OS and PFS in ovarian cancer patients treated with platinum-based chemotherapy. miRNA-mediated post-transcriptional regulation is likely responsible for high TMOD3 expression in ovarian cancer and platinum-resistant ovarian tissues. The expression of TMOD3 mRNA was associated with immune infiltration in ovarian cancer. These findings indicate that TMOD3 is highly expressed in ovarian cancer and is closely associated with platinum resistance and immune infiltration.

Introduction

Ovarian cancer is the second-highest in the mortality rate of gynecologic tumors worldwide1. It can be classified into three types based on histopathology: germ cell, gonadal mesenchymal, and epithelial tumors, of which 90% of patients are epithelial ovarian cancer. Risk factors associated with ovarian cancer include persistent ovulation, increased gonadotropin exposure, and inflammatory cytokines2. More than 75% of ovarian cancer cases are not detected until advanced stages, resulting in their lack of effective treatment. Patients with advanced ovarian cancer have a poor prognosis, with less than 20% of the 5-year survival rate despite new chemotherapy regimens, such as intraperitoneal administration and targeted therapy. The standard treatment for ovarian cancer mainly consists of tumor resection surgery followed by chemotherapy with drugs such as platinum and paclitaxel. However, tumor recurrence occurs in about 70% of cases1. Cisplatin exerts its therapeutic effects by interfering with DNA replication and transcription and is currently the first-line agent in ovarian cancer chemotherapy. However, a significant proportion of ovarian cancer patients are platinum-resistant3. Multiple cellular processes, such as drug efflux, cellular detoxification, DNA repair, apoptosis, and autophagy, are critical in platinum resistance in ovarian cancer cells4,5,6.

The alteration in the cytoskeleton is an important mechanism affecting platinum resistance in ovarian cancer. It has been recently reported that cytoskeleton-related genes are usually aberrantly expressed, and the actin cytoskeleton is significantly modified in the presence of cisplatin-triggered apoptosis7. Many studies have shown that cisplatin modulates the nanomechanics of ovarian cancer cells. The cell stiffness of sensitive cells increases with platinum dose-dependently, mainly contributed by the disruption of actin polymerization. In contrast, cisplatin-resistant cells showed no significant change in cell stiffness after cisplatin treatment8. Furthermore, the Young's moduli of cisplatin-sensitive ovarian cancer cells were lower, as revealed by atomic force microscopy. In contrast, cisplatin-resistant ovarian cancer cells exhibit a cytoskeleton characterized by long actin stress fibers. Inhibiting Rho GTPase decreases stiffness and enhances cisplatin sensitivity in these resistant cells. Conversely, activating Rho GTPase in cisplatin-sensitive cells increases cell stiffness and reduces their sensitivity to cisplatin9.The RNA-binding protein with multiple splicing (RBPMS), a tumor suppressor gene, reduces cisplatin resistance in ovarian cancer cells by regulating the protein expression of the cytoskeletal network and maintaining cell integrity10. Actin stress fibers are more pronounced in A2780/CP cells compared to A2780 cells. The development of drug resistance in ovarian cancer cells induces extensive reorganization of the actin cytoskeleton, thereby affecting cellular mechanical properties, motility, and intracellular drug transport11.

TMOD3 is a cytoskeleton-regulatory protein that prevents the depolymerization of actin by capping the slow-growing (pointed) ends of actin filaments12.TMOD3 plays different roles in different cell types by regulating actin dynamics and participates in various processes, such as promoting cell shape, cell migration, and muscle contraction. It was shown that TMOD3 deletion in mice leads to embryonic death at E14.5-E18.5, suggesting that TMOD3 may be a critical factor in embryonic development13. Based on its biological functions in stem and progenitor cells, TMOD3 may play an essential role in tumor progression. In hepatocellular carcinoma, TMOD3 promotes the growth, invasion, and migration of hepatocellular carcinoma cells by activating the MAPK/ERK signaling pathway14 and promotes distant metastasis by activating the PI3K-AKT pathway through interaction with the epidermal growth factor receptor15. MiRNA-490-3p inhibits hepatocellular carcinoma cell proliferation and invasion by targeting TMOD316. MiR-145 improves the radiosensitivity of radiation-resistant non-small cell lung cancer by inhibiting TMOD317. In vitro experiments revealed that TMOD3 mediated the invasion of esophageal cancer cells by regulating the cytoskeleton through interaction with lysyl oxidase homolog 2 (LOXL2)18. In addition, proteomic analysis revealed that high expression of TMOD3 could potentially mediate etoposide chemoresistance in lung cancer through the apoptotic pathway19. Although TMOD3 has been increasingly studied in tumors in recent years, there are still no reports on the role of TMOD3 in ovarian cancer and chemotherapy.

This study found that TMOD3 is upregulated in ovarian cancer. Notably, the upregulation of TMOD3 was associated with platinum resistance. This study also evaluated the prognostic value of TMOD3 in ovarian cancer and its correlation with tumor immune infiltration. This study suggests that overexpression of TMOD3 in ovarian cancer is associated with platinum resistance.

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Protocol

1. Gene Expression Omnibus (GEO)

NOTE: TMOD3 expression in ovarian cancer, in ovarian cancer treated with platinum drugs, and in drug-resistant ovarian cancer were derived from the GEO datasets. The study type of all datasets was expression profiling by array, and the organisms were Homo sapiens.

  1. Go to the GEO database (see Table of Materials), and input keywords such as TMOD3, ovarian cancer, and drug-resistant or data accession in the search box (see Supplementary Figure 1A).
  2. Divide data into different groups in the Define group box. In the option menu, choose Benjamini and Hochberg (False discovery rate) in the Apply adjustment to the P-values box, and then click on analyzed in the GEO2R menu. Click on download full table to get the results.
  3. Investigate and plot the downloaded data with graphing and statistics software (see Supplementary Figure 1B).
  4. Use unpaired t-test for the comparison between two groups.

2. TNMplot

NOTE: TNMplot utilizes RNA-seq data from The Cancer Genome Atlas (TCGA), Therapeutic Research to Generate Effective Treatments (TARGET), and Genotype-Tissue Expression (GTEx) repositories20. The expression of TMOD3 in normal ovarian tissues and ovarian cancer was analyzed using TNMplot.

  1. Go to the TNMplot web tool (see Table of Materials), and click on Compare Tumor and Normal.
  2. Insert TMOD3 in Choose a gene box, and choose Ovarian Serous Cystadenocarcinoma in Choose tissue box.
  3. Click on start analysis to get the results (see Supplementary Figure 1C).

3. UALCAN

NOTE: The University of ALabama at Birmingham CANcer data analysis Portal (UALCAN) is a user-friendly online resource for analyzing publicly available cancer data21. Protein level expression of TMOD3 in normal tissue and ovarian cancer in CPTAC data was analyzed using UALCAN.

  1. Go to UALCAN (see Table of Materials) and click on the Proteomics menu.
  2. Insert TMOD3 in the Enter gene names box, and choose ovarian cancer in the CPTAC dataset box.
  3. Click on Explore to get the results (see Supplementary Figure 1D).

4. KM-plotter prognostic analysis

NOTE: The prognostic value of TMOD3 in ovarian cancer was analyzed using Kaplan Meier plotter (KM-plotter), including overall survival (OS) and progression-free survival (PFS)22.

  1. Go to KM-plotter (see Table of Materials), and click on Start KM-plotter for ovarian cancer.
  2. Insert TMOD3 in Affy id/ Gene symbol box.
  3. Choose Auto select best cutoff in Split patients by box.
  4. Choose contains platin in the chemotherapy option when performing prognosis analysis for platinum-based chemotherapy patients.
  5. Choose TCGA in Use following dataset(s) for the analysis box.
  6. Click on Draw Kaplan-Meier plot to get the results (see Supplementary Figure 2).

5. ROC plotter

NOTE: The Receiver Operating Characteristic Curve (ROC) Plotter was used to analyze the expression of TMOD3 in patients resistant or sensitive to platinum-based chemotherapy and allows validation of the interested gene as a predictive marker by ROC curves. Datasets of ROC plotter at the transcriptome level are mainly from the TCGA and GEO databases and contain treatment and response data from 1816 ovarian cancer patients23.

  1. Go to The ROC Plotter (see Table of Materials), and click on ROC plotter for ovarian cancer.
  2. Insert TMOD3 in the Gene symbol box.
  3. Choose Relapse-free survival at 6 months in the Responds box, and choose Platin in the Treatment box.
  4. Click on Calculate to get the results (see Supplementary Figure 3).

6. mRNA-miRNA analysis

NOTE: The miRNAs targeting TMOD3 were predicted by TargetScan24, and then the correlation of TMOD3 with these miRNAs in the TCGA ovarian cancer dataset was analyzed by cBioportal25. Then, the result above was visualized by Cytoscape26. MiRNA expression in cisplatin-sensitive and drug-resistant ovarian cancer patients was analyzed by LinkedOmics27.

  1. Go to TargetScan (see Table of Materials), and insert TMOD3 in the Enter a human gene symbol box (see Supplementary Figure 4A).
  2. Go to cBioportal (see Table of Materials).
  3. Choose Ovarian Serous Cystadenocarcinoma (TCGA, Nature 2011) dataset and insert TMOD3 and miRNA symbols in the Enter Genes box.
  4. Click on Submit Query to get the correlation data of TMOD3 with miRNAs in the TCGA ovarian cancer data set (see Supplementary Figure 4B), then visualize the result by Cytoscape (see Table of Materials) (see Supplementary Figure 4C).
  5. Go to LinkedOmics (see Table of Materials), and select TCGA_OV Sample cohort.
  6. Choose clinical in the Select Search Dataset box, and select platinum status in the Select Search Dataset Attribute box.
  7. Choose miRNASeq in the Select Target Dataset box, and choose t-test in Select Statistical Method.
  8. Click on Submit Query to get the results (see Supplementary Figure 4D).

7. Immuno-infiltration analysis

NOTE: The Human Protein Atlas (HPA) database was used to analyze the distribution of TMOD3 in various immune cells. TIMER is a convenient online database that analyzes immune infiltration associated with multiple cancer types28. This study used TIMER to analyze the relationship between TMOD3 mRNA expression and ovarian cancer purity, and immune cell infiltration. TISIDB is an online portal for tumor-immune system interactions29. This study used TISIDB to determine the correlation between TMOD3 and immunomodulators in ovarian cancer.

  1. Go to HPA (see Table of Materials), and Insert TMOD3 in the Search box, then get the result.
  2. Choose Immune to show the distribution of TMOD3 in various immune cells (see Supplementary Figure 5A).
  3. Go to TIMER (see Table of Materials) and insert TMOD3 in the Gene Symbol box.
  4. Choose OV in the cancer types box and click on Submit to get the result (see Supplementary Figure 5B).
  5. Go to TISIDB (see Table of Materials) and insert TMOD3 in the Gene Symbol box.
  6. Click on Submit to get the result (see Supplementary Figure 5C).

8. TMOD3 coexpression networks in ovarian cancer

NOTE: Genes co-expressed with TMOD3 were analyzed by LinkedOmics, and TOP50 genes were displayed by heat maps. TMOD3 interacting genes were predicted by STRING30. Then, the overlapping genes were displayed by the Venn diagram. The overlapping genes were functionally annotated by DAVID31for Gene Ontology Biological Process (GO-BP), Gene Ontology Cellular Component (GO-CC), Gene Ontology Molecular Function (GO-MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.

  1. Go to LinkedOmics, and select TCGA_OV Sample cohort.
  2. Choose RNAseq in Select Search Dataset box and Select Target Dataset box.
  3. Click on Submit Query to get the results of genes co-expressed with TMOD3.
  4. Go to STRING (see Table of Materials), and insert TMOD3 in the Protein Name box.
  5. Choose homo sapiens in the Organisms box and click on search to get the result (see Supplementary Figure 6).
  6. Go to DAVID (see Table of Materials).
  7. Insert the overlapping genes in the Enter Gene List box and choose ENSEMBL _GENE_SYMBOL in the Select Identifier box.
  8. Click on Submit List to get the results (see Supplementary Figure 7).

9. CTD database

NOTE: The CTD database is a novel tool to analyze the relationships between chemistry, genes, phenotypes, disease, and the environment32. The CTD database predicts drugs that target TMOD3. The PubChem database is then used to determine the definitive molecular structure of the drug.

  1. Go to the CTD database (see Table of Materials) and choose Chemical - Gene Interaction Query in the Search menu.
  2. Insert TMOD3 in the GENE box, and click on search to get the results (see Supplementary Figure 8).
  3. Go to the PubChem database (see Table of Materials) and insert drugs in the search box to get the results (see Supplementary Figure 9).

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Results

TMOD3 expression in ovarian cancer
First, the GEO database showed that the mRNA expression levels of TMOD3 were elevated in microarray datasets GSE51088 and GSE66957 (Figure 1A,B). TMOD3 was also highly expressed in ovarian cancer compared to normal ovarian tissues by the TNMplot web tool (Figure 1C). Analysis of CPTAC data by the UALCAN web tool showed that the protein level of TMOD3 was also significantly higher in ovarian...

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Discussion

The cytoskeleton has been considered essential in the development and progression, treatment, and prognosis of various tumors52. Compared with TMOD1, which is restricted to erythrocytes and the cardiovascular system53, and TMOD2, which is restricted to the nervous system54, TMOD3 has a ubiquitous distribution, which makes the study of TMOD3 in systemic tumors more popular14,15,

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Disclosures

The authors report no conflict of interest.

Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (No. 32171143, 31771280) and grants from the Natural Science Foundation of Jiangsu Provincial Department of Education (No. 18KJD360003, 21KJD320004).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
cBioportalMemorial Sloan Kettering Cancer CenterCorrelation analysis of  TMOD3 with targeted miRNAs (https://www.cbioportal.org)
CTD databaseNorth Carolina State UniversityTo analyze the relationships between chemistry, genes, phenotype, disease, and environment (https://ctdbase.org/)
CytoscapeNational Institute of General Medical Sciences of the National Institutes of HealthNetwork Data Integration, Analysis, and Visualization (www.cytoscape.org/)
DAVIDFrederick National Laboratory for Cancer ResearchA comprehensive set of functional annotation tools for investigators to understand the biological meaning behind large lists of genes(https://david.ncifcrf.gov/)
GEONCBIGene expression analysis (https://www.ncbi.nlm.nih.gov/geo/ )
HPAKnut & Alice Wallenberg foundationThe Human Protein Atlas (HPA) database helped analyze the distribution of TMOD3 in various immune cells (https://www.proteinatlas.org/)
KM-plotter Department of Bioinformatics of the Semmelweis UniversityPrognostic Analysis (https://kmplot.com/analysis/)
LinkedOmicsBaylor College of MedicineA platform for biologists and clinicians to access, analyze and compare cancer multi-omics data within and across tumor types (http://www.linkedomics.org/)
PubChem databaseU.S. National Library of MedicineTo determine the definitive molecular structure of the drug
ROC Plotter Department of Bioinformatics of the Semmelweis UniversityValidation of the interest gene as a predictive marker (http://www.rocplot.org/)
STRINGSwiss Institute of BioinformaticsCoexpression networks analysis(https://string-db.org)
TargetScanWhitehead Institute for Biomedical ResearchPrediction of miRNA targets (www.targetscan.org/)
TIMERHarvard UniversitySystematical analysis of immune infiltrates across diverse cancer types (https://cistrome.shinyapps.io/timer/)
TISIDBThe University of Hong KongA web portal for tumor and immune system interaction(http://cis.hku.hk/TISIDB/)
TNMplotDepartment of Bioinformatics of the Semmelweis UniversityGene expression analysis (https://www.tnmplot.com/ )
UALCANThe University of ALabama at Birmingham Gene expression analysis (http://ualcan.path.uab.edu)

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