assessing the effect of tmod3 on overall survival and progression-free survival (pfs) in ovarian cancer patients

TMOD3 Expression and Its Role in Ovarian Cancer Progression

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&#160;the disruption of&#160;actin polymerization. In contrast, cisplatin-resistant cells showed no significant change in cell stiffness after cisplatin treatment8. Furthermore, the Young&#39;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.

Author Spotlight: Unveiling the Role of TMOD3 in Platinum Resistance and Immune Infiltration in Ovarian Cancer

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

Our research focuses on drug-resistant in ovarian cancer. In our paper, we try to uncover the role of TMOD3 in ovarian cancer and chemotherapy. Many cytos calendar proteins have been demonstrated to play important role in ovarian cancer and drug resistance, but there are still no reports on the role of TMOD3, ovarian cancer and sim therapy.Our study first reported that TMOD3 is highly expressed on ovarian cancer, and closely associated with platinum resistance and immune infiltration. This result contributed to the improvement of therapy outcomes in ovarian cancer.

Assessing the Effect of TMOD3 on Overall Survival and Progression-Free Survival (PFS) in Ovarian Cancer Patients

To begin, go to UALCAN and click on the proteomics menu. Insert TMOD3 in the enter gene names box. Choose ovarian cancer in the CPTAC dataset box, and click on explore to get the results.Then, go to KM plotter and click on start KM plotter for ovarian cancer. Insert TMOD3 in the Affy id or gene symbol box. After that, choose auto select best cutoff in split patients by box.When performing prognosis analysis for platinum-based chemotherapy patients, select the chemotherapy option that contains platin. Choose TCGA in use following datasets for the analysis box. At last, click on draw Kaplan-Meier plot to get the results.Protein levels of TMOD3 were significantly higher in ovarian cancer than in normal ovarian tissues. TMOD3 expression levels decreased after cisplatin treatment in the ovarian cancer CH1 cell line, and were lower in ovarian cancer xenograft mice treated with carboplatin, compared to untreated ones. TMOD3 expression was significantly higher in cisplatin-resistant ovarian cancer cell line A2780, compared to the cisplatin-sensitive group, and higher in cisplatin-resistant ovarian cancer spheroid expression, compared to the cisplatin-sensitive group.

assessing-effect-tmod3-on-overall-survival-progression-free-survival

Research

JoVE Journal

Cancer Research

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.

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.

The cytoskeleton plays an important role in platinum resistance in ovarian cancer. Tropomodulin 3 (TMOD3) is critical in the development of many tumors, ...

Analyzing the Expression of TMOD3 in Normal Ovarian Tissues and Ovarian Cancer

To begin, go to the GEO database and input keywords such as TMOD3, ovarian cancer and drug resistant or data accession in the search box. Divide data into different groups in the defined group box. Choose Benjamini and Hochberg in the apply adjustment to the P values box from the options menu.Click on analyze in the GEO2R menu, then click on download full table to get the results. Next, investigate and plot the downloaded data with graphing and statistics software. Go to the TNMplot web tool and click on compare tumor and normal.Insert TMOD3 in the choose a gene box and select ovarian serous cystadenocarcinoma in the choose tissue box. Afterward, click on start analysis to get the results. TMOD3 mRNA expression levels were elevated in microarray data sets.GSE51088 and GSE66957 and were highly expressed in ovarian cancer compared to normal ovarian tissues.

TMOD3 Expression Analysis in Platinum-Based Chemotherapy Resistant and Sensitive Patients and mRNA-miRNA Analysis

To begin, go to the ROC Plotter and click on ROC Plotter for Ovarian Cancer. Insert TMOD3 in the gene symbol box. In the response box, choose Relapse-free survival at 6 months.In the treatment box, select Platin. Then click on Calculate to get the results. Go to TargetScan and insert TMOD3 in the Enter a human gene symbol box.Then go to see cBIOPortal. Now choose Ovarian serous cystadenocarcinoma, TCGA Nature 2011 dataset. Insert TMOD3 and miRNA symbols in the Enter Genes box.Click on Submit Query to get the correlation data of TMOD3 with miRNAs in the TCGA ovarian cancer dataset. After that, visualize the result by Next, go to LinkedOmics and select TCGA OV sample cohort. Then choose Clinical in the Select Search Dataset box.Select Platinum_status in the Select Search Dataset Attribute box. Next, choose miRNA sequencing in the Select Target Dataset box. Choose T-test in Select Statistical Method, and click on Submit Query to get the results.TMOD3 expression was significantly reduced after platinum-based chemotherapy in highly sensitive ovarian cancer patients compared to untreated ones. TMOD3 expression was significantly higher in ovarian cancer patients with platinum resistance compared to those with platinum sensitivity, and the ROC curve showed TMOD3 as a marker for determining platinum resistance. 21 microRNAs targeting TMOD3 were identified, with most associated with cisplatin resistance in ovarian cancer, and hsa-miR-133a-3p being significantly negatively correlated with TMOD3 expression and lowly expressed in cisplatin-resistant cancers.

Checking the Distribution of TMOD3 in Immune Cells and TMOD3 Coexpression Networks in Ovarian Cancer

To begin, go to HPA and insert TMOD3 in the Search box to get the result. Choose Immune to show the distribution of TMOD3 in various immune cells. Go to TIMER and insert TMOD3 in the Gene Symbol box.Choose OV in the Cancer Types box, and click on Submit to get the result. Then go to TISIDB, insert TMOD3 in the Gene Symbol box, and click on Submit. In the LinkedOmics, select TCGAOV sample cohort.Choose RNA Sequencing in Select Search Dataset box and Select Target Dataset box. Now, click on Submit Query to get the results of genes co-expressed with TMOD3. Go to String and insert TMOD3 in the Protein Name box.Then choose Homo sapiens in the Organisms box, and click on Search to get the result. Afterward, go to DAVID. Insert the overlapping genes in the Enter Gene List box, and choose Ensemble Gene ID in the Select Identifier box.Click on Submit List to get the results. Then go to the CTD database and choose Chemical Gene Interaction Query in the Search menu. Insert TMOD3 in the Gene Box, and click on Search to get the results.Go to the PubChem database and insert drugs in the Search box. TMOD3 was widely distributed in various immune cells and negatively correlated with tumor purity, suggesting its role in the immune infiltration of ovarian cancer tissues. TMOD3 expression was positively correlated with B lymphocytes, CD-8 positive T cells, neutrophils and dendritic cells, but not with CD-4 positive T cells and macrophages.TMOD3 expression was significantly correlated with various immunomodulators, including immune checkpoints, such as PD-1, PDL-1, and CTLA-4.