Original Research Effects of OGFOD1 in bladder cancer progression and its prognostic significance: Insights from bioinformatics analysis Wenzhen Liu, Macao Wan* Abstract Background: Previous studies have established the role of 2-oxoglutarate and Fe(II)-dependent oxygenase domain–containing protein 1 (OGFOD1) in oncogenesis. The objective of this investigation was to discern the diagnostic and prognostic relevance of OGFOD1 within the context of bladder cancer (BLCA) using bioinformatics methodologies. Methods: We collected RNA sequencing data from The Cancer Genome Atlas database and verified it using the GSE13507 dataset. Immunohistochemical analysis was based on data from the human protein atlas, and the protein-protein interaction network was constructed using the STRING database. Bioinformatics analysis was performed using the R application, analyzing the correlation between clinical characteristics and OGFOD1 expression, exploring the potential mechanisms of OGFOD1 in BLCA through Kyoto Encyclopedia of Genes and Genomes analysis, and evaluating the diagnostic and prognostic value of OGFOD1 expression in BLCA through receiver operating characteristic curve analysis, Kaplan-Meier analysis, and multivariate Cox analysis. Furthermore, a BLCA prognostic nomogram was constructed. Results: We report higher expression levels of OGFOD1 in BLCA specimens compared with those in noncancerous tissues; this can be used to predict the outcome of the disease. Further, results suggest that OGFOD1 is implicated in the activation of the peroxisome proliferator-activated receptor signaling cascade, potentially interacting with other genes linked to expression in promoting the onset and progression of BLCA. Conclusions: OGFOD1 is a promising candidate as a prognostic indicator in BLCA. Keywords: OGFOD1; bladder cancer; bioinformatics; function; diagnosis; prognosis variety of biological oxidation reactions.[4] Evidence from previous studies reveals that depletion of OGFOD1 impairs cellular proliferation, triggers the assembly of stress granules, and arrests translation.[5] Its contribution to the progression of various cancers has also been reported.[6,7] Nevertheless, its implication in BLCA has not been thoroughly investigated. This study uses bioinformatics tools to examine OGFOD1 expression levels in BLCA compared with those in healthy tissues, to unravel its involvement in BLCA and assess its potential as a biomarker for diagnostic and prognostic applications. 1. Introduction Globally, bladder cancer (BLCA) seriously affects the urinary tract. It is the ninth most common malignancy and ranks 13th in cancerrelated mortality rates.[1] Although the death rate for patients with non–muscle-invasive BLCA is relatively low, their susceptibility to redeveloping the disease is significantly high.[2] In contrast, muscleinvasive BLCA has an approximately 50% 5-year disease-specific survival (DSS) rate, primarily due to fatalities resulting from local recurrence or metastasis.[3] The pathogenesis of BLCA and its reliable diagnostic and prognostic markers remain elusive. The enzyme 2-oxoglutarate (2OG) and Fe(II)-dependent oxygenase domain–containing protein 1 (OGFOD1) is a member of the extensive 2OG-dependent oxygenase family, recognized for its role in a 2. Methods 2.1. Data collection RNA sequencing (RNAseq) data for differential gene expression, formatted as transcripts per million (TPM), were sourced from The Cancer Genome Atlas (TCGA) repository. Verification was performed using dataset GSE13507, procured from the GEO dataset. This dataset encompasses a gene expression profile from 165 BLCA specimens, 10 noncancerous control samples, and 58 adjacent tissue samples, analyzed using the GPL6102 Illumina human-6 v2.0 expression beadchip. log2 transformation was applied to the TPM RNAseq data for comparison. Department of The Second Clinic, The 940 Hospital of Joint Logistics Support Force of Chinese PLA, Lanzhou 730050, China. * Corresponding author. Address: Department of The Second Clinic, The 940 Hospital of Joint Logistics Support Force of Chinese PLA, Lanzhou 730050, China. E-mail address: 763488683@qq.com (M.-C. Wan). Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Huazhong University of Science and Technology. This is an open access article distributed under the Creative Commons Attribution License 4. 0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. 2.2. Reference databases Immunohistochemical analysis of the clinical specimens was based on data from the human protein atlas (HPA; available at https://www. proteinatlas.org/). For construction of the protein-protein interaction (PPI) network, the STRING database (currently at version 11.5, accessible at https://www.string-db.org/) was used. The Cytoscape and MCODE applications were used to create functional clusters of genes. Oncology and Translational Medicine (2024) 10:3;143–150 Received: 21 November 2023; Revised: 27 December 2023; Accepted: 5 February 2024 http://dx.doi.org/10.1097/ot9.0000000000000040 143 Oncology and Translational Medicine Liu and Wan  Volume 10  Issue 3  2024 2.3. Bioinformatics analysis 2.4. Statistical analysis Statistical computations and graphic outputs in the bioinformatics analyses were carried out using the R application (version 3.6.3; RStudio, PBC, Boston, Massachusetts, USA). For evaluating gene expression differences and their correlations, the base R environment along with the ggplot2 library (version 3.3.3) was used. Pathway enrichment analyses referencing the Kyoto Encyclopedia of Genes and Genomes (KEGG) were executed using clusterProfiler (version 3.14.3) in conjunction with org.Hs.eg.db (version 3.10.0). Receiver operating characteristic (ROC) curves were constructed using the pROC (version 1.17.0.1) and ggplot2 (version 3.3.3) libraries. Cox proportional hazards regression models and Kaplan-Meier survival plots were generated using the Survminer (version 0.4.9) and survival (version 3.2-10) packages. Analyses of time-dependent ROC curves were performed utilizing timeROC (version 0.4) and ggplot2 (version 3.3.3). Furthermore, nomograms and their respective calibration plots were developed using the rms (version 6.2-0) and survival (version 3.2-10) tools. To evaluate gene expression discrepancies in BLCA, the MannWhitney U test and t test were used. Differences in OGFOD1 expression across various groups categorized by clinicopathological data were examined using the chi-squared and Fisher’s exact tests. Prognostic assessment via the Kaplan-Meier method incorporated the log-rank test, and Cox regression was used to identify prognostic indicators for BLCA. Correlations were determined using Spearman’s or Pearson’s correlation coefficients. Statistical significance was attributed to P values <0.05, whereas a P value of >0.05 was considered to indicate a lack of significant difference. 3. Results 3.1. Expression of OGFOD1 in BLCA Analysis of RNAseq data from TCGA database revealed that OGFOD1 expression levels were significantly elevated in BLCA Figure 1. Expression of OGFOD1 in BLCA. OGFOD1 overexpression in tumor tissues compared with that in normal tissues from unmatched (A) and paired (B) BLCA tissues based on data from the TCGA database. OGFOD1 overexpression in tumor tissues compared with that in surrounding (C) and healthy (D) BLCA tissues based on data from the GSE13507 dataset. Immunohistochemical staining of OGFOD1 expression from BLCA (E) and healthy (F) samples based on HPA database. *P < 0.05; **P < 0.01; ***P < 0.001. 144 Oncology and Translational Medicine Liu and Wan  Volume 10  Issue 3  2024 3.2. OGFOD1 expression correlated with clinical characteristics Table 1 Clinical characteristics of patients with bladder cancer from TCGA database. Characteristics Total T stage, n (%)* N stage, n (%)* M stage, n (%)* Gender, n (%) Race, n (%)* Age, n (%) Histologic grade, n (%)* Smoker, n (%)* Age, median (IQR) Levels Overall T1 T2 T3 T4 N0 N1 N2 N3 M0 M1 Female Male Asian Black or African American White <=70 >70 High Grade Low Grade No Yes 414 5 (1.3%) 119 (31.3%) 196 (51.6%) 60 (15.8%) 239 (64.6%) 46 (12.4%) 77 (20.8%) 8 (2.2%) 202 (94.8%) 11 (5.2%) 109 (26.3%) 305 (73.7%) 44 (11.1%) 23 (5.8%) 330 (83.1%) 234 (56.5%) 180 (43.5%) 390 (94.9%) 21 (5.1%) 109 (27.2%) 292 (72.8%) 69 (60, 76) Clinical data from 414 individuals within the TCGA repository are summarized in Table 1, with corresponding OGFOD1 expression levels across various categories shown in Figures 2A–2H. A notable association was observed between elevated OGFOD1 levels and advanced T stages (Figure 2A, P < 0.05), as well as across different racial groups, specifically Black or African American and White individuals (Figure 2F, P < 0.05), and across increased histologic grades (Figure 2G, P < 0.001). This pattern suggests a trend where augmented OGFOD1 expression correlates with the progression and severity of cancer traits. 3.3. PPI network construction and KEGG pathway analyses of OGFOD1-related genes Utilizing the STRING database, a PPI network was generated, highlighting genes interacting with OGFOD1 within the context of BLCA. This network included 4 primary clusters: mitochondriaassociated genes (Figure 3A), members of the small proline-rich protein (SPRR) family (shown in Figure 3B), and a group of collagenrelated genes (Figure 3C), along with genes associated with calcium voltage-gated channels (Figure 3D). Pathway analysis using the KEGG identified 12 pathways with potential roles in BLCA, including the peroxisome proliferator-activated receptor (PPAR) signaling pathway (Figure 4). Further correlations were established between OGFOD1 expression and that of specific PPAR subtype genes, with OGFOD1 showing a positive correlation with PPARA ( P < 0.001, Figure 5A) and PPARD ( P < 0.001, Figure 5B), and a negative correlation with PPARG ( P < 0.001, Figure 5C), in BLCA patient samples from the TCGA database. *Some data missing. samples than those in healthy tissues, as demonstrated using both matched and nonmatched datasets (Figure 1A and Figure 1B, P < 0.05). This upregulation was verified through additional analysis of the GSE13507 dataset, where OGFOD1 expression in tumor tissues was higher than that in adjacent nontumor or healthy tissues, yielding consistent results (Figure 1C and Figure 1D, P < 0.05). Immunohistochemical analysis of BLCA specimens further supported these findings, with higher OGFOD1 expression observed in tumor samples compared with that in healthy tissues (Figure 1E and Figure 1F). Collectively, these insights suggest a significant elevation in OGFOD1 levels in BLCA, implicating its potential involvement in tumor pathogenesis and progression. 3.4. Significance of OGFOD1 overexpression in diagnosis and prognosis Examination of OGFOD1 expression through the TCGA repository yielded a modest diagnostic differentiation between tumor and healthy samples, as shown by the receiver operating characteristic (ROC) curve with an area under the curve (AUC) of 0.650 (Figure 6). In contrast, survival analyses using Kaplan-Meier estimates revealed that OGFOD1 has considerable prognostic significance for overall survival (OS) (Figure 7A, P = 0.038), DSS Figure 2. OGFOD1 expression in different clinical characteristics groups. OGFOD1 expression in different groups of T stage (A), N stage (B), M stage (C), sex (D), age (E), race (F), histologic grade (G), and smoking status (H). *P < 0.05; ***P < 0.001. 145 Oncology and Translational Medicine Liu and Wan  Volume 10  Issue 3  2024 Figure 3. PPI network construction for OGFOD1-related genes in BLCA. (A–D) The top 4 network clusters of PPI network of OGFOD1related genes in BLCA. (Figure 7B, P = 0.007), and progression-free survival (PFS) (Figure 7C, P < 0.001). To quantify prognostic accuracy, timedependent ROC curves for OGFOD1 were generated, yielding AUC metrics for 1-, 3-, and 5-year projections for OS as 0.587, 0.595, and 0.579, respectively (Figure 7D). Corresponding AUC results for DSS projections were 0.637, 0.613, and 0.603 for the same time frames (Figure 7E), whereas PFS predictions were 0.611, 0.603, and 0.621 (Figure 7F). Cox regression models showed that advanced Figure 4. KEGG pathway analyses for OGFOD1-related genes. 146 Oncology and Translational Medicine Liu and Wan  Volume 10  Issue 3  2024 Figure 5. Association between expression of OGFOD1 and PPARA (A), OGFOD1 and PPARD (B), and OGFOD1 and PPARG (C). T, N, and M stages; older age; and increased OGFOD1 levels were linked to suboptimal OS outcomes (Table 2, P < 0.05). Similar patterns were noted for DSS and PFS, where advanced T, N, and M stages and elevated OGFOD1 expression served as markers of poorer prognosis (Table 2, P < 0.05). To enhance clinical decision-making, 3 distinct nomogram models incorporating clinicopathologic factors (such as T, N, and M stages and optionally age), along with OGFOD1 expression levels, were developed, providing individualized 1-, 3-, and 5-year prognostic assessments for patients with BLCA (Figures 8A–C). Precision of nomogram predictions was refined using calibration curves (Figures 9A–C). with a reduction in cell growth and viability, along with increase in tumor cell apoptosis.[6] OGFOD1 has also been reported to stimulate the expansion of breast cancer cells and is linked with adverse outcomes in breast cancer prognosis.[7] Analysis of sequencing data from the TCGA database revealed a pronounced upregulation of OGFOD1 in BLCA. This finding was verified using RNAseq data within the Gene Expression Omnibus (GEO) dataset, along with immunohistochemical evidence sourced from the HPA database. Additionally, examination of OGFOD1 expression across varying clinical subgroups emphasized its elevated levels in advanced pathological T stages and grades. This pattern tentatively indicates a potential role of OGFOD1 in driving the progression of BLCA. We created PPI networks that depict OGFOD1 gene interactions to determine the role of OGFOD1 in BLCA. We discovered that the primary clusters in this network included genes connected to mitochondrial function, the SPRR gene family, collagen production, and genes involved in the operation of calcium voltage-gated 4. Discussion Previous studies have indicated that elevated levels of OGFOD1 increase the growth rate of laryngeal papillomas bearing the human papillomavirus. Reduced OGFOD1 levels have been associated Figure 6. ROC curve for OGFOD1 expression in BLCA diagnosis. 147 Oncology and Translational Medicine Liu and Wan  Volume 10  Issue 3  2024 Figure 7. Kaplan-Meier plots for OGFOD1 expression in BLCA prognosis. Prognostic value of OGFOD1 expression in OS (A), DSS (B), and PFS (C). Prognostic value of OGFOD1 expression for 1, 3, and 5 years for OS (D), DSS (E), and PFS (F). development and prognosis of various cancers, including gastric and lung adenocarcinomas.[9–13] Similarly, expression of collagen genes is known to be tumor-relevant, with studies demonstrating that the suppression of COL3A1 and COL5A1 by miR-29a-3p can augment the cancer-inhibiting effects of sulforaphane in gastric channels. Mitochondria, crucial for energy production and genomic maintenance, have roles that extend to tumorigenesis through pathways like oxidative phosphorylation and DNA repair.[8] SPRRs, precursors in keratinocyte envelopes, have been implicated in epithelial growth and oncogenesis, with studies linking them to the Table 2 Cox regression analysis of prognostic indicators for OS, DSS, and PFS. Characteristics Pathologic T stage T1 and T2 T3 and T4 Pathologic N stage N0 N1–N3 Pathologic M stage M0 M1 Gender Female Male Race Asian Black or African American White Histologic grade Low High Smoker No Yes Age OGFOD1 Low High Total (n) 377 123 254 367 238 129 212 201 11 411 108 303 394 44 23 327 408 21 387 398 109 289 411 411 206 205 Univariate analysis (OS) Hazard ratio (95% CI) P value Total (n) Reference 2.157 (1.485–3.132) <0.001 Reference 2.250 (1.649–3.072) <0.001 Reference 3.112 (1.491–6.493) 0.002 Reference 0.868 (0.629–1.198) 0.390 Reference 2.024 (0.864–4.743) 1.633 (0.832–3.208) 0.105 0.154 Reference 2.960 (0.732–11.959) 0.128 Reference 1.306 (0.923–1.849) 0.132 1.031 (1.015–1.046) <0.001 Reference 1.381 (1.028–1.855) 0.032 364 123 241 355 232 123 207 196 11 397 102 295 380 43 22 315 394 21 373 385 105 280 397 397 198 199 The bold font indicates that the differences are statistically significant, P < 0.05. 148 Univariate analysis (DSS) Hazard ratio (95% CI) P value Total (n) Reference 2.196 (1.399–3.447) <0.001 Reference 3.180 (2.166–4.669) <0.001 Reference 4.171 (1.874–9.282) <0.001 Reference 0.877 (0.593–1.296) 0.510 Reference 2.823 (0.963–8.276) 2.040 (0.829–5.022) 0.059 0.121 Reference 2.154 (0.531–8.741) 0.283 Reference 1.302 (0.857–1.977) 0.216 1.017 (0.999–1.035) 0.066 Reference 1.771 (1.228–2.555) 0.002 378 123 255 368 238 130 212 201 11 412 108 304 395 44 23 328 409 21 388 399 109 290 412 412 206 206 Univariate analysis (PFS) Hazard ratio (95% CI) P value Reference 2.048 (1.417–2.960) <0.001 Reference 2.802 (2.034–3.858) <0.001 Reference 6.416 (3.099–13.286) <0.001 Reference 0.911 (0.655–1.266) 0.578 Reference 2.205 (0.928–5.236) 1.852 (0.943–3.636) 0.073 0.074 Reference 3.647 (0.903–14.726) 0.069 Reference 1.145 (0.818–1.603) 1.013 (0.998–1.027) 0.430 0.083 Reference 1.761 (1.302–2.382) <0.001 Oncology and Translational Medicine Liu and Wan  Volume 10  Issue 3  2024 Figure 8. Nomogram models built to predict OS (A), DSS (B), and PFS (C) probabilities at 1, 3, and 5 years for BLCA patients. Figure 9. Calibration curves built for nomogram models of OS (A), DSS (B), and PFS (C). malignancies.[14] Ion channels, essential in cell life cycles, can influence cancer cell behaviors like proliferation and apoptosis, with alterations in Ca2+ signaling being particularly significant for these processes.[15,16] Moreover, our KEGG pathway analysis linked these genes to the PPAR signaling pathway, with subsequent correlation studies showing the significant relationship of OGFOD1 with the PPAR gene family, including PPARA, PPARD, and PPARG. PPARs are known to be involved in tumor pathology by modulating metabolic pathways.[17] Evidence collected from these analyses reveals biological pathways and molecular activities possibly regulated by OGFOD in BLCA. OGFOD1 did not exhibit strong diagnostic relevance for BLCA; however, it did show potential as a prognostic indicator for OS, DSS, and PFS in assessment of diagnostic and prognostic utility. Consequently, we devised predictive nomograms that integrate various clinical parameters influencing prognosis, aiming to estimate OS, DSS, and PFS rates at 1, 3, and 5 years for patients with BLCA. These findings underscore the potential utility of OGFOD1 in the clinical management of BLCA. One of the constraints of this study was that it was grounded on bioinformatics data and did not include empirical validation of the findings. Therefore, our future studies will concentrate on bridging this gap using comprehensive molecular biology assays and validation in clinical settings. Conflicts of interest statement The authors declare that they have no conflict of interest with regard to the content of this report. Author contributions All authors contributed to data acquisition and interpretation and reviewed and approved the final version of this manuscript. Data availability statement The datasets used and analyzed in the current study are available from the corresponding author on reasonable request. Ethical approval Not applicable. References [1] Antoni S, Ferlay J, Soerjomataram I, Znaor A, Jemal A, Bray F. Bladder cancer incidence and mortality: a global overview and recent trends. Eur Urol 2017;71(1):96–108. [2] Witjes JA. Follow-up in non–muscle invasive bladder cancer: facts and future. World J Urol 2021;39(11):4047–4053. [3] De Vries RR, Nieuwenhuijzen JA, Vincent A, van Tinteren H, Horenblas S. Survival after cystectomy for invasive bladder cancer. Eur J Surg Oncol 2010;36(3):292–297. [4] Loenarz C, Schofield CJ. Physiological and biochemical aspects of hydroxylations and demethylations catalyzed by human 2-oxoglutarate oxygenases. Trends Biochem Sci 2011;36(1):7–18. 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