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
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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.
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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.
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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.
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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.
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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
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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.
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This study benefited from TCGA database, the GEO database, the
Human Protein Atlas database, and the STRING database.
Financial support and sponsorship
Not applicable.
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