Background
Muscular invasive bladder cancer (MIBC) is a common malignant tumor in the world. Because of their heterogeneity in prognosis and response to treatment, biomarkers that can predict survival or help make treatment decisions in patients with MIBC are essential for individualized treatment.
Aim
We aimed to integrate bioinformatics research methods to identify a set of effective biomarkers capable of predicting, diagnosing, and treating MIBC. To provide a new theoretical basis for the diagnosis and treatment of bladder cancer.
Methods and results
Gene expression profiles and clinical data of MIBC were obtained by downloading from the Cancer Genome Atlas database. A dataset of 129 MIBC cases and controls was included. 2084 up‐regulated genes and 2961 down‐regulated genes were identified by differentially expressed gene (DEG) analysis. Then, gene ontology analysis was performed to explore the biological functions of DEGs, respectively. The up‐regulated DEGs are mainly enriched in epidermal cell differentiation, mitotic nuclear division, and so forth. They are also involved in the cell cycle, p53 signaling pathway, PPAR signaling pathway, and so forth. The weighted gene co‐expression network analysis yielded five modules related to pathological stages and grading, of which blue and turquoise were the most relevant modules for MIBC. Next, Using Kaplan–Meier survival analysis to identify further hub genes, the screening criteria at
p
≤ .05, we found
CNKSR1
,
HIP1R
,
CFL2
,
TPM1
,
CSRP1
,
SYNM
,
POPDC2
,
PJA2
, and
RBBP8NL
genes associated with the progression and prognosis of MIBC patients. Finally, immunohistochemistry experiments further confirmed that
CNKSR1
plays a vital role in the tumorigenic context of MIBC.
Conclusion
The research suggests that
CNKSR1
,
POPDC2
, and
PJA2
may be novel biomarkers as therapeutic targets for MIBC, especially we used immunohistochemical further to validate
CNKSR1
as a therapeutic target for MIBC which may help to improve the prognosis for MIBC.