Aging is among the most mystical human processes, which is considered to be an obscure syndrome involving multiple biological pathways [25, 26]. Cellular senescence and accumulation of senile cells may lead to a higher risk of cancer [27, 28]. Bladder cancer (BLCA) as a prevalent urinary tumor, general therapy methods are limited to surgery, immunotherapy as well as perfusion chemotherapy, and the effect of treatment largely depends on precise staging [29, 30]. BLCA is divided into two main subtypes, the majority of cases are non–muscle-invasive bladder cancer (NMIBC), the other is muscle‐invasive bladder cancer (MIBC) [31]. With the advancement of molecular level technique, prognostic models with novel characteristics will do benefit to improve clinical decisions [32, 33]. We identified seven aging-related biomarkers and constructed prognostic nomogram, which can guide further clinical treatment and predict the progression of BLCA.
In the current study, we filtered DEARGs based on genome sequence data from BLCA and normal. Subsequently, we performed enrichment analysis on the function of these differential genes and found that they are mainly involved in many complex biological processes, such as aging, cell apoptotic. Cellular senescence can precisely regulate the survival status of various cells, but undesirable accumulation of senescent cells also can cause the instability of internal microenvironment [6, 34, 35]. One of the characteristics of aging is the disorder of inflammatory mediators, called the senescence-associated secretory phenotype (SASP), which constitutes tumor-promoting microenvironment [36–38]. SASP, a mysteries factor complex, acts on many physiological mechanisms and pathological processes, including tumorigenesis and chemoresistance [13, 39, 40]. Therefore, the aging-related genes play a remarkable role in BLCA and will be a huge opportunity to develop more advanced clinical intervention strategies.
To further define the aging-related signature genes in BLCA, this study performed univariate Cox regression and LASSO Cox regression analysis and developed seven genes (IGF1, NGF, GCLM, PYCR1, EFEMP1, APOC3 and IFNB1). Furthermore, a risk assessment model and nomogram were established for identifying high risk BLCA patients and assessing prognosis based on the seven genes and clinical information. Among these seven genes, Insulin-like growth factors (IGF) is star factor of energy metabolism pathway, abnormal IGF expression may lead to tumorigenesis, invasion and metastasis [41–43]. IGF-1, an intersection of carcinogenic pathways, can act as an inducer to generate karyokinesis, retard programmed cell death, promote angiogenesis, and it also can conduct the expression of anti-apoptotic gene Bcl-2 [44–47]. NGF, through TRKA receptor, can lead to several molecular expression changes of signal pathways, which associated with tumor cell proliferation and angiogenesis [48, 49]. Many kinds of cancer have found upregulated PYCR1 expression, and JAK/STAT signaling is activated through the action of PYCR1 [50, 51]. Normal expression of EFEMP1 can maintain the function of basement membranes, and abnormal EFEMP1 expression directly correlated with the more powerful capacity for invasion and metastasis in cancer [52]. IFNB1, which is a type of I IFNs, not only has antiviral activity, but also regulates immunologic functions and cell cycle, such as anti-proliferative, proapoptotic [53–55]. Besides, the relationships between GCLM and BLCA as well as APOC3 and BLCA have not clear investigation. GCLM and APOC3 are potential biomarkers to predict tumor progression and be targeted molecules for clinical treatment [56–59]. These results provide background knowledge for further ARGs research in BLCA.
Finally, we found that seven immune cells (B cells naive, dendritic cells activated, eosinophils, macrophages M1, mast cells resting, monocytes, plasma cells) exert significant differences between the low- and high-risk groups through CIBERSORT algorithms and Wilcoxon test. Cellular senescence often triggers several changes of immune system, which is responsible for promoting cancer and other chronic conditions susceptibility [60–62]. The immune cells in the TME execute highly dynamic tumor resistance and induction [18, 63, 64]. For example, high invasion of TIGIT+ CD8+ T-cells predicted poor outcome in MIBC [65]. The immune score and stromal score of BLCA was calculated by the ESTIMATE algorithm, and we found that risk signatures based on 7 ARGs were closely correlated with these scores. Consequently, we speculate that seven prognostic aging-related genes may affect tumor immune microenvironment, further the prognoses of BLCA patients are affected by the imbalance between immunological surveillance and immune escape [66, 67].
This study had a limitation that it was insufficient to draw conclusions only through bioinformatics. Clinical and experimental studies are needed to verify the roles of these seven aging-related genes in BLCA.