GRASLND is a prognostic biomarker and associated with immune infiltration in bladder cancer

doi:10.21203/rs.3.rs-4973148/v1

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GRASLND is a prognostic biomarker and associated with immune infiltration in bladder cancer

https://doi.org/10.21203/rs.3.rs-4973148/v1

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GRASLND has been shown to be a potent biomarker in a variety of cancers. However, studies on GRASLND and bladder cancer have been limited. Therefore, this study aimed to analyze the expression of GRASLND and their relationship with clinicopathological features, prognosis and immune cell infiltration in patients of bladder cancer. The RNA sequencing data and clinical information of 412 patients with bladder cancer and 19 normal subjects were obtained from The Cancer Genome Atlas database. Our results demonstrated that GRASLND was highly expressed in bladder cancer (P < 0.001) and could effectively distinguish bladder cancer tissue from normal bladder tissue (area under the curve = 0.805). Moreover, the expression of GRASLND was significantly correlated with pathologic stage, pathologic T stage, pathologic N stage, histologic grade and subtype. Kaplan–Meier curves analysis revealed that patients with high gene expression had shorter overall survival and worse prognosis (hazard ratio = 1.46, P = 0.012). Cox regression analysis indicated that GRASLND expression, Age, Pathologic T stage, Pathologic N stage, Pathologic M stage, Pathologic stage and Subtype were risk factors for overall survival. Immunoinfiltration analysis found that the expression of GRASLND was positively correlated with the enrichment abundance of immune cells such as macrophages, Tgd and Tem. Collectively, our results suggested that GRASLND may be an important biomarker for the diagnosis and predicting prognosis of bladder cancer, and may even be a new therapeutic target.

bladder cancer

GRASLND

biomarker

prognosis

immune cell infiltration

Bladder cancer (BCa) ranks among the most prevalent malignant neoplasms within the urinary system and ranks as the tenth most prevalent type of cancer worldwide(Sung et al. 2021). According to global statistics from the year 2020, there were an estimated 573,000 newly diagnosed cases of BCa and approximately 213,000 fatalities resulting from the disease. Projections indicate that by 2040, the incidence and mortality rates of BCa worldwide will escalate by 73% and 87%, respectively(Zhang et al. 2023). Currently, surgery and chemotherapy constitute the primary therapeutic modalities for BCa, demonstrating efficacy in extending patient survival(Szarvas et al. 2020). The five-year survival rate for BCa is estimated to be around 80%; however, the prognosis for patients with advanced stages remains poor, with a high mortality rate attributable to the disease(Jiang et al. 2021). Consequently, the identification of effective biomarkers is of paramount importance for the early detection, treatment, and predicting prognosis of BCa.

GRASLND, also referred to as Long Non-Coding RNF144A-AS1, was initially identified for its role in promoting cartilage formation(Stadler 2020). Long non-coding RNAs (lncRNAs), which are defined as RNA molecules exceeding 200 nucleotides in length(Nemeth et al. 2024), were once considered to be non-functional transcriptional byproducts. However, a growing body of evidence has elucidated their diverse biological functions(Kawasaki et al. 2018). LncRNAs have been implicated in tumorigenesis through mechanisms such as sustaining proliferative signaling, facilitating invasion and metastasis, and promoting angiogenesis(Ma et al. 2024). Due to their distinct expression patterns in different tissues, lncRNAs present considerable potential as innovative biomarkers and therapeutic targets in the field of oncology(Bhan, Soleimani, and Mandal 2017). Research has indicated that GRASLND is significantly correlated with the prognosis of gastric cancer, with evidence suggesting that its downregulation can inhibit the invasive capabilities of gastric cancer cells(Yang et al. 2022). Furthermore, Aiwei Ma et al. demonstrated that GRASLND regulated the progression of melanoma cells through the regulation of the miR-218-5p/STAM2 signaling pathway, positioning GRASLND as a potential prognostic biomarker for tumors(Ma et al. 2024). Therefore, GRASLND is a potential biomarker for tumor prognosis. However, its role in BCa needs to be thoroughly investigated.

In recent years, researchers have found that high expression of GRASLND is associated with unfavorable prognosis and poor outcomes in immunotherapy(Ma et al. 2024). Various immune cell types play critical roles in tumor development and therapeutic responses(Tran et al. 2021). For instance, CD8 + T cells can induce apoptosis through the release of cytotoxic substances or the expression of death ligands(Martínez-Lostao, Anel, and Pardo 2015). Jiayin Sun et al. identified a tumor-suppressive circular RNA, circMGA, which enhances the response to immunotherapy in BCa by promoting the chemotaxis of CD8 T cells(Sun et al. 2023). Conversely, tumor-associated macrophages can facilitate cancer progression and treatment resistance by secreting growth factors, cytokines, and proteases(Tran et al. 2021). Baoying Hu et al. found that inhibiting dendritic cell-specific C-type lectin in tumor-associated macrophages can reactivate anti-tumor immunity and enhance immunotherapy efficacy in muscle-invasive BCa(Hu et al. 2020). Therefore, investigating the correlation between gene expression and immune cell infiltration in BCa is crucial for the development of novel therapeutic strategies.

In this research, we investigated the relationship between GRASLND and BCa utilizing data from The Cancer Genome Atlas (TCGA) – Bladder Urothelial Carcinoma (BLCA) cohort, further assessing the prognostic value of GRASLND in BCa patients. Through RNA sequencing data, we analyzed the differential expression of GRASLND between cancerous and normal tissues. Subsequently, we stratified patients based on clinicopathological characteristics to evaluate the difference in GRASLND expression and its correlation with prognosis. In addition, we investigated the relationship between GRASLND expression and immune cell infiltration, which may be the mechanism of GRASLND promoting the progression of BCa. Collectively, our findings may offer significant insights into potential prognostic biomarkers and therapeutic targets for BCa.

2.1 Collection of data and bioinformatics analysis

The transcriptomic data and clinical information utilized in this study were sourced from the TCGA database (https://portal.gdc.cancer.gov/ ). RNA sequencing data from the TCGA - BLCA project were processed using the STAR workflow and subsequently extracted in TPM format to acquire both gene expression and clinical information. Prognostic data were sourced from an article in Cell (Liu et al. 2018). Data that were clinically uninformative or duplicated were omitted from the analysis. In accordance with the characteristics of the data format, suitable statistical methodologies were identified utilizing the R packages 'stats' and 'car' for the statistical analysis. Additionally, the R package 'ggplot2' was employed for data visualization.

2.2 Receiver operating characteristic (ROC) curve analysis

The 'pROC[1.18.0]' package was employed to conduct ROC curve analysis on data that had been processed prior.

2.3 Kaplan–Meier curve analysis

After further removing samples without prognostic information, We conducted Kaplan–Meier curves analysis utilizing the 'survival' package. The findings were subsequently visualized using the 'survminer' and 'ggplot2' packages.

2.4 Immune infiltration analysis

Through the ‘GSVA[1.46.0]’ package(Hänzelmann, Castelo, and Guinney 2013), we used the single-sample gene set enrichment analysis algorithm to investigate the correlation between GRASLND and 24 distinct immune cell types. These immune cells are all from a published article(Bindea et al. 2013).

2.5 Statistical analysis

All statistical analyses were performed using R software (R version 4.2.1). The GRASLND expressions between tumor tissue and normal tissue were analyzed by the Wilcoxon rank sum test and Wilcoxon signed rank test. The Spearman correlation was used in the immune infiltration analysis. P < 0.05 is regarded as statistically significant.

3.1 Differential expression of GRASLND in BCa and normal tissues

To investigate the association between GRASLND expression and BCa, we obtained the transcriptomic and clinical data of 412 BCa samples and 19 normal samples from the TCGA database. An unpaired analysis revealed that GRASLND was significantly overexpressed in BCa compared to normal bladder tissue (P < 0.001, Fig. 1A). Following this, we conducted a paired sample analysis using 19 BCa tissues and 19 normal tissues, which yielded consistent results, indicating a significant overexpression of GRASLND in BCa tissues (Fig. 1B). The findings from both analytical approaches robustly confirmed the relationship between GRASLND expression and BCa. Furthermore, we constructed ROC curves to assess the diagnostic performance of GRASLND in distinguishing BCa from normal tissue. The area under the curve for GRASLND was calculated to be 0.805 (CI = 0.745–0.866), indicating high effectiveness in differentiating between BCa and normal tissue (Fig. 1C). Lastly, we analyzed differentially expressed genes in BCa and constructed a volcano plot, which highlighted the high expression of GRASLND, as illustrated in Fig. 1D.

At the same time, we analyzed GRASLND expression across 23 prevalent cancer types. Among these, GRASLND exhibited significant overexpression in 13 cancer types, including bladder urothelial carcinoma (P < 0.001), breast invasive carcinoma (P < 0.001), cholangiocarcinoma (P < 0.001), colon adenocarcinoma (P < 0.001), esophageal carcinoma (P < 0.001), head and neck squamous cell carcinoma (P < 0.001), kidney chromophobe (P < 0.001), kidney renal papillary cell carcinoma (P < 0.001), liver hepatocellular carcinoma (P < 0.001), lung adenocarcinoma (P < 0.001), lung squamous cell carcinoma (P < 0.001), pheochromocytoma and paraganglioma (P < 0.01), and stomach adenocarcinoma (P < 0.001).

Consequently, GRASLND may serve as a significant and effective biomarker for BCa.

3.2 Relationship between GRASLND expression and clinicopathological features

We compiled and examined the baseline characteristics of 412 patients diagnosed with BCa (Table 1). Patients were categorized into two groups based on the median expression level of GRASLND: a high-expression group (n = 206) and a low-expression group (n = 206). The analysis indicated the presence of statistically significant disparities in the distribution of patients across the two groups concerning pathologic stage (P < 0.001), histologic grade (P = 0.004), and subtype (P = 0.015). In the context of TNM staging, significant differences were observed between the groups at the T stage (P = 0.023) and N stage (P = 0.009), while no significant difference was noted at the M stage (P = 0.984). Additionally, there were no statistically significant differences in the distribution of patients between the two groups concerning age (P = 0.112), sex (P = 0.179), and BMI (P = 0.201).

Table 1

Relationship between GRASLND expression and clinicopathological features of bladder cancer.
	GRASLND expression
characteristics	Low (n = 206)	High (n = 206)	pvalue
Age, n (%)			0.112
<= 70	124 (30.1%)	108 (26.2%)
> 70	82 (19.9%)	98 (23.8%)
Gender, n (%)			0.179
Male	146 (35.4%)	158 (38.3%)
Female	60 (14.6%)	48 (11.7%)
BMI, n (%)			0.201
<= 25	82 (22.7%)	70 (19.3%)
> 25	99 (27.3%)	111 (30.7%)
Pathologic T stage, n (%)			0.023
T1	3 (0.8%)	2 (0.5%)
T2	69 (18.3%)	49 (13%)
T3	80 (21.2%)	116 (30.7%)
T4	29 (7.7%)	30 (7.9%)
Pathologic N stage, n (%)			0.009
N0	132 (35.9%)	106 (28.8%)
N1	17 (4.6%)	29 (7.9%)
N2	28 (7.6%)	49 (13.3%)
N3	3 (0.8%)	4 (1.1%)
Pathologic M stage, n (%)			0.984
M0	109 (51.4%)	92 (43.4%)
M1	6 (2.8%)	5 (2.4%)
Pathologic stage, n (%)			<0.001
Stage I	3 (0.7%)	1 (0.2%)
Stage II	83 (20.2%)	46 (11.2%)
Stage III	68 (16.6%)	74 (18%)
Stage IV	52 (12.7%)	83 (20.2%)
Histologic grade, n (%)			0.004
Low grade	17 (4.2%)	4 (1%)
High grade	189 (46.2%)	199 (48.7%)
Subtype, n (%)			0.015
Papillary	78 (19.2%)	56 (13.8%)
Non-Papillary	124 (30.5%)	149 (36.6%)

The application of logistic regression to investigate the association between GRASLND expression and clinicopathological characteristics in BCa patients is presented in Table 2. The findings from the logistic regression analysis align closely with prior conclusions. Notably, a statistically significant difference was observed between the T3 and T4 stages compared to the T1 and T2 stages, with an odds ratio (OR) of 1.891 (95% confidence interval (CI): 1.223–2.925, P = 0.004). Additionally, a significant difference was identified between the N1, N2, and N3 stages relative to the N0 stage (OR = 2.127 (95% CI: 1.372–3.298), P < 0.001). Moreover, a notable difference was observed between stages III and IV in contrast to stages I and II (OR = 2.394 (95% CI: 1.561–3.671), P < 0.001). Moreover, a significant difference was found between high and low histologic grades (OR = 4.475 (95% CI: 1.479–13.540), P = 0.008), as well as between papillary and non-papillary BCa (OR = 1.674 (95% CI: 1.102–2.542), P = 0.016). However, GRASLND expression did not demonstrate a statistically significant difference concerning age (P = 0.112), sex (P = 0.180), body mass index (P = 0.202), or M stage (P = 0.984).

Table 2

The correlation between GRASLND expression and clinicopathological features of bladder cancer (logistic regression)
Characteristics	Total (N)	OR (95% CI)	P value
Age (> 70 vs. <= 70)	412	1.372 (0.928–2.028)	0.112
Gender (Female vs. Male)	412	0.739 (0.476–1.149)	0.180
BMI (> 25 vs. <= 25)	362	1.313 (0.864–1.996)	0.202
Pathologic T stage (T3&T4 vs. T1&T2)	378	1.891 (1.223–2.925)	0.004
Pathologic N stage (N1&N2&N3 vs. N0)	368	2.127 (1.372–3.298)	< 0.001
Pathologic M stage (M1 vs. M0)	212	0.987 (0.292–3.340)	0.984
Pathologic stage (Stage III&Stage IV vs. Stage I&Stage II)	410	2.394 (1.561–3.671)	< 0.001
Histologic grade (High grade vs. Low grade)	409	4.475 (1.479–13.540)	0.008
Subtype (Non-Papillary vs. Papillary)	407	1.674 (1.102–2.542)	0.016

To further elucidate the relationship between GRASLND expression and clinicopathological features, multiple stratified analyses were conducted. The expression levels of GRASLND in samples from the T3 and T4 stages were significantly elevated compared to normal samples, with statistical significance confirmed. A significant difference was also noted between T3 and T2 stage samples (Fig. 2A). In terms of N stages, GRASLND expression in samples from N0, N1, and N2 stages was significantly higher than in normal samples; however, no statistical difference was observed among the N stage samples (Fig. 2B). The expression of GRASLND in M0 stage tissues was significantly greater than that in normal tissues, while no significant difference was found between M1 and normal samples (Fig. 2C). GRASLND expression was significantly higher in samples from stages III and IV compared to normal samples, with statistical differences also noted between the III and II stages, as well as between the IV and II stages (Fig. 2D). Furthermore, GRASLND expression in high-grade samples was significantly elevated compared to both normal and low-grade samples (Fig. 2E). Lastly, GRASLND expression was higher in both papillary and non-papillary BCa samples compared to normal samples, with a statistically significant higher expression in non-papillary BCa samples compared to papillary samples (Fig. 2F).

3.3 Role of GRASLND expression in survival of patients with BCa

Kaplan–Meier survival curves were utilized to assess the prognostic significance of GRASLND expression in patients diagnosed with BCa. Analysis of the overall survival curve for a cohort of 411 patients indicated that those exhibiting high levels of GRASLND expression had lower survival and worse prognosis compared to their counterparts with low expression levels, with this finding reaching statistical significance (HR = 1.46, 95% CI: 1.09–1.96, P = 0.012) (Fig. 3A). Conversely, the analysis of disease-specific survival curves revealed no statistically significant differences between the two groups (Fig. 3B).

To enhance the predictive accuracy regarding overall survival in BCa patients, we performed a Cox regression analysis, treating overall survival as the dependent variable (Table 3). The results of the univariate analysis showed that GRASLND expression, Age, Pathologic T stage, Pathologic N stage, Pathologic M stage, Pathologic stage and Subtype were significantly correlated with overall survival, including low vs high GRASLND expression (HR = 1.459 (1.086–1.960), P = 0.012), age < = 70 vs > 70 (HR = 1.424 (1.064–1.906), P = 0.018), pathologic stage T1&T2 vs T3&T4 (HR = 2.157 (1.485–3.132), P < 0.001), pathologic stage N0 vs N1&N2&N3 (HR = 2.250 (1.649–3.072), P < 0.001), pathologic stage M0 vs M1 (HR = 3.112 (1.491–6.493), P = 0.002), pathologic stage I&II vs III&IV (HR = 2.267 (1.567–3.281), P < 0.001), and papillary vs non-papillary (HR = 1.450 (1.025–2.052), P = 0.036). At last, after multifactor analysis, only the pathologic N stage was found to be an independent risk factor for overall survival, pathologic stage N0 vs N1&N2&N3 (HR = 1.856 (1.056–3.262), P = 0.032).

Table 3

Univariate and multivariate Cox regression analysis of prognostic factors of OS in bladder cancer.
		Univariate analysis		Multivariate analysis
Characteristics	Total(n)	Hazard ratio(95% CI)	P value	Hazard ratio(95% CI)	P value
GRASLND	411
Low	206	Reference		Reference
High	205	1.459 (1.086–1.960)	0.012	1.552 (0.917–2.627)	0.101
Age	411
<= 70	231	Reference		Reference
> 70	180	1.424 (1.064–1.906)	0.018	1.225 (0.753–1.993)	0.413
Gender	411
Male	303	Reference
Female	108	1.152 (0.835–1.590)	0.390
BMI	361
<= 25	151	Reference
> 25	210	1.000 (0.721–1.386)	0.998
Pathologic T stage	377
T1&T2	123	Reference		Reference
T3&T4	254	2.157 (1.485–3.132)	< 0.001	3.095 (0.925–10.362)	0.067
Pathologic N stage	367
N0	238	Reference		Reference
N1&N2&N3	129	2.250 (1.649–3.072)	< 0.001	1.856 (1.056–3.262)	0.032
Pathologic M stage	212
M0	201	Reference		Reference
M1	11	3.112 (1.491–6.493)	0.002	1.398 (0.526–3.714)	0.502
Pathologic stage	409
Stage I&Stage II	133	Reference		Reference
Stage III&Stage IV	276	2.267 (1.567–3.281)	< 0.001	0.446 (0.118–1.682)	0.233
Histologic grade	408
Low grade	21	Reference		Reference
High grade	387	2.960 (0.732–11.959)	0.128	1.907 (0.246–14.786)	0.537
Subtype	406
Papillary	133	Reference		Reference
Non-Papillary	273	1.450 (1.025–2.052)	0.036	1.158 (0.651–2.060)	0.617

3.4 Correlation between GRASLND expression and immune infiltration

Aiwei Ma and colleagues identified a notable correlation between increased expression of GRASLND and the outcomes of immunotherapy in melanoma cases(Ma et al. 2024). To assess this correlation, we employed the single-sample gene set enrichment analysis algorithm to evaluate the relationship between GRASLND expression and the abundance of 24 distinct immune cell types, subsequently illustrating the results in a lollipop graph (Fig. 4A). In this graph, the size of each dot and its distance from the baseline indicate the strength of the correlation between the abundance of a specific immune cell and GRASLND expression, while the color of the dot reflects the corresponding p-value. We then focused on the three immune cell types exhibiting the strongest correlations and the most significant p-value differences for further analysis. In the context of BCa, GRASLND expression demonstrated a positive correlation with macrophages (R = 0.358, P < 0.001), Tgd (R = 0.295, P < 0.001), and Tem (R = 0.243, P < 0.001) (Fig. 4B-D). Finally, we categorized the samples based on the median level of GRASLND expression and observed that the abundance of macrophages, Tgd, and Tem in the high-expression group was significantly greater than that in the low-expression group (Fig. 4E-G).

BCa ranks among the most prevalent cancers globally and is the fourth most common malignancy in males(He et al. 2021). Over the past decade, it has been reported that more than 200,000 individuals succumb to BCa annually worldwide(Peng et al. 2024). This malignancy can be categorized into two types: non-muscle invasive and muscle-invasive BCa. The latter constitutes approximately 25% of all cases and is characterized by its aggressive nature, rapid progression, early metastasis, and elevated rates of recurrence and mortality(Huang et al. 2024). Consequently, there is a pressing need to identify novel biomarkers for the early diagnosis, treatment, and prognostic evaluation of BCa.

In recent years, lncRNAs have emerged as a significant area of research. Genome-wide association studies have identified lncRNAs in tumor samples that are associated with various cancers, indicating their potential as innovative biomarkers and potential therapeutic targets(Bhan, Soleimani, and Mandal 2017). For instance, lncRNA CCAT1 has been shown to enhance the proliferation, migration, and invasion of BCa cells(Zhang et al. 2019). Additionally, lncRNA RP11-89 has been implicated in promoting tumorigenesis and resistance to ferroptosis in BCa by facilitating PROM2-mediated iron output through the sponging of miR-129-5p(Luo et al. 2021). LncRNA RNF144A-AS1, also referred to as GRASLND, has been recognized for its significant role in the progression of several cancers. GRASLND has been shown to promote the advancement of skin cutaneous melanoma by targeting the miR-218-5p/STAM2 axis. Furthermore, elevated levels of GRASLND are associated with poor prognostic outcomes and diminished efficacy of immunotherapy(Ma et al. 2024). In gastric cancer, GRASLND is markedly upregulated and correlates with poor prognosis and advanced disease stages, facilitating metastasis and proliferation through the miR-30c-2-3p/LOX axis(Li et al. 2021). Research by Binghai Chen et al. has indicated a close relationship between GRASLND and the prognosis of papillary renal cell carcinoma. However, the specific role of GRASLND in BCa warrants further investigation, which is the objective of this study.

For our analysis, we utilized the TCGA-BLCA cohort from the public database. Analysis of transcriptome data from both unpaired and paired samples revealed that GRASLND is significantly overexpressed in BCa tissues compared to normal tissues. The ROC curve analysis further indicated that GRASLND serves as an effective biomarker for differentiating BCa from normal tissue. Additionally, we investigated the correlation between GRASLND expression and various clinicopathological features, finding significant associations with pathological stage, histological grade, and subtype. Subgroup analyses based on these clinicopathological characteristics suggest that high GRASLND expression may be linked to disease progression. Notably, GRASLND expression levels in non-papillary BCa were significantly higher than those in papillary BCa. Therefore, GRASLND may serve as a valuable biomarker for the diagnosis of BCa, with its expression levels potentially predictive of disease progression and tissue type.

Subsequent Kaplan-Meier curve analysis indicated that patients exhibiting elevated expression levels of GRASLND experienced reduced overall survival and a poorer prognosis compared to those with lower expression levels. This finding was statistically significant, suggesting that GRASLND may serve as a biomarker for overall survival in individuals diagnosed with BCa. However, no statistically significant differences were observed in the Kaplan-Meier curve results concerning disease-specific survival, implying that GRASLND may not be a reliable predictor of specific survival outcomes in this patient population. Then cox regression was used to analyze the influencing factors of overall survival. GRASLND expression, Age, Pathologic T stage, Pathologic N stage, Pathologic M stage, Pathologic stage and Subtype were significantly correlated with overall survival. Notably, the pathologic N stage is an independent risk factor. Consequently, the integration of GRASLND expression with these clinicopathological characteristics may enhance the prognostic prediction for patients with BCa.

In the end, the single-sample gene set enrichment analysis algorithm and Spearman correlation were used to analyze the relationship between GRASLND expression and immune infiltration within BCa tissue. The enrichment abundance of macrophages, Tgd and Tem exhibited a positive correlation with GRASLND expression, with these immune cells being significantly overrepresented in BCa relative to normal tissues. Macrophages constitute a primary component of leukocyte infiltration in the tumor microenvironment, with M1 and M2 macrophages being the two predominant subtypes(Ngambenjawong, Gustafson, and Pun 2017). Increasing evidence suggests that M2 macrophages facilitate tumor proliferation, invasion, and metastasis(Jiang et al. 2022). Tumor-associated macrophages have been implicated in the progression of BCa through mechanisms that enhance cell growth, migration, invasion, and cytokine production(Huang, Liu, and Shyr 2020). Additionally, macrophage presence has been associated with prognosis and response to systemic therapies in patients with muscle-invasive BCa(Koll et al. 2023). Elevated infiltration levels of Tgd have also been observed in the immune microenvironments of various cancers, including hepatocellular carcinoma(Xie et al. 2021), lung adenocarcinoma(Tong et al. 2023), oral squamous cell carcinoma(Sun et al. 2022), etc. Moreover, high levels of Tem infiltration correlate with improved prognoses in several malignancies, such as gastric cancer(Ning et al. 2020), colon cancer(Ding et al. 2023), melanoma(Jiang et al. 2024), and so on. The efficacy of Bacillus Calmette Guerin therapy against superficial BCa is also contingent upon the activity of immune cells, such as Tem(Reale et al. 2002). The aggregation and infiltration of immune cells represent critical mechanisms of anti-tumor activity within the human body. The overexpression of GRASLND appears to significantly enhance the invasion of macrophages, Tgd, Tem and other factors. Therefore, GRASLND may not only predict the prognosis of patients with BCa, but may even become a new therapeutic target.

This study does have certain limitations. Firstly, the sample size is relatively small, necessitating larger datasets for further validation. Secondly, the analysis was conducted using data sourced from public databases; thus, future research should involve the collection of tissue or cell specimens for verification purposes. Lastly, while this study establishes GRASLND as a potential biomarker for BCa and a prospective therapeutic target, additional cellular and animal studies are required to elucidate the biological processes associated with GRASLND and to develop innovative treatment strategies.

In conclusion, the findings of this study indicated that GRASLND may serve as a significant biomarker for the detection of BCa. The expression levels of GRASLND have the potential to predict both the extent of BCa progression and the specific tissue type involved. Furthermore, GRASLND expression demonstrates a substantial correlation with the overall survival rates of BCa patients, and when analyzed alongside clinicopathological characteristics, it can effectively forecast patient prognosis. Additionally, the overexpression of GRASLND is closely associated with the infiltration of immune cells within BCa, indicating its potential as a novel and critical therapeutic target for this malignancy.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval

All the data used in this study were from TCGA database and no ethical application was required.

Funding

This work was supported by the Innovation Fund of Beijing Aerospace General Hospital (NO.2023 − 713).

Author Contribution

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yuezheng Li, Taozhu Fu and Weiren Li. The first draft of the manuscript was written by Ziang Wu and Hongrun Chen. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Acknowledgement

The authors would like to thank the TCGA database for the data provided, the support of the Innovation Fund of Beijing Aerospace General Hospital (NO.2023-713), and Jieluoxuan and Xiantao Academic for the bioinformatics knowledge provided.

Data Availability

The datasets generated during and/or analysed during the current study are available in the TCGA repository, https://portal.gdc.cancer.gov/.

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GRASLND is a prognostic biomarker and associated with immune infiltration in bladder cancer

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Abstract

Figures

1 Introduction

2 Methods

2.1 Collection of data and bioinformatics analysis

2.2 Receiver operating characteristic (ROC) curve analysis

2.3 Kaplan–Meier curve analysis

2.4 Immune infiltration analysis

2.5 Statistical analysis

3 Results

3.1 Differential expression of GRASLND in BCa and normal tissues

3.2 Relationship between GRASLND expression and clinicopathological features

3.3 Role of GRASLND expression in survival of patients with BCa

3.4 Correlation between GRASLND expression and immune infiltration

4 Discussion

5 Conclusion

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References

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