Identification of SNHG16 and Its Derived Tumor Immune Gene Signatures for Predicting Prognosis and Efficacy of Immunotherapy in Bladder Cancer

doi:10.21203/rs.3.rs-2118651/v1

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Identification of SNHG16 and Its Derived Tumor Immune Gene Signatures for Predicting Prognosis and Efficacy of Immunotherapy in Bladder Cancer

https://doi.org/10.21203/rs.3.rs-2118651/v1

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Background

In the era of individualized therapy,there is a great need to incorporate lncRNAs into preclinical models to develop prognostic or therapeutic biomarkers.We sought to apply the tumor immune-associated lncRNA – SNHG16 to develop and validate a predictive model constructed from SNHG16 and associated immune gene signatures in BC patients obtained from independent public datasets and clinical internal cohorts, to assess the benefit of immune checkpoint inhibitors treatment and the prognosis in BC patients.

Materials and methods

A novel immune-related and independent predictive model was developed for prognosis and immunotherapeutic evaluation of bladder cancer, based on the identification and analysis of the immune-related SNHG16. Based on the training (TCGA-BLCA) and external validation datasets, the SNHG16-associated immune gene signature was applied to classify BC patients into low- and high-risk groups. Cell proliferation assay and Transwell assay were used to detect the function of related genes.

Results

There were significant differences in prognosis and response to immunotherapy among patients in the different risk groups. Univariate and multivariate analyses confirmed the SNHG16-associated immune gene signature to be an independent predictor of BC prognosis. In addition,in vitro functional assay data confirmed the cancer biological function of SNHG16 and its associated immune signature genes.

Conclusions

Immune-associated lncRNA-SNHG16 and its associated immune gene signatures are reliable tools for predicting BC prognosis and response to immunotherapy and may provide valuable insights for deciding the treatment for BC. Further, the model will provide useful guidance for clinical judgment and personalized regimen-selection for immunotherapy of bladder tumors.

lncRNA

SNHG16

bladder cancer

prognosis

immunotherapy

Bladder cancer (BC) is the most common urological tumor worldwide. With advances in diagnosis, surgery, radiation therapy, and molecular therapeutics, clinical outcomes for bladder cancer patients have improved significantly. However, the five-year survival rates of BC remain low [1]. Evidence suggests that the discovery and application of new molecular biomarkers in conjunction with therapeutic direction may provide useful guidance for prognosis and efficacy assessment.

Recently, immune checkpoint inhibitors (ICIs) have emerged as a revolutionary cancer immunotherapy, acting by targeting immune checkpoints [2–4]. However, to date, only a fraction of patients has achieved substantial benefits from ICI therapy. Candidate biomarkers that can help in clinical selection of patients for ICI therapy include programmed death ligand 1 (PD-L1) expression, tumor mutational load (TMB), defective mismatch repair (dMMR) / microsatellite instability high (MSI-H), and neo-antigenic load (NAL), but these approaches are limited by small percentage populations, spatiotemporal heterogeneity, and moderate accuracy [5]. Therefore, in the era of individualized therapy, there is an urgent need for new prognostic classifiers or therapeutic biomarkers to improve the clinical benefits of ICI therapy for BC patients.

Among the newly identified candidate tumor-prognostic and therapeutic biomarkers, long non-coding RNAs (lncRNAs) (length > 200 nucleotides with mRNA-like transcripts but no protein-coding capability) have attracted a lot of attention from researchers [6, 7]. A growing number of studies have shown that lncRNAs are closely associated with tumorigenesis, progression, prognosis, and drug resistance and susceptibility [8–11]. Notably, emerging evidence reveals an important role for lncRNAs in immunomodulatory responses involving immune cell development, differentiation and effector functions, tumor immune micro-environment regulation, and efficacy of immunotherapy [12–14]. Therefore, there is a great need to incorporate lncRNAs into preclinical models to develop prognostic or therapeutic biomarkers.

In this study, we sought to apply the tumor immune-associated lncRNA – SNHG16 to develop and validate a predictive model constructed from SNHG16 and associated immune gene signatures in BC patients obtained from independent public datasets and clinical internal cohorts, to assess the benefit of ICI treatment and the prognosis in BC patients. We believe this work will help to reveal the role of lncRNAs in tumor immune response in optimization of precise therapy to improve clinical outcomes in BC patients.

SNHG16 expression and prognosis

By analyzing the data of bladder cancer from the Cancer Genome Atlas (TCGA) database, we found that the expression of SNHG16 in cancer tissues was increased (Fig. 1A). The survival rate of patients with high expression of SNHG16 was significantly lower than that of patients with low expression of SNHG16 (Fig. 1B). The expression level of SNHG16 in cancer tissues was indeed higher than that in adjacent tissues by collecting tumor tissues and adjacent tissues from 20 bladder cancer patients (Fig. 1C). The univariate Cox regression analysis showed that clinical stage [hazard ratio (HR) 1.720, 95% confidence interval (CI) 1.423, 2.080, p < 0.001] and SNHG16 expression (HR 1.522, 95% CI 1.130, 2.050, p = 0.006) were associated with poor prognosis in bladder cancer. The multivariate Cox regression analysis also showed clinical stage (HR 1.729, 95% CI 1.428, 2.093, p < 0.001) and SNHG16 expression (HR 1.681, 95% CI 1.242, 2.276, p < 0.001) as the factors related to poor prognosis (Fig. 1D). We thus found that we could use the expression of SNHG16 to guide the prognosis.

Association Of Snhg16 With Various Immune Cell Infiltrations

We evaluated the level of immune cell infiltration under different expressions of SNHG16. The levels of T helper 2 (Th2) cells (p < 0.001), central memory T cells (Tcm) (p < 0.001), T gamma delta cells (Tgd) (p = 0.004) and T helper cells (p = 0.028) were higher in the high-expression group than those in the low-expression group. The levels of natural killer cells (NKs) (p = 0.001), cytotoxic cells (p = 0.001), NK CD56 bright cells (p < 0.001), and plasmacytoid dendritic cells (pDC) (p < 0.001) were lower in the high-expression group than in the low-expression group (Fig. 2A-I).

Model Establishment

We first identified the differentially expressed genes related to SNHG16, and then selected the differentially expressed genes related to immunity from these genes. We then screened the differentially expressed genes related to prognosis by univariate Cox regression analysis (p < 0.05) (Fig. 3A). The risk signature was built using differentially expressed genes related to prognosis via Least absolute shrinkage and selection operator (LASSO) Cox regression and the risk score was then calculated (Fig. 3B-C). The prognosis was worse when the expression of four genes that made up the model was low, but the difference in the expressions in tumor and normal tissues, of the four genes constituting the model, was not statistically significant (Fig. 3D-H).

The Risk Signature And Prognosis Of Bladder Cancer Patients

We divided the patients into a high-risk group and a low-risk group based on the median of the risk score (Fig. 4A). We then confirmed that this grouping method could distinguish high-risk and low-risk patients (Fig. 4B-C). The Kaplan-Meier curve showed that the prognosis of the high-risk group was significantly worse than that of the low-risk group (p < 0.001) (Fig. 4D). The area under the curve (AUC) for the receiver operating characteristic (ROC) curve was 0.705 at 1 year, 0.671 at 3 years and 0.662 at 5 years, respectively (Fig. 4E). The univariate Cox regression analysis showed that clinical stage (HR 1.848, 95% CI 1.476, 2.312, p < 0.001), T stage (HR 1.652, 95% CI 1.299, 2.101, p < 0.001), N stage (HR 1.572, 95% CI 1.333,1.855, p < 0.001) and risk score (HR 3.322, 95% CI 2.228, 4.954, p < 0.001) were associated with poor prognosis in bladder cancer (Fig. 4F). Multivariate Cox regression analysis showed that risk score (HR 2.937, 95% CI 1.925, 4.481, p < 0.001) and N stage (HR 1.430, 95% CI 1.064, 1.922, p = 0.018) were factors related to poor prognosis (Fig. 4G). Thus, the risk signature could independently predict poor prognosis in bladder cancer patients.

The gene expression omnibus (GEO) database was used to validate the signature. We divided patients into high-risk and low-risk groups based on the median risk score of TCGA (Fig. 4H). We then confirmed that this grouping method could distinguish high-risk and low-risk patients of GEO(Fig. 4I-J).Kaplan-Meier curve also showed that the prognosis of the high-risk group was significantly worse than that of the low-risk group (p = 0.001) (Fig. 4K). The AUC for the ROC curve was 0.666 at 1 year, 0.612 at 3 years, and 0.601 at 5 years, respectively (Fig. 4L).

Correlation Of Risk Score With Tumor Microenvironment (Tme)

Analysis of immune cell infiltration revealed that Macrophages M0 and Macrophages M1 were enriched in the high-risk group, while B cells, plasma cells, T cells CD8 and T cells regulatory (Tregs) were enriched in the low-risk group (Fig. 5A). We found that the benefits of immunotherapy were better in the high-risk group (p < 0.01) (Fig. 5B). We found that risk score positively correlated with stromal score (p = 0.012) (Fig. 5C) and negatively with immune score (p = 0.043) (Fig. 5D). We also examined the relationship between risk scores and a number of representative immune-related genes. We found that risk score was positively correlated with CD274 and LAG3(Fig. 5E-F). Hence, we found the risk score to significantly correlate with the tumor microenvironment. Cell cycle, Extracellular matrix receptor interaction, Focal adhesion, MAPK signaling pathway, Melanoma and Neuroactive ligand receptor interaction were enriched in the high risk group, and Olfactory transduction was enriched in the low risk group by GSEA analysis(Fig. 5G).

The Association Of Risk Scores With Numerous Functions

The pathways enriched in the high-risk group are shown in Tables 1, respectively. The pathways enriched in the low-risk group are shown in Tables 2, respectively.

Table 1

The pathways enriched in the high-risk group
NAME	NES	NOM p-val	FDR q-val
KEGG_CELL_CYCLE	2.231541	0	0
KEGG_GAP_JUNCTION	1.9185518	0	0.015227
KEGG_RENAL_CELL_CARCINOMA	1.828499	0.001984	0.031074
KEGG_WNT_SIGNALING_PATHWAY	1.857431	0.002012	0.028061
KEGG_ERBB_SIGNALING_PATHWAY	1.73504	0.003945	0.046165
KEGG_TGF_BETA_SIGNALING_PATHWAY	1.7416348	0.005917	0.047335
KEGG_PROSTATE_CANCER	1.6759052	0.008081	0.058566
KEGG_BLADDER_CANCER	1.5965015	0.018256	0.079663
KEGG_P53_SIGNALING_PATHWAY	1.5792774	0.023256	0.084684
KEGG_MAPK_SIGNALING_PATHWAY	1.4932407	0.031873	0.112134

Table 2

The pathways enriched in the low-risk group
NAME	NES	NOM p-val	FDR q-val
KEGG_OXIDATIVE_PHOSPHORYLATION	-1.68395	0.037849	0.247062
KEGG_PARKINSONS_DISEASE	-1.61419	0.04878	0.237247

Proliferation And Metastasis Of Bladder Cancer Cells

We first knocked down SNHG16 in bladder cancer cells, the proliferation of the cells was decreased (Fig. 6F). And then we knocked down CTSE, CCL17, UNC93B1 and TNFRSF14 separately in four groups of bladder cancer cells, and then the proliferation and metastasis of these four groups of bladder cancer cells were increased (Fig. 6A-J).

Of late, there has been growing evidence that lncRNAs play an important role in the regulation of the immune system [15–18]. For example, the lncRNA Nron has been shown to maintain the resting state of T cells by chelating phosphorylation of cytosolic factors in the cytoplasm [19]. The oncogenic gene link A downregulates antigen presentation and intrinsic tumor suppression in cancer cells [20]. LncRNA nesting has been found to be associated with T cell activation and is essential for regulation of immune responses [21]. LncRNAs are key regulators of gene expression in the immune system and play a critical role in the development and progression of cancer. However, to date, only a few immune-related lncRNAs have been found to play a role in cancer. Therefore, further studies on tumor immune-related lncRNAs and their roles in immune regulation will be crucial in identifying immunotherapeutic targets in cancer.

In this study, we developed a novel immune-related and independent predictive model for prognosis and immunotherapeutic evaluation of bladder cancer, based on the identification and analysis of the immune-related lncRNA – SNHG16. Based on the training (TCGA-BLCA) and external validation datasets, the SNHG16-associated immune gene signature was applied to classify BC patients into low- and high-risk groups. As expected, there were significant differences in prognosis and response to immunotherapy among patients in the different risk groups. Univariate and multivariate analyses confirmed the SNHG16-associated immune gene signature to be an independent predictor of BC prognosis. In addition, our in-vitro functional assay data confirmed the cancer biological function of SNHG16 and its associated immune signature genes. Thus, immune-associated lncRNA-SNHG16 and its associated immune gene signatures are reliable tools for predicting BC prognosis and response to immunotherapy and may provide valuable insights for deciding the treatment for BC.

Over the past decade, anticancer immunotherapies targeting PD-1/PD-L1 have achieved positive responses in BC patients [22, 23]. However, only some BC patients can respond to immunotherapy, mainly due to the limitations in their tumor immune status. To determine whether the prediction model constructed by immune-associated lncRNA-SNHG16 and associated immune gene signatures could predict the effectiveness of anticancer immunotherapy in BC patients, we measured the expression levels of several common immune checkpoints in the low- and high-risk populations and found that more than half of them significantly correlated with risk levels, and that the low-risk group had higher immune scores. Considering the importance of immune infiltration in the tumor ecosystem, we also used the ssGSEA algorithm to estimate the activity of TME cells in the low- and high-risk populations. Eleven types of immune cells were significantly correlated with the risk score. According to survival analysis, a higher abundance of activated B cells, effector memory CD8⁺ T cells and activated CD8⁺ T cells in the low-risk group may contribute to a better prognosis. These results suggest that the poor survival outcome of patients in the high-risk group may be due to the low infiltration by these protective immune cells, and therefore the low objective response rate to immunotherapy, consistent with previous observations [24]. These results suggest that patients in different risk groups may respond differentially to immunotherapy and that the predictive models constructed by immune-associated SNHG16 and associated immune gene signatures may be potential biomarkers for predicting the effectiveness of BC immunotherapy.

Identification and characterization of potential targets of immune-associated lncRNAs are key to investigating their functions and mechanisms. In the current study, we identified genes associated with lncRNA expression and further investigated potential immune pathways. All genes involved are either associated with the immune system or tumorigenesis. SNHG16 has been found to promote proliferation, invasion, and drug resistance in a variety of solid tumors, including bladder cancer. In a predictive model constructed from the SNHG16-associated immune gene signature, the tumor necrosis factor (TNF) receptor superfamily member – herpesvirus entry mediator (HVEM) (TNFRSF14) regulates T-cell immune response by activating inflammatory and inhibitory signaling pathways [25]. The histone protease E (Cathepsin E, CTSE), an intracellular hydrolytic aspartate protease, is expressed in immune and gastrointestinal system cells, lymphoid tissue, erythrocytes, and cancer cells, and is involved in protein conversion, regulation of immune responses, and apoptosis [26]. TLR7 promotes or suppresses B-cell-mediated autoimmune responses, and the TLR7-specific protein molecular chaperone – UNC93B1 is essential for maintaining TLR stability and signaling activity after synthesis [27]. The chemotactic protein CCL17 binds specifically to CCR4 and has a role in attracting immune cells to specific sites and inducing chemotaxis [28]. Systematic exploration of the different signature gene sets between the low- and high-risk groups provided additional insights into the mechanisms of immune regulation of SNHG16 tumors. Those signature pathways with increased levels in the high-risk group were found to be associated with well-known oncogenic signaling pathways, including the cell cycle pathway, MAPK pathway, nuclear-receptor pathway, and cell-adhesion pathway [29–31]. These preliminary data strongly suggest an intrinsic link between immune-derived signaling and oncogenic pathways and could provide additional clues or new strategies for drug candidate discovery in the future. An increasing number of genetic signatures have been established to elucidate the prognostic classification of BC. For example, Dai et al., developed an eleven-gene prognostic model that showed good performance for prognosis prediction [32]. Zhu et al., reported a six-gene signature for predicting OS [33]. Although there is growing evidence that tumor immune phenotypes are significantly associated with prognosis and treatment response in various types of cancer, few studies have focused on the genetic signature of key genes of the tumor immune microenvironment based on immune-associated lncRNAs. This study has the advantage of being based on a large sample size and high AUC. Unlike previous studies in bladder cancer that have only described prognostic gene signatures, we have performed a comprehensive analysis that has led to a deeper and more comprehensive understanding of risk models and confirmed the cancer biological functions of model components. Thus, the predictive model constructed by immune-associated SNHG16 and associated immune gene signatures may reveal a novel linkage trail closely related to the immune status of BC.

It is worth pointing out that determining how SNHG16 affects related gene expression needs to be done by future studies. Recent studies have found that lncRNAs can function by acting as scaffolding lncRNAs or signaling lncRNAs, among others [34]. We have identified the interaction targets of lncRNAs in the tumor immunomodulatory environment. We believe that identifying these targets will further improve our understanding of SNHG16-mediated regulation of immune pathways and advance our understanding of the functions and mechanisms of immune-related lncRNAs such as SNHG16.

In conclusion, we have constructed a predictive model based on SNHG16 and associated immune gene signatures using a novel “immune-associated lncRNA- guided” strategy to predict prognosis and immunotherapy outcomes in BC patients and have validated this model in multiple ways. Combining these results with the linkage between predictive models and cancer marker pathways, our study provides a new perspective for identifying prognostic classifiers and even for discovering immunotherapy-sensitive patient populations. This will help in understanding the role of lncRNAs in tumor immune responses. Further, the model will provide useful guidance for clinical judgment and personalized regimen-selection for immunotherapy of bladder tumors.

Data collection

The mRNA data and related clinical data were obtained from the Cancer Genome Atlas (TCGA) database (TCGA-BLCA). mRNA data and clinical information also comes from Gene Expression Omnibus (GEO) database (GSE13507, GSE31684, https://www.ncbi.nlm.nih.gov/geo/). In this study, 411 bladder cancer tissues and 19 normal tissues in TCGA database were included to construct risk signature, and 258 bladder cancer organizations in GEO database were used to verify the risk signature. Moreover, an independent cohort of patients with metastatic urothelial cancer (mUC) receiving PD-1 blockade therapy, the IMvigor210 (mUC) trial, was included to evaluate the effects of immunotherapy.

Single Gene Analysis

The expression of SNHG16 in 411 bladder cancer samples and 19 normal tissue samples from TCGA database were compared using “ggplot2” software package of R software. 411 bladder cancer samples from the TCGA database were divided into high SNHG16 expression group and low SNHG16 expression group according to SNHG16 expression level. Kaplan–Meier survival curves were used to compare the overall survival between two groups via the “survival” and “survminer” packages of R software. Univariate Cox regression and multivariate Cox regression were used to confirm whether SNHG16 expression could be an independent prognostic factor for bladder cancer via the “survival” packages of R software.

Immune Cell Infiltration

The ssGSEA algorithm and Spearman statistical analysis were used to detect the relationship between SNHG16 expression and immune cell infiltration.

Risk Signature

Based on immune related genes from “Immport” (https://www.immport.org),we find out the differentially expressed genes in the high and low expression groups of SNHG16. Differentially expressed genes related to survival were screened using univariable Cox regression (P < 0.05). Then, using the selected genes, the risk signature was established by lasso Cox regression, and the risk score was calculated by “glmnet” software package of R software. Bladder cancer patients were divided into high-risk group and low-risk group. Kaplan–Meier survival curves were used to compare the overall survival between two groups. Receiver operating characteristic (ROC) curve was performed to estimate the accuracy of risk signature via the “survival”, “survminer” and “timeROC” package of R software. Univariate Cox regression and multivariate Cox regression were used to confirm whether risk signature could be an independent prognostic factor for bladder cancer. Finally, the risk signature was verified using GEO database. The ssGSEA algorithm was also used to detect the relationship between risk score and immune cells infiltration. The relationships with risk score and TME (stromal score, immune score and tumor purity) were evaluated via the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm.

Biological Functions

The gene set enrichment analysis (GSEA) was performed to analysis the risk score related functions. The “c2.cp.kegg.v7.4.symbols.gmt” and “c5.go.v7.4.symbols.gmt” were included. A nominal P value (NOM P value) < 0.05 and a false discovery rate Q value (FDR Q value) < 0.25 were considered significant.

Cell Culture And Sirna Transfection

Human BC cell lines UMUC3 were purchased from Cell Bank of Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. They were maintained in DMEM medium supplemented with 10% fetal bovine serum (FBS). Cells were incubated at 37°C in 5% CO2. For siRNA transfection, Lipofectamine (Invitrogen) was used. The siRNA sequences were as follows:

CTSE: forward 5’ GAC CUU UGU GGA UGC AGA GUU UGA U 3’

reverse 5’ AUC AAA CUC UGC AUC CAC AAA GGU C 3’

UNC93B1: forward 5’ CAC CUC GUG CCU UUC UUU AUC UAC A 3’

reverse 5’ UGU AGA UAA AGA AAG GCA CGA GGU G 3’

CCL17: forward 5’ GAG UGA AGA AUG CAG UUA AAU ACC U 3’

reverse 5’ AGG UAU UUA ACU GCA UUC UUC ACU C 3’

TNFRSF14:forward 5’ CAU CGU CAU UGU UUG CUC CAC AGU U 3’

reverse 5’ AAC UGU GGA GCA AAC AAU GAC GAU G 3’

Rna Extraction And Real-time Qpcr

We selected samples from 20 patients with bladder cancer, and obtained the cancer tissue and paracancer tissue respectively.

Total RNA was extracted using the Eastep Super TotalRNA Extraction Kit (Promega). miRNA was extractedusing the miRcute miRNA Isolation Kit (Tiangen Bio-tech, Beijing, China). cDNA was synthesized using the Mir-XTM miRNA First-Strand Synthesis Kit (TaKaRa). qPCR was conducted using SYBRGreen PCR Master Mix (TaKaRa) on a LightCycler480 Real-Time PCR instrument (Roche, Indianapolis, IN, USA). Thesequences of the primers were as follows:

SNHG16: forward 5’AAG ACA TGG CCA CTC CAG TC 3’

reverse 5’AGG CTG ACT GCA CCA TCA TC 3’

GAPDH: forward 5’GGA GCG AGA TCC CTC CAA AAT 3’

reverse 5’GGC TGT TGT CAT ACT CTC ATG G 3’

Transwell Assay

Cell migration assay was conducted with a 24-well insert Trans-well chambers (corning Costar, USA). Transfected cells (20000/200µl) were suspended in serum-free medium and then added to the upper chamber. 600µL of complete medium containing 10% FBS was injected into the lower chamber. After 24 h, wipe the cells on the upper filter membrane with a cotton swab, and clean the chamber with PBS. After staining with crystal violet solution for 30 minutes, observed invading and migrating cells.

Cell Proliferation Assays

RTCA S16 (Agilent, USA) was used in cell proliferation assays. Transfected cells (3000/500µl) were suspended in complete medium containing 10% FBS and then added to the well. And the E-Plates placed on the RTCA single plate station in the cell culture incubator. Cell proliferation were monitored every 15 minutes up to 72 hours.

Statistical analysis

When comparing the two groups of data, if the data conforms to the normal distribution, the unpaired t-student test was used, otherwise the Wilcoxon rank sum test was selected. Log rank test was used in survival analysis. All statistical calculations are completed on R software.

Availability of data and materials

The datasets generated and analysed during the current study are available in the Cancer Genome Atlas (TCGA) database (TCGA-BLCA), (http://portal.gdc.cancer.gov) and Gene Expression Omnibus (GEO) database (GSE13507, GSE31684, https://www.ncbi.nlm.nih.gov/geo/).

Acknowledgements

We would like to thank TopEdit (www.topeditsci.com) for its linguistic assistance during the preparation of this manuscript.

Authors’ contributions

Conceived the study: Chuize Kong ，Du Shi and Yuyan Zhu. Performed the experimental work: Yuchen Li ，Danyang Guo and Meng Yu . Bioinformatic analysis: Yuchen Li ，Yang Fu and Yuyan Zhu . Data analysis: Yuchen Li ， Yang Fu ，Yuyan Zhu and Zeliang Li. Wrote the manuscript: Chuize Kong and Du Shi. Edited or revised the manuscript: Chuize Kong and Du Shi . All authors read and approved the final manuscript.

Funding: This work was supported by Shenyang Plan Project of Science and Technology (Grant No. 20-204-4-42) and (Grant No. 21-103-0-13).

Ethics approval and consent to participants

Not applicable.

Consent to publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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No competing interests reported.

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Identification of SNHG16 and Its Derived Tumor Immune Gene Signatures for Predicting Prognosis and Efficacy of Immunotherapy in Bladder Cancer

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Abstract

Background

Materials and methods

Results

Conclusions

Figures

Introduction

Results

SNHG16 expression and prognosis

Association Of Snhg16 With Various Immune Cell Infiltrations

Model Establishment

The Risk Signature And Prognosis Of Bladder Cancer Patients

Correlation Of Risk Score With Tumor Microenvironment (Tme)

The Association Of Risk Scores With Numerous Functions

Proliferation And Metastasis Of Bladder Cancer Cells

Discussion

Conclusion

Materials And Methods

Data collection

Single Gene Analysis

Immune Cell Infiltration

Risk Signature

Biological Functions

Cell Culture And Sirna Transfection

Rna Extraction And Real-time Qpcr

Transwell Assay

Cell Proliferation Assays

Statistical analysis

Declarations

References

Additional Declarations

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