Abstract

   Background: GNG4, a member of the G-protein γ family, is a marker of
   poor overall survival (OS) rates in some malignancies. However, the
   potential role of GNG4 in bladder cancer (BLCA) is unknown. It is also
   unclear whether GNG4 may be utilized as a marker to guide chemotherapy
   or immunotherapy. Methods: Single-cell RNA sequencing data were used to
   explore the expression of GNG4 in tumor microenvironment of BLCA. Bulk
   RNA sequencing data from TCGA were used to evaluate the relationship
   between GNG4 expression and biological features, such as immune cell
   infiltrations and gene mutations. The associations between GNG4
   expression and survival in BLCA patients under or not under
   immunotherapy were evaluated using seven BLCA cohorts. Results: GNG4
   was specifically expressed in exhausted CD4^+ T cells. And the high
   expression of the GNG4 was associated with high level of immune cell
   infiltration. The high-GNG4-expression group displayed a better
   response to immunotherapy, whereas patients in the low-GNG4-expression
   group often benefited from chemotherapy. Moreover, the high-GNG4 group
   was more similar to the basal group, whereas the low-GNG4 group was
   similar to the luminal group. Conclusions: GNG4 may be a potential
   biomarker for the prediction of the response to therapy in BLCA. Higher
   GNG4 expression can be used as a predictor of response to
   immunotherapy, and lower GNG4 expression can be used as a predictor of
   response to chemotherapy.

   Keywords: bladder cancer, G-protein subunit gamma 4, immune
   infiltration, immunotherapy, chemotherapy

1. Introduction

   Bladder cancer (BLCA), a malignancy of the urinary tract, is the second
   most common urinary cancer, with approximately 573,278 new cases and
   212,536 deaths reported in 2020, which means it ranks as the 10th most
   frequently diagnosed cancer worldwide [[40]1,[41]2,[42]3]. It is urgent
   to develop effective treatment strategies to improve the survival rate
   of BLCA. Chemotherapy plays an important role in the comprehensive
   treatment of cancer. However, tumor recurrence often occurs after
   chemotherapy, and over the last 30 years, there have been no
   significant advancements in the treatment of BLCA [[43]4]. Despite the
   advent of neoadjuvant and adjuvant chemotherapy, the treatment outcome
   and prognosis of metastatic BLCA remain unsatisfactory [[44]5]. Cancer
   immunotherapy, such as immune checkpoint blockade (ICB), has resulted
   in promising improvements in survival rates in individuals with
   advanced BLCA [[45]6,[46]7], which is characterized by a high tumor
   mutation burden (TMB) and neoantigens [[47]8]. However, only a minority
   of patients respond to ICB because they are resistant to the primary or
   secondary mechanisms of ICB [[48]6,[49]7]. Although the advent of
   immunotherapy provided another excellent avenue for the treatment of
   BLCA, the results continue to be unsatisfactory [[50]9]. Therefore,
   both chemotherapy and immunotherapy need to continue to improve in
   terms of how effectively they can treat BLCA. In general, there is an
   urgent need to develop novel biomarkers that can be used to effectively
   predict treatment response in BLCA patients.

   As a member of the G-protein γ family, GNG4 normally transduces signals
   from upstream G-protein-coupled receptors (GPCRs)
   [[51]10,[52]11,[53]12,[54]13]. Incidentally, GPCRs consist of a large
   family of receptors that respond to a variety of extracellular stimuli
   such as hormones, growth factors, light, and other sensory stimulus
   signals [[55]14]. Several G-protein family members have been determined
   to be pan-inhibitors and oncogenes of Gβγ, which reduce cell
   proliferation and impede metastasis in breast and prostate cancer cell
   lines [[56]15,[57]16]. Meanwhile, GNG4 has been reported to be a tumor
   suppressor gene in glioblastoma [[58]14]. GNG4 is also used as a
   biomarker in gastric cancer, especially in liver metastasis of gastric
   cancer. High levels of GNG4 in primary tumor tissue have been reported
   to be associated with short overall survival times and the likelihood
   of liver metastasis recurrence [[59]17]. Notably, GNG4 is not only a
   candidate biomarker for the diagnosis of synchronous metastasis and the
   prediction of metachronous metastasis but may also be a predictive
   factor concerning 5FU, a representative pyrimidine drug [[60]17]. GNG4
   was also demonstrated to be the key element of the colorectal carcinoma
   (CRC) tumor mutation burden (TMB), which is essential information in
   the ICB therapy of CRC [[61]18]. However, the role of GNG4 in BLCA
   remains unclear.

   In this study, we integrated the public bulk and single-cell RNA
   sequencing data to comprehensively investigate the GNG4 expression in
   tumor microenvironment and its relationship with immunotherapy response
   in bladder cancer.

2. Materials and Methods

2.1. Data Collection and Processing

   The RNA sequencing (RNA-seq) transcriptome data of patients with BLCA
   and the corresponding clinical data and mutation profiles were
   downloaded from The Cancer Genome Atlas (TCGA) database
   ([62]https://portal.gdc.cancer.gov/, accessed on 12 September 2021).
   Fragments per kilobase million (FPKM) values were converted to
   transcripts per kilobase million (TPM) values, and the Ensembl gene IDs
   of the RNA-seq data were converted to gene symbols concerning the
   annotation file.

   Three BLCA validation datasets were included for analysis from the Gene
   Expression Omnibus (GEO) data portal
   ([63]http://www.ncbi.nlm.nih.gov/geo/, accessed on 24 September 2021)
   ([64]GSE13507, [65]GSE32894, and [66]GSE70691) [[67]19,[68]20,[69]21].
   The raw data were downloaded as microarray data.

   We included three immunotherapeutic cohorts and obtained the relative
   transcriptomic and clinical data from the online [70]Supplementary data
   appended to the published studies. We downloaded RNA sequencing of the
   immunotherapy cohort of BLCA from Mvigor210CoreBiologies, an R package
   [[71]22]. Another immunotherapy cohort of BLCA ([72]GSE176307) was
   included for analysis from the Gene Expression Omnibus (GEO) database
   ([73]http://www.ncbi.nlm.nih.gov/geo/, accessed on 24 September 2021)
   [[74]23]. RNA sequencing of the immunotherapy cohort of melanoma
   (PRJEB23709) was downloaded from GEO [[75]24].

   The copy number variation (CNV) and somatic mutation profiles were also
   obtained from TCGA data. The somatic mutation data sorted in the form
   of Mutation Annotation Format (MAF) were analyzed using the R package
   ‘maftools’.

2.2. Single-Cell Transcriptome Sequencing and Data Preprocessing

   Single-cell transcriptome sequencing data, downloaded from the Genome
   Sequence Archive (GSA) database ([76]https://ngdc.cncb.ac.cn/gsa/,
   accessed on 15 October 2021), were analyzed using the Seurat (v3.1.3) R
   toolkit [[77]25]. First, cells of poor quality were filtered out
   [[78]26]. Then, DoubletFinder was used with the default parameters to
   predict cell doublets. Next, the “NormalizeData” function was used to
   normalize the data, and the “FindVariableFeatures” function was used to
   choose the highly variable genes. Then, the gene expression matrices of
   all of the samples were integrated using the “Runharmony” functions
   [[79]27]. Finally, we determined the different cell types with default
   parameters; the different cell types were visualized using UMAP.

   We used the following rules to annotate the different cell types: Based
   on using the “wilcoxauc” function in presto to derive the top ten
   differentially expressed genes, we identified 8 main clusters and
   annotated them according to the expression of typical gene markers,
   including endothelial cells, epithelial cells, fibroblast cells,
   myeloid cells, mast cells, plasma cells, B cells, and T cells. CD4 and
   CD8 gene expressions were used for the differentiation of CD4^+ and
   CD8^+ T cells. CD4^+ T-cell subclusters were named using the first
   marker gene.

2.3. Functional and Pathway Enrichment Analysis of Bulk RNA-Seq

   The identification of differentially expressed genes between the
   low-GNG4 and the high-GNG4 groups was carried out using the Limma R
   package [[80]28]. To explore the phenotype-specific signaling pathways
   of the tumor microenvironment (TME) in the high-GNG4 and low-GNG4
   groups, the Gene Set Enrichment Analysis (GSEA) approach was used with
   an adjusted p < 0.05 using the ‘clusterfiler’ R package [[81]29].

2.4. Immunological Characteristics of the TME in BLCA

   The proportions of immune cell types (i.e., TICs) evaluated for immune
   cell infiltration in each sample (with immune infiltration scores) were
   computed using the Timer [[82]30], MCP_counter [[83]31], CIBERSORT, and
   Xcell [[84]32] algorithms.

2.5. Prediction of the Molecular Subtypes in BLCA

   The molecular subtype of each individual was determined using the
   ConsensusMIBC R package [[85]33]. All of the patients in the TCGA
   database were divided into six phenotypes.

2.6. Statistical Analysis

   Using the Kaplan–Meier method to estimate OS or FPS, the Kaplan–Meier
   curves were compared using the log-rank test. A two-sided p-value of
   less than 0.05 was regarded as significant. Meanwhile, all of the
   sample sizes were large enough to enable proper statistical analysis.
   Spearman correlation analysis was applied in all of the correlation
   analyses. GraphPad Prism ([86]https://www.graphpad.com/, accessed on 25
   March 2022) was also used to perform statistical analyses. p-values
   less than 0.05 were deemed to be statistically significant. All of the
   t-test analyses were two-sided t-tests (paired or unpaired, depending
   on the experiments).

3. Result

3.1. GNG4 Is a Biomarker of Exhausted CD4^+ T Cells in BLCA

   Unsupervised clustering of the published BLCA single-cell RNA
   sequencing data revealed eight cell clusters ([87]Figure 1A and
   [88]Figure S1A). Intriguingly, GNG4 was specifically expressed in T
   cells ([89]Figure 1B, [90]Table S1). After dividing all CD4^+ T cells
   into six sub-clusters, we found GNG4 only expressed in cluster
   CD4_CXCL13 (GNG4 was not expressed in CD8^+ T cells, and the results
   are not shown) ([91]Figure 1C,D and [92]Figure S1B, Table S2). In
   addition to the high expression of GNG4, this cluster is accompanied by
   the high expression of PDCD1, LAG3, TIGIT, and CXCL13, suggesting that
   the cells of this cluster were in an exhausted state ([93]Figure 1E).
   Overall, we determined that GNG4 is specifically expressed in exhausted
   CD4^+ T cells in BLCA tumor microenvironment.

Figure 1.

   [94]Figure 1
   [95]Open in a new tab

   GNG4 is a biomarker for exhausted CD4^+ T cells population in BLCA.
   (A). The UMAP plot of all cells. Each dot indicates a single cell.
   Color-coded for the cell type. (B). The UMAP plot of GNG4 expression.
   (C). UMAP plot of CD4^+ T cells. Each dot indicates a single cell, and
   colors refer to clusters denoted by CD4^+ T cell types. (D). The
   expression of GNG4 in each type of CD4^+ T cells. (E). Dot plot of
   immune-related genes expressed in types of CD4^+ T cells.

3.2. High Expression of GNG4 Reveals High Immune Infiltration but Tends to Be
Exhausted

   Since GNG4 was found as a biomarker to measure the exhausted CD4^+ T
   cells, we next used the bulk RNA sequencing data to further explore the
   relationship between GNG4 and immune cell infiltration. Hence, we
   obtained and analyzed the clinical data of 406 patients diagnosed with
   BLCA in the TCGA database ([96]Table S3) and investigate the
   association between the level of GNG4 expression and immune-related
   indicators. The result reveals that GNG4 was positively correlated with
   many immunomodulators, including chemokines, receptors, MHC, and
   immunostimulators. ([97]Figure 2A). Many chemokines, such as CCL3,
   CCL4, CCL7, CCL8, CCL11, CXCL9, CXCL10, and paired receptors, including
   CCR1, CCR2, CCR4, CXCR4, and CXCR6, were also positively correlated
   with GNG4. These chemokines and receptors promote the recruitment of
   effector TIICs, including TH17 cells, antigen-presenting cells, and
   CD8^+ T cells [[98]34]. Among them, two vital chemokines (CXCL9 and
   CXCL10), which trigger the recruitment of CD8^+ T cells into the tumor
   microenvironment (TME) in BLCA, were upregulated in the high-GNG4
   group. In addition, a majority of MHC molecules, especially Human
   Leukocyte Antigen (HLA) molecules, were highly expressed in the
   high-GNG4 group, which suggested the upregulation of antigen
   presentation and processing ability in the high-GNG4 group.
   Additionally, many immunostimulators also had high expression in the
   high-GNG4 group, which were able to stimulate the immune system by
   inducing activation or increasing the activity of any of its
   components. The same result was obtained in three BLCA validation
   cohorts including [99]GSE13507, [100]GSE32894, and [101]GSE70691
   ([102]Figure S2A). Surprisingly, in the present study, we found that
   GNG4 was positively related to many immune checkpoint inhibitors,
   including PDCD1, CTLA4, LAG3, IDO1, HAVCR2, and TIGIT ([103]Figure 2B).
   This demonstrated that the high-GNG4 group had an inflammatory but
   inhibitory TME. Similarly, we also found a positive correlation between
   GNG4 and immune checkpoint inhibitors in three validation cohorts
   ([104]Figure S2B). We further analyzed the differential expression of
   genes in the high- and low-GNG4 groups in BLCA ([105]Figure 2C,
   [106]Table S4). Based on these results, the biological functions of
   gene sets in high-GNG4 groups were investigated via Gene Ontology (GO)
   analysis ([107]Figure 2D, [108]Table S5). Interestingly, these genes
   were found to have a strong association with significant
   immunomodulatory processes such as the negative regulation of T-cell
   activation, granulocyte chemotaxis, and the negative regulation of cell
   adhesion.

Figure 2.

   [109]Figure 2
   [110]Open in a new tab

   High-GNG4 group exhibits the high expression of immune-related
   molecules. (A). Differential expression of 122 immunomodulators
   (chemokines, receptors, MHC, and immunostimulators) between high-GNG4
   and low-GNG4 groups in BLCA. (B). Correlation of GNG4 with 20
   inhibitory immune checkpoints. The color indicates the Spearman
   correlation coefficient. (C). Volcano map shows differentially
   expressed genes between high-GNG4 and low-GNG4 groups in BLCA (high:
   logFC > 1 and log p-value > 2; low: logFC < −1 and log p-value > 2).
   (D). Functional comments and pathway enrichment of the genes with GNG4
   co-expression genes. (E,F). Estimation of the abundance of various
   types of immune cells between the high-GNG4 and low-GNG4 groups using
   TIMER_counter and MCP_counter algorithm. *, p-value ≤ 0.05; *** p-value
   ≤ 0.001; ****, p-value ≤ 0.0001; ns, p-value > 0.05.

   To further explore the relationship between GNG4 and immune cell
   infiltration, the MCP counter, xCell, CIBERSORT, and TIMER algorithms
   were used to estimate the levels of various types of immune cells from
   TCGA-BLCA bulk RNA-sequencing data. We showed significantly higher
   levels of Tregs, CD8^+T cells, NK cells, CD4^+T cells, neutrophils,
   macrophages, and B cells in the high-GNG4 group ([111]Figure 2E,F and
   [112]Figure S3A,B). Additionally, the high-GNG4 group had a higher
   level of cytotoxic lymphocytes ([113]Figure 2F). We also used the MCP
   counter to calculate immune scores and estimate the abundance of
   various types of immune cells in three validation sets ([114]Figure
   S2C). Similarly, we confirmed substantial immune cell infiltration in
   the high-GNG4 group in three validation sets. To our surprise,
   fibroblasts also had a higher valuation score in the high-GNG4 group
   ([115]Figure 2F). In addition, the number and diversity of T-cell
   receptors (TCR) were higher in the high-GNG4 group than those in the
   low-GNG4 group ([116]Figure S3C). Taken together, these results also
   show that the high-GNG4 group had the status of a higher level of
   immune cell infiltration environment but tended to be exhausted.

   Genomic mutation is an important factor in the occurrence of malignant
   tumors, and this factor affects treatment. Therefore, we employed the
   ‘maftools’ package to analyze the somatic mutation profile between the
   high- and low-GNG4 groups in the TCGA-BLCA cohort; the top 10 most
   commonly mutated genes in each group are shown in [117]Figure 2A.
   Previously published studies have claimed that genetic variations in
   the tumor suppressor gene TP53 contribute to human cancers in different
   ways, whereas TTN mutation was associated with responses to
   immunotherapy in solid tumors [[118]35,[119]36]. Similar to the
   previous study, TP53 and TTN were critical genetic alterations in the
   pathogenesis of BLCA, and these gene alterations were observed in both
   the high- and the low-GNG4 groups ([120]Figure S4A). Notably, RB1,
   FGFR3, HECTD4, KIAA1109, and TRPM2 occupied the top 5 positions among
   the different mutant genes between high- and low-GNG4 groups
   ([121]Figure S4B, p < 0.001). In recent years, molecular classification
   (i.e., classification on the basis of genetic alterations and
   expression) has diversified our knowledge of BLCA and offered a new
   framework for the stratification and evaluation of responses to
   different treatment approaches [[122]33,[123]37]. The six consensus
   classes (LumNS, LumP, LumU, Ba/Sq, stroma-rich, and neuroendocrine
   (NE)-like) are the most commonly recognized molecular subtypes of BLCA,
   which can be mainly divided into basal and luminal subtypes.
   Interestingly, the mutation rate of RB1 was 26.96% in the high-GNG4
   group, and RB1 was also mutated in a high proportion in the Ba/Sq group
   in the molecular classification of BLCA. Similarly, the mutation rate
   was 21.18% in the low-GNG4 group, which is consistent with the results
   of mutations in the LumP groups in the molecular classification of BLCA
   [[124]33]. Thus, related mutations of GNG4 can also directly classify
   BLCA. Then, we used the MCP counter to estimate the infiltration
   abundance of different immune cells in GNG4 mutant groups. We defined
   patients with RB1 and HECTD4 mutations (the related mutated genes in
   the high-GNG4 group) and those with FGFR3, KIAA1109, and TRPM2
   mutations (the related mutated genes in the low-GNG4 group) as the
   mutation group, and the rest were defined as the wild group (WT). To
   our surprise, the results showed that the associated mutation group of
   the high-GNG4 group also exhibited higher immune cell infiltration,
   whereas the associated mutation group of the low-GNG4 group also
   exhibited lower immune cell infiltration ([125]Figure S4C,D).

3.3. GNG4 as a Biomarker to Predict the Effect of Immunotherapy

   As GNG4 is highly expressed in exhausted CD4^+ T cells and its higher
   expression is correlated with immune infiltration, we investigated
   whether GNG4 could be used as a biomarker to predict the
   immunotherapeutic response to BLCA. Surprisingly, patients with high
   GNG4 expression had a better response to immunotherapy ([126]Figure
   3A). Correspondingly, patients in the high-GNG4 group had better OS and
   PFS rates than those in the low-GNG4 group ([127]Figure 3B,C).
   Similarly, results from the IMvogo210 dataset consistently showed that
   higher GNG4 expression was linked to a better OS rate in patients
   undergoing immunotherapy with BLCA ([128]Figure 3D). Interestingly,
   GNG4 predicted the same prognosis for immunotherapy in a melanoma
   cohort ([129]Figure 3E,F). According to these results, GNG4 may be
   employed as a biomarker to predict the immunotherapy response in BLCA
   b, as well as in other cancer types.

Figure 3.

   [130]Figure 3
   [131]Open in a new tab

   GNG4 as a biomarker to predict the effect of immunotherapy. (A).
   Boxplots showing the proportion of responders between high-GNG4 and
   low-GNG4 groups in the [132]GSE176307 cohort. Overall survival (B) and
   progression-free survival (C) analysis between high-GNG4 and low-GNG4
   groups that received immunotherapy in the [133]GSE176307 cohort. (D).
   Overall survival analysis between high-GNG4 and low-GNG4 groups that
   received immunotherapy in the IMvigor210 cohort. (E). Boxplots showing
   the proportion of responders between high-GNG4 and low-GNG4 groups in
   the PRJEB23709 cohort (SKCM). (F). Overall survival analysis between
   high-GNG4 and low-GNG4 groups that received immunotherapy in the
   PRJEB23709 cohort (SKCM).

3.4. GNG4 Is an Indicator of Poor Prognosis and Can Predict the Effect of
Chemotherapy

   GNG4 is positively correlated with overall survival under
   immunotherapy, but, we found that higher expression of GNG4 was
   associated with worse overall survival (OS) and progression-free
   survival (PFS) rates in BLCA patients who did not receive immunotherapy
   ([134]Figure 4A,B). Additionally, three BLCA validation cohorts also
   confirmed that GNG4 expression was an indicator of poor survival in
   BLCA patients ([135]Figure 4C–E, [136]Table S3). Meanwhile, subgroup
   analysis of the pathological stage of tumors in the TCGA-BLCA cohort
   revealed that the levels of GNG4 expression increased gradually from
   stage I to stage IV ([137]Figure 4F). Similarly, an identical upward
   trend of GNG4 expression was also observed in the T-stage subgroup
   analysis of the TCGA-BLCA cohort ([138]Figure 4G). GNG4 expression was
   also significantly increased in patients with lymph node metastases and
   distal metastases ([139]Figure 4G). In particular, we found that GNG4
   was highly expressed in metastatic patients. This indicates that GNG4
   may also be related to the progress of BLCA. Molecular classifications
   of BLCA have been divided into six consensus classes (LumNS, LumP,
   LumU, Ba/Sq, stroma-rich, and neuroendocrine (NE)-like), which are the
   most commonly recognized molecular subtypes of BLCA [[140]33,[141]37]
   ([142]Table S3). Interestingly, we found that the high-level GNG4
   expression group was mostly transformed from the basal group
   (basal/squamous, Ba/Sq) and the stroma-rich group, whereas the
   low-level GNG4 expression group was mostly from the luminal group
   (luminal papillary, LumP) ([143]Figure 4H). To examine whether GNG4
   could predict the response to chemotherapy in BLCA, we then performed
   an analysis of the GNG4 gene expression levels in patients with
   different responses to chemotherapy based on the BLCA data from the
   TCGA database. The results showed that a better response to
   chemotherapy occurred in the low-level GNG4 expression group
   ([144]Figure 4I). Then, we also analyzed the differences in DNA repair
   gene expression between the high-level and low-level GNG4 expression
   groups in BLCA. The heatmap shows that the genes of DNA repair were
   highly expressed in the high-level GNG4 expression group ([145]Figure
   4J). Therefore, we concluded that the poor prognosis in the high-level
   GNG4 expression group might be due to DNA repair, which leads to
   chemotherapy resistance, whereas the low-level group had a better
   response to chemotherapy with a better prognosis.

Figure 4.

   [146]Figure 4
   [147]Open in a new tab

   GNG4 as a biomarker to predict survival and chemotherapy effectiveness.
   (A). Overall survival analysis of high and low expression levels of
   GNG4 in TCGA-BLCA. (B). Progression-free survival analysis between high
   and low expression levels of GNG4 in TCGA-BLCA. (C–E). Overall survival
   analysis comparing high and low expression levels of GNG4 in three
   external, dependent sets: [148]GSE13507, [149]GSE32894, and
   [150]GSE70691. (F). Based on the TCGA data, the expression levels of
   the GNG4 gene between the different groups of the main pathological
   stages (stage I, stage II, stage III, and stage IV) of BLCA. ***
   p-value ≤ 0.001. (G). Based on the TCGA data, the expression levels of
   the GNG4 gene between the different groups of the TNM pathological
   stages (stage T, stage N, and stage M) of BLCA. **, p-value ≤ 0.01; ***
   p-value ≤ 0.001. (H). Sankey chart displaying the path analysis in
   patients with BLCA between the published classification systems (LumP,
   LumU, stroma-rich, LumNS, Ba/Sq, and NE-like) of BLCA and expression
   levels of GNG4. The line indicates the group; the width of the lines
   signifies the number of patients who shifted from one state to another
   (n = 406). (I). Based on the BLCA data in the TCGA database, the
   expression levels of the GNG4 gene between the different groups of the
   response of patients to chemotherapy. *, p-value ≤ 0.05. (J).
   Differences in the gene expression of DNA repair between high-level and
   low-level GNG4 expression groups in BLCA.

4. Discussion

   In this study, we found that GNG4 was specifically expressed in a
   subset of CD4^+ T cells with exhausted characteristics. The expression
   of GNG4 is positively correlated with immune cell infiltrations and
   overall survival under immunotherapy. Interestingly, GNG4 expression is
   negatively correlated with overall survival and progression-free
   survival in bladder cancer patients who did not receive immunotherapy,
   which might be because GNG4 plays a role in developing chemo-resistance
   in bladder cancer.

   GNG4 is an important component of the immune microenvironment in CRC
   [[151]18]. In our study, we for the first time showed that GNG4 was
   specifically expressed in CD4^+ T cells using BLCA single-cell RNA
   sequencing data. Notably, GNG4 was highly expressed in a cluster of
   exhausted CD4^+ T cells, which highly expressed PDCD1, CXCL13, BTLA,
   and TIGIT. Correspondingly, we found that patients with high GNG4
   expression were indeed in an immune-inflamed tumor environment, not
   immune-excluded or an immune-desert tumor environment [[152]38].
   Meanwhile, the levels of effector immune-infiltrating cells, including
   NK cells, CD8^+ T cells, macrophages, dendritic cells, and TH1 cells,
   were also markedly increased in patients with high GNG4 expression.
   Similarly, the levels of immunosuppressive cells, such as neutrophils,
   TH2 cells, and Tregs, were also significantly increased. Exhausted
   cells are transformed from functional T-helper cells or effector cells.
   Additionally, the high infiltration of exhausted CD4^+ T cells often
   causes tumor cells to evade the attack of the immune system, making the
   overall immune microenvironment present a state of exhausted cells that
   are enriched but have lost toxic function. However, after
   immunotherapy, the exhausted state of cells becomes an effector state,
   which increases the inflammation of the overall microenvironment, which
   explains the positive association between GNG4 expression and better
   response to immune checkpoint blockade (ICB) immunotherapy, the
   first-line treatment for cisplatin-ineligible patients [[153]39]. An
   effective biomarker to predict ICB immunotherapy response is important
   for precise treatment. However, although CD8^+ T effector signatures,
   hypoxia-related genes, and PVR-related genes have been shown to predict
   BLCA immunotherapy [[154]40,[155]41,[156]42], these indicators often
   involve more genes and are difficult to measure. GNG4 as a single gene
   may have the advantage compared with these biomarkers. Additionally,
   GNG4 can also predict the immunotherapy of melanoma, which may be an
   indicator of Pan-cancer. Moreover, GNG4 could also be served as a
   potential biomarker to predict response to chemotherapy, a common and
   first-line treatment for BLCA [[157]43]. We found that patients with
   low expression of GNG4 had a better response to chemotherapy.

   Molecular classifications of BLCA have been studied extensively
   [[158]44,[159]45,[160]46]. The six consensus classes (LumNS, LumP,
   LumU, Ba/Sq, stroma-rich, and neuroendocrine (NE)-like) are the most
   commonly recognized molecular subtypes of BLCA, which can be mainly
   divided into basal and luminal subtypes. We found that patients with
   high expression of GNG4 are often considered as the basal subtypes with
   RB1 and TP53 mutations. Patients with low expression of GNG4 are often
   considered as the luminal subtypes with FGFR3 mutation. This indicates
   that GNG4 can be used as an important indicator of molecular subtypes,
   which has not been found in other similar studies [[161]33].

5. Conclusions

   GNG4 was specifically expressed in exhausted CD4^+ T cells. Higher GNG4
   expression indicates a state of immune inflammation and immune
   infiltration and can be used as a potential biomarker to predict the
   response of both immunotherapy and chemotherapy in BLCA.

Acknowledgments