Clear cell renal cell carcinoma (ccRCC) is a common tumor type in genitourinary system and has a poor prognosis. Ubiquitin dependent modification systems have been reported in a variety of malignancies and have influenced tumor genesis and progression. However, the molecular characteristics and prognostic value of ubiquitin in ccRCC have not been systematically reported. In our study, 204 differentially expressed ubiquitin related genes (URGs) were identified from The Cancer Genome Atlas (TCGA) cohort, including 141 up-regulated and 63 down-regulated URGs. A total of seven prognostic related URGs (CDCA3, CHFR, CORO6, RNF175, TRIM72, VAV3, and WDR72) were identified by Cox regression analysis of differential URGs and used to construct a prognostic signature. Kaplan-Meier analysis confirmed that high-risk patients had a worse prognosis (P = 1.11e-16), and the predicted area under the receiver operating characteristic (ROC) curves were 0.735 at 1 year, 0.702 at 3 years, and 0.744 at 5 years, showing good prediction accuracy. Stratified analysis showed that the URGs-based prognostic signature could be used to evaluate tumor progression in ccRCC. Further analysis confirmed that the signature is an independent prognostic factor related to the prognosis of ccRCC patients, which may help to reveal the molecular mechanism of ccRCC and provide potential diagnostic and prognostic markers for ccRCC.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide. The activation of the toll-like receptor 4/myeloid differentiation primary response gene 88/nuclear factor-κB (TLR4/MyD88/NF-κB) pathway contributes to the development and progression of HCC. The ubiquitin-proteasome system regulates TLR4 expression. However, whether ubiquitin specific peptidase 13 (USP13) stabilizes TLR4 and facilitates HCC progression remains unclear. Here, quantitative real-time PCR (qRT-PCR) and immunohistochemistry analysis revealed that USP13 expression in HCC tissues was higher than in non-tumor liver tissues. Moreover, the elevated expression of USP13 was detected in HCC cells (SK-HEP-1, HepG2, Huh7, and Hep3B) compared to LO2 cells. Interestingly, the positive staining of USP13 was closely correlated with tumor size ≥ 5 cm and advanced tumor stage and conferred to significantly lower survival of HCC patients. Next, USP13 knockdown prominently reduced the proliferation, epithelial-mesenchymal transition (EMT), migration, and invasion of Hep3B and Huh7 cells, while USP13 overexpression enhanced these biological behaviors of HepG2 and LO2 cells. The silencing of USP13 significantly restrained the growth and lung metastasis of HCC cells in vivo. Mechanistically, the USP13 depletion markedly inhibited the TLR4/MyD88/NF-κB pathway in HCC cells. USP13 interacted with TLR4 and inhibited the ubiquitin-mediated degradation of TLR4. Significantly, TLR4 re-expression remarkably reversed the effects of USP13 knockdown on HCC cells. USP13 expression was markedly upregulated in HCC cells under hypoxia conditions. Notably, USP13 knockdown repressed hypoxia-induced activation of the TLR4/MyD88/NF-κB pathway in HCC cells. In conclusion, our study uncovered that hypoxia-induced USP13 facilitated HCC progression via enhancing TLR4 deubiquitination and subsequently activating the TLR4/MyD88/NF-κB pathway.

Hypoxia microenvironment, a critical feature of hepatocellular carcinoma, contributes to hepatocarcinogenesis, tumor progression and therapeutic resistance. Hypoxia-inducible factors (HIFs)-activated target genes are the main effectors in hypoxia-induced HCC progression. In this study, we identified ubiquitin E3 ligase ring finger protein 146 (RNF146) as a novel HIFs target gene. Either HIF-1α or HIF-2α knockdown significantly repressed hypoxia-induced RNF146 upregulation in Hep3B and Huh7 cells. TCGA data and our immunohistochemistry analysis consistently revealed the overexpression of RNF146 in HCC tissues. The upregulated expression of RNF146 was also detected in HCC cell lines. The high RNF146 level was correlated with poor clinical features and predicted a shorter overall survival of patients with HCC. RNF146 knockdown suppressed the proliferation, colony formation and glycolysis of HCC cells, but suppressed but RNF146 overexpression promoted these malignant behaviors. Moreover, RNF146 silencing weakened HCC growth in mice. RNF146 inversely regulated phosphatase and tensin homolog (PTEN) protein level, thereby activating the AKT/mechanistic target of rapamycin kinase (mTOR) pathway in HCC cells. MG132 reversed RNF146 overexpression-induced PTEN reduction. RNF146 knockdown decreased the ubiquitination and degradation of PTEN in HCC cells. Therefore, we clarified that PTEN knockdown notably abolished the effects of RNF146 silencing on the AKT/mTOR pathway and Hep3B cells' proliferation, colony formation and glycolysis. To conclude, our data confirmed that RNF146 was transcriptionally regulated by HIF-1/2α and activated the AKT/mTOR pathway by promoting the ubiquitin proteolysis of PTEN, thereby contributing to HCC progression. RNF146 may be a potential new drug target for anti-HCC.

Invasion and metastasis of cervical cancer are the main factors affecting the prognosis of patients with cervical squamous cell carcinoma (CESC). Therefore, it is of vital importance to find novel biomarkers that are associated with CESC invasion and metastasis, which will aid in the amelioration of individualized therapeutic methods for advanced patients.

Increasing evidence has demonstrated that long non-coding RNAs (lncRNAs) play a crucial part in maintaining genomic instability. We therefore identified genome instability-related lncRNAs and constructed a prediction signature for early stage lung adenocarcinoma (LUAD) as well in order for classification of high-risk group of patients and improvement of individualized therapies.

Perioperative chemotherapy has been accepted as one of the most common approaches for locally advanced gastric cancer. However, the efficacy of chemotherapy varies among patients, and there is no effective method to predict the chemotherapy efficacy currently. We previously established the first larval zebrafish patient-derived xenografts (zPDXs) of gastric cancer as a platform for the translational research and personalized treatment. The objective of this study was to investigate the feasibility of screening individualized chemotherapeutics using the zPDXs.

Glioblastoma (GBM) is the most common primary malignant tumor of the central nervous system, with a 5-year overall survival (OS) rate of only 5.6%. This study aimed to develop a novel DNA methylation-driven gene (MDG)-based molecular classification and risk model for individualized prognosis prediction for GBM patients.

Background: Systemic metastasis is the main cause of death in patients with prostate cancer. It is necessary to establish a more accurate model to distinguish and predict patients with a high risk of metastasis to optimize individualized treatment. Methods: In this study, it was determined that hypoxia could affect the metastasis-free survival of patients with prostate cancer, and a hypoxia-related gene signature composed of seven genes for predicting metastasis was established and verified in different cohorts. The study further evaluated the effects of ALDOB expression on the proliferation and invasion of the LNCaP and DU145 cell lines under hypoxia and finally constructed a nomogram containing specific clinical characteristics of prostate cancer combined with the hypoxia gene signature to quantify the metastasis risk of individual patients. Results: The hypoxia-related gene signature was identified as an independent risk factor for metastasis-free survival in patients with prostate cancer. The expression of ALDOB increased under hypoxia and promoted the proliferation and invasion of LNCaP and DU145 cells. In addition, patients with a high risk score showed therapeutic resistance and immunosuppression. Compared with other parameters, the nomogram had the strongest predictive power and net clinical benefit. Conclusion: The study established a hypoxia-related gene signature and a nomogram to distinguish and predict patients with a high risk of prostate cancer metastasis, which may help to optimize individualized treatment and explore possible therapeutic targets.

The hereditary ataxias are a heterogenous group of disorders with an increasing number of causative genes being described. Due to the clinical and genetic heterogeneity seen in these conditions, the majority of such individuals endure a diagnostic odyssey or remain undiagnosed. Defining the molecular etiology can bring insights into the responsible molecular pathways and eventually the identification of therapeutic targets. Here, we describe the identification of biallelic variants in the GEMIN5 gene among seven unrelated families with nine affected individuals presenting with spastic ataxia and cerebellar atrophy. GEMIN5, an RNA-binding protein, has been shown to regulate transcription and translation machinery. GEMIN5 is a component of small nuclear ribonucleoprotein (snRNP) complexes and helps in the assembly of the spliceosome complexes. We found that biallelic GEMIN5 variants cause structural abnormalities in the encoded protein and reduce expression of snRNP complex proteins in patient cells compared with unaffected controls. Finally, knocking out endogenous Gemin5 in mice caused early embryonic lethality, suggesting that Gemin5 expression is crucial for normal development. Our work further expands on the phenotypic spectrum associated with GEMIN5-related disease and implicates the role of GEMIN5 among patients with spastic ataxia, cerebellar atrophy, and motor predominant developmental delay.

The combination of immune-checkpoint blockade (ICB) and lenvatinib has demonstrated robust clinical effects that are superior to those of monotherapies, but the synergistic anti-tumor mechanisms remain unclear. Exploring the synergistic molecular mechanisms and early identifying potential application have key importance for clinical therapeutics. We firstly systematically reviewed published data of ICB in combination with lenvatinib for the treatment of cancer by meta-analysis. A subsequent bioinformatics analysis explored the mechanism of combined ICB and lenvatinib therapy in 33 cancer types. Transcriptomic analysis was conducted by RNA-seq, and genomic analysis was performed on gene mutations and copy-number alteration data. Tumor-related pathways and tumor immune micro-environment (TIME) were also investigated. The meta-analysis showed a 38.0% objective response rate (ORR) and 79% disease control rate (DCR) for ICB combined with lenvatinib. Multi-omics analysis revealed that ICB and lenvatinib target genes were highly expressed and showed driving alterations in six specific malignancies. Pathway-enrichment analysis found target genes were implicated in tumor development, angiogenesis, and immunoregulatory associated pathways. This study verified the potential synergistic mechanisms of ICB combined with lenvatinib at transcriptomics, genomics, protein, and cellular levels and recognized nine tumor types had ≥ 2 positive treatment-related molecular characteristics, which might benefit particularly from this combined strategy. The findings would help to provide clinical insights and theoretical basis for optimizing of targeted therapy-immunotherapy combinations, and for guiding individualized precision-medicine approaches for cancer treatment.

Acute myeloid leukemia (AML) is one of the malignant hematologic cancers with rapid progress and poor prognosis. Most AML prognostic stratifications focused on genetic abnormalities. However, none of them was established based on the cell type compositions (CTCs) of peripheral blood or bone marrow aspirates from patients at diagnosis. Here we sought to develop a novel prognostic model for AML in adults based on the CTCs. First, we applied the CIBERSORT algorithm to estimate the CTCs for patients from two public datasets (GSE6891 and TCGA-LAML) using a custom gene expression signature reference constructed by an AML single-cell RNA sequencing dataset (GSE116256). Then, a CTC-based prognostic model was established using least absolute shrinkage and selection operator Cox regression, termed CTC score. The constructed prognostic model CTC score comprised 3 cell types, GMP-like, HSC-like, and T. Compared with the low-CTC-score group, the high-CTC-score group showed a 1.57-fold [95% confidence interval (CI), 1.23 to 2.00; p = 0.0002] and a 2.32-fold (95% CI, 1.53 to 3.51; p < 0.0001) higher overall mortality risk in the training set (GSE6891) and validation set (TCGA-LAML), respectively. When adjusting for age at diagnosis, cytogenetic risk, and karyotype, the CTC score remained statistically significant in both the training set [hazard ratio (HR) = 2.25; 95% CI, 1.20 to 4.24; p = 0.0119] and the validation set (HR = 7.97; 95% CI, 2.95 to 21.56; p < 0.0001]. We further compared the performance of the CTC score with two gene expression-based prognostic scores: the 17-gene leukemic stem cell score (LSC17 score) and the AML prognostic score (APS). It turned out that the CTC score achieved comparable performance at 1-, 2-, 3-, and 5-years timepoints and provided independent and additional prognostic information different from the LSC17 score and APS. In conclusion, the CTC score could serve as a powerful prognostic marker for AML and has great potential to assist clinicians to formulate individualized treatment plans.

Hepatocellular carcinoma (HCC) is a severe cancer endangering human health. We constructed a novel glycometabolism-related risk score to predict prognosis and immunotherapy strategies in HCC patients. The HCC data sets were obtained from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, and the glycometabolism-related gene sets were obtained from the Molecular Signature Database. The least absolute contraction and selection operator (LASSO) regression model was used to construct a risk score based on glycometabolism-related genes. A simple visual nomogram model with clinical indicators was constructed and its effectiveness in calibration, accuracy, and clinical value was evaluated. We also explored the correlation between glycometabolism-related risk scores and molecular pathways, immune cells, and functions. Patients in the low-risk group responded better to anti-CTLA-4 immune checkpoint treatment and benefited from immune checkpoint inhibitor (ICI) therapy. The study found that glycometabolism-related risk score can effectively distinguish the prognosis, molecular and immune-related characteristics of HCC patients, and may provide a new strategy for individualized treatment.

Background: Histone acetylation modification has been found to be correlated the development of renal carcinoma; however, its role in clear cell renal carcinoma (ccRCC) remains to be investigated. Thus, this study aimed to identify the molecular subtypes and establish a relevant score based on histone acetylation modification in ccRCC. Methods: Gene expression and mutation data were retrieved from The Cancer Genome Atlas database. Molecular subtypes were identified by unsupervised clustering based on histone acetylation regulators expression, and the molecular and clinical characteristics including survival, tumor microenvironment, gene set variation, immune cell infiltration, and immune checkpoints in each subtype were investigated. Next, we employed univariate Cox analysis to analyze these genes and established acetylation-related score by lasso regression analysis. Furthermore, we investigated the differences including survival, signaling pathways, mutational landscape, and tumor mutation burden (TMB) between high-risk and low-risk groups. The established score was validated by receiver operating curve and univariate and multivariate Cox regression analyses. We also established a nomogram including acetylation score, age, gender, grade, and stage and verified it by decision curve analysis and calibration plot. The E-MTAB-1980 cohort from the ArrayExpress database was employed as a reference to validate the established score. Results: Thirty-three types of histone acetylation regulators were employed in this study, and two clusters were identified. The two clusters presented significant differences in survival, tumor microenvironment, immune cell infiltration, immune checkpoints, and signaling pathways. Furthermore, an acetylation-related score, composed of six genes (BRD9, HDAC10, KAT2A, KAT5, BRDT, SIRT1, KAT6A, HDAC5), was verified to be significantly associated with prognosis and TMB. Thus, the established scores were successfully verified by the validated cohort, and the nomogram was constructed and successfully validated. Conclusion: The identification of the histone acetylation-related subtypes and score in our study may help reveal the potential relation between histone acetylation and immunity and provide novel insights for the development of individualized therapy for ccRCC.

Ovarian insufficiency is identified as a perplexing entity in the long list of pathologies impairing fertility dynamics. The three distinct classifications of ovarian insufficiency are poor ovarian response, premature ovarian insufficiency/failure, and advanced maternal age, sharing the common denominator of deteriorated ovarian reserve. Despite efforts to define clear lines among the three, the vast heterogeneity and overlap of clinical characteristics renders their diagnosis and management challenging. Lack of a consensus has prompted an empirically based management coupled by uncertainty from the clinicians' perspective. Profiling of patients in the era of precision medicine seems to be the way forward, while the necessity for a novel approach is underlined. Implicating miRNAs in the quest for patient profiling is promising in light of their fundamental role in cellular and gene expression regulation. To this end, the current study sets out to explore and compare the three pathophysiologies-from a molecular point of view-in order to enable profiling of patients in the context of in vitro fertilization treatment and enrich the data required to practice individualized medicine. Following a systematic investigation of literature, data referring to miRNAs were collected for each patient category based on five included studies. miRNA-target pairs were retrieved from the DIANA-TarBase repository and microT-CDS. Gene and miRNA annotations were derived from Ensembl and miRbase. A subsequent gene-set enrichment analysis of miRNA targets was performed for each category separately. A literature review on the most crucial of the detected pathways was performed to reveal their relevance to fertility deterioration. Results supported that all three pathophysiologies share a common ground regarding the affected pathways, naturally attributed to the common denominator of ovarian insufficiency. As evidenced, miRNAs could be employed to explore the fine lines and diverse nature of pathophysiology since they constitute invaluable biomarkers. Interestingly, it is the differentiation through miRNAs and not through the molecular affected pathways that corresponds to the three distinctive categories. Alarming discrepancies among publications were revealed, pertaining to employment of empirical and arbitrary criteria in categorizing the patients. Following bioinformatic analysis, the final step of the current study consisted of a critical analysis of the molecular data sourced, providing a clear and unique insight into the physiological mechanisms involved. It is our intention to contribute to mapping future research dedicated to ovarian insufficiency and to help researchers navigate the overwhelming information published in molecular studies.

Background: The incidence of oropharyngeal squamous cell carcinoma (OPSCC) is rapidly increasing in high income countries due to its association with persistent high-risk human papilloma virus (HPV) infection. Recent scientific advances have highlighted the importance of the tumor microenvironment in OPSCC. In this study, including 216 OPSCC patients, we analyze the composition of four established markers of cancer associated fibroblasts (CAFs) in the context of intratumoral CD8 T-cell infiltration. Methods: Immunohistochemical staining for fibroblast activation protein (FAP), platelet-derived growth factor receptor beta (PDGFRb), periostin, alpha smooth muscle actin (α-SMA) and CD8 were analyzed digitally and their association with survival, tumor- and patient characteristics was assessed. Results: Co-expression of CAF markers was frequent but not associated with HPV status. FAPhigh and PDGFRbhigh expression were associated with increased CD8 T-cell infiltration. Low expression of PDGFRb improved patient survival in female patients but not in male patients. We identified PDGFRblow periostinlow α-SMAlow status as an independent predictor of improved survival (hazard ratio 0.377, p = 0.006). Conclusion: These findings elucidate the co-expression of four established CAF markers in OPSCC and underscore their association with T-cell infiltration and patient survival. Future analyses of CAF subgroups in OPSCC may enable the development of individualized therapies.

Primary open-angle glaucoma (POAG) is a progressive optic neuropathy and its damage to vision is irreversible. Therefore, early diagnosis assisted by biomarkers is essential. Although there were multiple researches on the identification of POAG biomarkers, few studies systematically revealed the transcriptome dysregulation mechanism of POAG from the perspective of pre- and post-transcription of genes. Here, we have collected multiple sets of POAG's aqueous humor (AH) tissue transcription profiles covering long non-coding RNA (lncRNA), mRNA and mircoRNA (miRNA). Through differential expression analysis, we identified thousands of significant differentially expressed genes (DEGs) between the AH tissue of POAG and non-glaucoma. Further, the DEGs were used to construct a competing endogenous RNA (ceRNA) regulatory network and 1,653 qualified lncRNA-miRNA-mRNA regulatory units were identified. Two ceRNA regulatory subnets were identified based on the random walk algorithm and revealed to be involved in the regulation of multiple complex diseases. At the pre-transcriptional regulation level, a transcriptional regulatory network was constructed and three transcription factors (FOS, ATF4, and RELB) were identified to regulate the expression of multiple genes and participate in the regulation of T cells. Moreover, we revealed the immune desert status of AH tissue for POAG patients based on immune infiltration analysis and identified a specific AL590666.2-hsa-miR-339-5p-UROD axis can be used as a biomarker of POAG. Taken together, the identification of regulatory mechanisms and biomarkers will contribute to the individualized diagnosis and treatment for POAG.

The identification of reliable indicators in the tumor microenvironment (TME) is critical for tumor prognosis. Tumor associated macrophages (TAMs) are the major component of non-tumor stromal cells in TME and have increasingly been recognized as a predictive biomarker for lung adenocarcinoma (LUAD) prognosis. Here, we report the development of a prognosis model for LUAD using three immune-related genes (IRGs) detected in The Cancer Genome Atlas (TCGA) which potentially regulate TAMs in TME. In 497 LUAD patients, higher immune scores conferred better overall survival (OS). We identified 93 hub IRGs out of 234 for further prognostic significance. Among them, three IRGs (BTK, Cd1c, and S100P) were proved to be closely correlated to the prognosis of patients with LUAD. Moreover, the immune risk score (IRS) based on the gene expression level of the three IRGs was an independent prognostic factor for OS. Higher IRS predicted lower OS, higher mortality and worse tumor stage. With a good predictive ability [area under the ROC curve (AUC) in TCGA = 0.701, AUC in GEO = 0.722], the IRS contributed to a good risk stratification ability of the nomogram. Immunologically, the three IRGs were related to M1 macrophages and NK cell subsets in TME. Interestingly, by characterizing these immune components in situ we found that S100P is a driver for tumor cells to induce TAM migration and M2 polarization in the immunosuppressive tumor niche. We identified the key genes driving TAM migration and transformation and elucidated the immune landscape of LUAD. The data suggest that IRGs from TME have the potential to become indicators for estimating cancer prognosis and guiding individualized treatment.

Background: Hepatocellular carcinoma (HCC) is the world's second most deadly cancer, and metabolic reprogramming is its distinguishing feature. Among metabolite profiling, variation in amino acid metabolism supports tumor proliferation and metastasis to the most extent, yet a systematic study on the role of amino acid metabolism-related genes in HCC is still lacking. An effective amino acid metabolism-related prediction signature is urgently needed to assess the prognosis of HCC patients for individualized treatment. Materials and Methods: RNA-seq data of HCC from the TCGA-LIHC and GSE14520 (GPL3921) datasets were defined as the training set and validation set, respectively. Amino acid metabolic genes were extracted from the Molecular Signature Database. Univariate Cox and LASSO regression analyses were performed to build a predictive risk signature. K-M curves, ROC curves, and univariate and multivariate Cox regression were conducted to evaluate the predictive value of this risk signature. Functional enrichment was analyzed by GSEA and CIBERSORTx software. Results: A nine-gene amino acid metabolism-related risk signature including B3GAT3, B4GALT2, CYB5R3, GNPDA1, GOT2, HEXB, HMGCS2, PLOD2, and SEPHS1 was constructed to predict the overall survival (OS) of HCC patients. Patients were separated into high-risk and low-risk groups based on risk scores and low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for HCC. ROC curves showed that this risk signature can effectively predict the 1-, 2-, 3- and 5-year survival times of patients with HCC. Additionally, prognostic nomograms were established based on the training set and validation set. These genes were closely correlated with the immune regulation. Conclusion: Our study identified a nine-gene amino acid metabolism-related risk signature and built predictive nomograms for OS in HCC. These findings will help us to personalize the treatment of liver cancer patients.

Breast cancer (BC) is the most common cancer affecting women and the leading cause of cancer-related deaths worldwide. Compelling evidence indicates that microRNAs (miRNAs) are inextricably involved in the development of cancer. Here, we constructed a novel model, based on miRNA-seq and clinical data downloaded from The Cancer Genome Atlas (TCGA). Data from a total of 962 patients were included in this study, and the relationships among their clinicopathological features, survival, and miRNA-seq expression levels were analyzed. Hsa-miR-186 and hsa-miR-361 were identified as internal reference miRNAs and used to normalize miRNA expression data. A five-miRNA signature, constructed using univariate and multivariate Cox regression, was significantly associated with disease-specific survival (DSS) of patients with BC. Kaplan-Meier (KM) and receiver operating characteristic (ROC) analyses were conducted to confirm the clinical significance of the five-miRNA signature. Finally, a nomogram was constructed based on the five-miRNA signature to evaluate its clinical value. Cox regression analysis revealed that a five-miRNA signature was significantly associated with DSS of patients with BC. KM analysis demonstrated that the signature could efficiently distinguish high- and low-risk patients. Moreover, ROC analysis showed that the five-miRNA signature exhibited high sensitivity and specificity in predicting the prognosis of patients with BC. Patients in the high-risk subgroup who received adjuvant chemotherapy had a significantly lower incidence of mortality than those who did not. A nomogram constructed based on the five-miRNA signature was effective in predicting 5-year DSS. This study presents a novel five-miRNA signature as a reliable prognostic tool to predict DSS and provide theoretical reference significance for individualized clinical decisions for patients with BC.

Immune checkpoint inhibitor (ICI) treatment has been used to treat advanced urothelial cancer. Molecular markers might improve risk stratification and prediction of ICI benefit for urothelial cancer patients. We analyzed 406 cases of bladder urothelial cancer from The Cancer Genome Atlas (TCGA) data set and identified 161 messenger RNAs (mRNAs) as differentially expressed immunity genes (DEIGs). Using the LASSO Cox regression model, an eight-mRNA-based risk signature was built. We validated the prognostic and predictive accuracy of this immune-related risk signature in 348 metastatic urothelial cancer (mUC) samples treated with anti-PD-L1 (atezolizumab) from IMvigor210. We built an immune-related risk signature based on the eight mRNAs: ANXA1, IL22, IL9R, KLRK1, LRP1, NRG3, SEMA6D, and STAP2. The eight-mRNA-based risk signature successfully categorizes patients into high-risk and low-risk groups. Overall survival was significantly different between these groups, regardless if the initial TCGA training set, the internal TCGA testing set, all TCGA set, or the ICI treatment set. The hazard ratio (HR) of the high-risk group to the low-risk group was 3.65 (p < 0.0001), 2.56 (p < 0.0001), 3.36 (p < 0.0001), and 2.42 (p = 0.0009). The risk signature was an independent prognostic factor for prediction survival. Moreover, the risk signature was related to immunity characteristics. In different tumor mutational burden (TMB) subgroups, it successfully categorizes patients into high-risk and low-risk groups, with significant differences of clinical outcome. Our eight-mRNA-based risk signature is a stable biomarker for urothelial cancer and might be able to predict which patients benefit from ICI treatment. It might play a role in precision individualized immunotherapy.