Identification of Immune-Related lncRNA Signature to Predict Prognosis and Immunotherapeutic Efficiency in Bladder Cancer

PurposeIdentify immune-related lncRNA (IRL) signature related to the prognosis and immunotherapeutic efficiency for bladder cancer (BLCA) patients.MethodsA total of 397 samples, which contained RNA-seq and clinical information from The Cancer Genome Atlas (TCGA) database, were used for the following study. Then the Lasso penalized Cox proportional hazards regression model was used to construct prognostic signature. According to the optimal cut-off value determined by time-dependent ROC curve, low and high-risk groups were set up. One immunotherapy microarray dataset as validation set was used to verify the ability of predicting immunotherapy efficacy. Furthermore, more evaluation between two risk groups related clinical factors were conducted. Finally, external validation of IRL-signature was conducted in Zhengzhou cohort.ResultFour IRLs (HCP5, IPO5P1, LINC00942, and LINC01356) with significant prognostic value (P<0.05) were distinguished. This signature can accurately predict the overall survival of BLCA patients and was verified in the immunotherapy validation set. IRL-signatures can be used as independent prognostic risk factor in various clinical subgroups. According to the results of GSVA and MCP algorithm, we found that IRL-signature risk score is strikingly negative correlated with tumor microenvironment (TME) CD8+T cells and Cytotoxic lymphocytes infiltration, indicating that the better prognosis and immunotherapy might be caused partly by these. Then, the results from the TIDE analysis revealed that IRL could efficiently predict the response of immunotherapy in BLCA. External validation had similar results with TCGA-BLCA cohort.ConclusionsThe novel IRL-signature has a significant prognostic value for BLCA patients might facilitate predicting the efficacy of immunotherapy.

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Identification of an IRGP Signature to Predict Prognosis and Immunotherapeutic Efficiency in Bladder Cancer

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.607090 ◽

2021 ◽

Vol 8 ◽

Author(s):

Liang-Hao Zhang ◽

Long-Qing Li ◽

Yong-Hao Zhan ◽

Zhao-Wei Zhu ◽

Xue-Pei Zhang

Keyword(s):

Bladder Cancer ◽

Stromal Cells ◽

Molecular Mechanisms ◽

Proportional Hazards ◽

Enrichment Analysis ◽

Cox Proportional Hazards ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Validation Set

BackgroundIdentify immune-related gene pairs (IRGPs) signature related to the prognosis and immunotherapeutic efficiency for bladder cancer (BLCA) patients.Materials and MethodsOne RNA-seq dataset (The Cancer Genome Atlas Program) and two microarray datasets (GSE13507 and GSE31684) were included in this study. We defined these cohorts as training set to construct IRGPs and one immunotherapy microarray dataset as validation set. Identifying BLCA subclasses based on IRGPs by consensus clustering. The Lasso penalized Cox proportional hazards regression model was used to construct prognostic signature and potential molecular mechanisms were analyzed.ResultsThis signature can accurately predict the overall survival of BLCA patients and was verified in the immunotherapy validation set. IRGP-signatures can be used as independent prognostic risk factor in various clinical subgroups. Use the CIBERSORT algorithm to assess the abundance of infiltrating immune cells in each sample, and combine the results of the gene set enrichment analysis of a single sample to explore the differences in the immune microenvironment between IRPG signature groups. According to the results of GSVA, GSEA, and CIBERSORT algorithm, we found that IRGP is strikingly positive correlated with tumor microenvironment (TME) stromal cells infiltration, indicating that the poor prognosis and immunotherapy might be caused partly by enrichment of stromal cells. Finally, the results from the TIDE analysis revealed that IRGP could efficiently predict the response of immunotherapy in BLCA.ConclusionThe novel IRGP signature has a significant prognostic value for BLCA patients might facilitate personalized for immunotherapy.

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Use of single polymorphism arrays (SNP-A) to modify prognostic scoring systems for myelodysplastic syndromes.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.6521 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. 6521-6521

Author(s):

Manojkumar Bupathi ◽

Paul Elson ◽

Visconte Valeria ◽

Fabiola Traina ◽

Christine O' Keefe ◽

...

Keyword(s):

Prognostic Value ◽

Proportional Hazards ◽

Uniparental Disomy ◽

Scoring Systems ◽

Risk Groups ◽

Cox Proportional Hazards ◽

Clinical Factors ◽

Cox Proportional Hazards Models ◽

Revised Definitions ◽

Endpoint Data

6521 Background: MDS are hematologic neoplasms characterized by dysplasia and cytopenias. Two commonly used risk stratifications, IPSS (Greenberg et al Blood 1997) and IPSS-R are based on clinical factors (marrow blasts, number of cytopenias and genetic abnormalities determined by conventional karyotyping), with the IPSS based on 4 cytogenetic risk groups and the IPSS-R based on 5 cytogenetic risk groups defined by Schanz et al JCO 2012. A drawback to metaphase cytogenetics (MC) is its inability to detect small cryptic chromosomal defects and uniparental disomy (UPD). SNP-A can identify these small cytogenetic lesions/UPD and has been shown to be of prognostic value in MDS. The goal of this investigation was to determine if SNP-A detected defects could be used to refine the IPSS and IPSS-R. Methods: SNP-A karyotyping was performed as previously described (Tiu et al, Blood, 2011). We reviewed data from 327 patients idenfitying SNP-A results, MC, and the IPSS/IPSS-R-related clinical factors were identified. Overall survival (OS) measured from the date of sampling for SNP-A karyotyping was the primary endpoint. Data were analyzed using Cox proportional hazards models. Results: SNP-A lesions not detected by MC were grouped as 1) none or only gains/losses; 2) UPD only or gains+losses but no UPD; and 3) UPD+other defects. The frequencies/median os in months (mo) of the groups were: 66%/20.0, 23%/12.7 and 11%/5.8, respectively, p<.0001 for OS. Combining the SNP-A groups with MC resulted in revised definitions of cytogenetic risk that had better discrimination than the underlying models; and when combined with the relevant clinical factors resulted in refined IPSS (IPSSSNP-A) and IPSS-R (IPSS-RSNP-A) risk stratifications (Table). Conclusions: Detection of chromosomal gains, losses, and UPD by SNP-A has prognostic value in MDS and can be used to refine current risk stratifications. [Table: see text]

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A novel survival model based on a Ferroptosis-related gene signature for predicting overall survival in bladder cancer

BMC Cancer ◽

10.1186/s12885-021-08687-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yingchun Liang ◽

Fangdie Ye ◽

Chenyang Xu ◽

Lujia Zou ◽

Yun Hu ◽

...

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognosis Prediction

Abstract Background The effective treatment and prognosis prediction of bladder cancer (BLCA) remains a medical problem. Ferroptosis is an iron-dependent form of programmed cell death. Ferroptosis is closely related to tumour occurrence and progression, but the prognostic value of ferroptosis-related genes (FRGs) in BLCA remains to be further clarified. In this study, we identified an FRG signature with potential prognostic value for patients with BLCA. Methods The corresponding clinical data and mRNA expression profiles of BLCA patients were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to extract FRGs related to survival time, and a Cox regression model was used to construct a multigene signature. Both principal component analysis (PCA) and single-sample gene set enrichment analysis (ssGSEA) were performed for functional annotation. Results Clinical traits were combined with FRGs, and 15 prognosis-related FRGs were identified by Cox regression. High expression of CISD1, GCLM, CRYAB, SLC7A11, TFRC, ACACA, ZEB1, SQLE, FADS2, ABCC1, G6PD and PGD was related to poor survival in BLCA patients. Multivariate Cox regression was used to construct a prognostic model with 7 FRGs that divided patients into two risk groups. Compared with that in the low-risk group, the overall survival (OS) of patients in the high-risk group was significantly lower (P < 0.001). In multivariate regression analysis, the risk score was shown to be an independent predictor of OS (HR = 1.772, P < 0.01). Receiver operating characteristic (ROC) curve analysis verified the predictive ability of the model. In addition, the two risk groups displayed different immune statuses in ssGSEA and different distributed patterns in PCA. Conclusion Our research suggests that a new gene model related to ferroptosis can be applied for the prognosis prediction of BLCA. Targeting FRGs may be a treatment option for BLCA.

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A Novel Survival Model Based on Ferroptosis-related Gene Signature for Overall Survival Prediction in Bladder Cancer

10.21203/rs.3.rs-136951/v1 ◽

2021 ◽

Author(s):

Yingchun Liang ◽

Fangdie Ye ◽

Chenyang Xu ◽

Lujia Zou ◽

Yun Hu ◽

...

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

Prognosis Prediction

Abstract Background: The effective treatment and prognosis prediction of bladder cancer(BLCA) remains a medical problem. Ferroptosis is an iron-dependent form of programmed cell death. Ferroptosis are closely related to tumor occurrence and progression, but the prognostic value of ferroptosis-related genes (FRGs) in BLCA remains to be further clarified. In this study, we identified a FRGs signature with potential prognostic value for patients with BLCA. Methods: The corresponding clinical data and the mRNA expression profile of BLCA patients were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to extract FRGs related to survival time, Cox regression model was applied to construct a multigene signature. Both principal component analysis (PCA) and single-sample gene set enrichment analysis (ssGSEA) were performed for functional annotation. Results: Clinical traits were combined with FRGs, so that 15 prognostic-related FRGs were identified by Cox regression. High expression of CISD1, GCLM, CRYAB, SLC7A11, TFRC, ACACA, ZEB1, SQLE, FADS2, ABCC1, G6PD and PGD are related to poor survival rates of BLCA patients. Multivariate Cox regression constructed a prognostic model with 7 FRGs and divided patients into two risk groups. Compared with the low-risk group, the overall survival(OS) of patients in the high-risk group was significantly lower (P <0.001). In multivariate regression analysis, the risk score was shown to be an independent predictor of OS (HR> 1, P <0.01). ROC curve analysis verified the predictive ability of the model. In addition, the two risk groups displayed different immune statuses in the ssGSEA and different distributed patterns in PCA. Conclusion: Our research suggests that a new gene model related to ferroptosis can be applied for the prognosis prediction of BLCA. Targeting FRGs may be a treatment option for BLCA.

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A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

10.21203/rs.3.rs-28390/v1 ◽

2020 ◽

Author(s):

Yinglian Pan ◽

LiPing Jia ◽

Yuzhu Liu ◽

Yiyu Han ◽

Qian Li ◽

...

Keyword(s):

Ovarian Cancer ◽

High Risk ◽

Proportional Hazards ◽

Cox Regression ◽

Risk Groups ◽

Cox Proportional Hazards ◽

Low Risk ◽

Patient Choice ◽

The Cancer Genome Atlas ◽

Chemotherapy Treatment

Abstract Background: Ovarian cancer (OV) is the most common type of primary female reproductive cancer. BRCA1/2 gene is an important biomarker for evaluating the risk of OV, breast cancer and other related tumors and influences patient choice of individualized treatment. A powerful signature to predict OV prognosis and improve treatment personalization is urgently needed. This study aimed to identify a novel OV-related lncRNA prognostic biomarker.Methods: A Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from The Cancer Genome Atlas (TCGA) database. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was assessed, and the sensitivity and specificity of the prediction model were determined.Results: The signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as a criterion for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The 3-year overall survival (OS) rates for the high- and low-risk groups were approximately 38% and 100%, respectively. Chemotherapy treatment survival rates indicated that high-risk groups had significantly shorter OS rates with adjuvant chemotherapy than the low-risk groups. The OS of 1-, 3- and 5- years were 100%, 40%, and 15% in the high-risk groups respectively. The survival rate of the high-risk group declined rapidly after two years of OA chemotherapy treatment. In addition, multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development.Conclusion: In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with in BRCA1/2 mutations to predict prognosis and chemotherapy efficiency.

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Clinical Significance and Immunologic Landscape of a Five-IL(R)-Based Signature in Lung Adenocarcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.693062 ◽

2021 ◽

Vol 12 ◽

Author(s):

Tao Fan ◽

Shize Pan ◽

Shuo Yang ◽

Bo Hao ◽

Lin Zhang ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Prognostic Value ◽

Proportional Hazards ◽

Immune Cell ◽

Gene Expression Omnibus ◽

Cox Proportional Hazards ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Interleukin Receptors ◽

Public Datasets

Interleukins (ILs) and interleukin receptors (ILRs) play important role in the antitumor immune response. However, the expression signature and clinical characteristics of the IL(R) family in lung adenocarcinoma (LUAD) remains unclear. The main purpose of this study was to explore the expression profile of IL(R) family genes and construct an IL(R)-based prognostic signature in LUAD. Five public datasets of 1,312 patients with LUAD were enrolled in this study. Samples from The Cancer Genome Atlas (TCGA) were used as the training set, and samples from the other four cohorts extracted from Gene Expression Omnibus (GEO) database were used as the validation set. Additionally, the profile of IL(R) family signature was explored, and the association between this signature and immunotherapy response was also analyzed. Meanwhile, the prognostic value was compared between this IL(R)-based signature and different immunotherapy markers. A signature based on five identified IL(R)s (IL7R, IL5RA, IL20RB, IL11, IL22RA1) was constructed using the TCGA dataset through univariate/multivariable Cox proportional hazards regression and least absolute shrinkage and selection operator (LASSO) Cox analysis. These cases with LUAD were stratified into high- and low-risk group according to the risk score. This signature showed a strong prognostic ability, which was verified by the five independent cohorts and clinical subtypes. The IL(R)-based models presented unique characteristics in terms of immune cell infiltration and immune inflammation profile in tumor microenvironment (TME). Biological pathway analysis confirmed that high-risk patients showed significant T- and B-cell immunosuppression and rapid tumor cell proliferation. More importantly, we researched the relationship between this IL(R)-based signature and immune checkpoints, tumor mutation burden (TMB), tumor purity and ploidy, and tumor immune dysfunction and exclusion (TIDE) score, which confirmed that this signature gave the best prognostic value. We first provided a robust prognostic IL(R)-based signature, which had the potential as a predictor for immunotherapy response to realize individualized treatment of LUAD.

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Prognostic value of MRI-measured tumor thickness in patients with tongue squamous cell carcinoma

Scientific Reports ◽

10.1038/s41598-021-90655-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ki-Sun Park ◽

Yangsean Choi ◽

Jiwoong Kim ◽

Kook-Jin Ahn ◽

Bum-soo Kim ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Prognostic Value ◽

Proportional Hazards ◽

Correlation Coefficients ◽

Cox Proportional Hazards ◽

Tumor Thickness ◽

Tongue Squamous Cell Carcinoma ◽

Depth Of Invasion

AbstractThis study aimed to assess the prognostic value of MRI-measured tumor thickness (MRI-TT) in patients with tongue squamous cell carcinoma (SCC). This single-center retrospective cohort study included 133 pathologically confirmed tongue SCC patients between January 2009 and October 2019. MRI measurements of tongue SCC were based on axial and coronal T2-weighted (T2WI) and contrast-enhanced T1-weighted (CE-T1WI) images. Two radiologists independently measured MRI-TT. Intraclass correlation coefficients (ICC) were calculated for inter-rater agreements. Spearman’s rank correlation between MRI-TT and pathologic depth of invasion (pDOI) was assessed. Cox proportional hazards analyses on recurrence-free (RFS) and overall survival (OS) were performed for MRI-TT and pDOI. Kaplan–Meier survival curves were plotted with log-rank tests. The intra- and inter-rater agreements of MRI-TT were excellent (ICC: 0.829–0.897, all P < 0.001). The correlation between MRI-TT and pDOI was good (Spearman’s correlation coefficients: 0.72–0.76, P < 0.001). MRI-TT were significantly greater than pDOI in all axial and coronal T2WI and CE-T1WI (P < 0.001). In multivariate Cox proportional hazards analysis, MRI-TT measured on axial CE-T1WI yielded a significant prognostic value for OS (hazards ratio 2.77; P = 0.034). MRI-TT demonstrated excellent intra- and inter-rater agreements as well as high correlation with pDOI. MRI-TT may serve as a prognostic predictor in patients with tongue SCC.

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Eps15 Homology Domain-Containing Protein 3 Hypermethylation as a Prognostic and Predictive Marker for Colorectal Cancer

Biomedicines ◽

10.3390/biomedicines9050453 ◽

2021 ◽

Vol 9 (5) ◽

pp. 453

Author(s):

Yu-Han Wang ◽

Shih-Ching Chang ◽

Muhamad Ansar ◽

Chin-Sheng Hung ◽

Ruo-Kai Lin

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

Mrna Expression ◽

Proportional Hazards ◽

Uterine Cancer ◽

Colonic Polyps ◽

Predictive Marker ◽

Cox Proportional Hazards ◽

The Cancer Genome Atlas ◽

Eps15 Homology Domain

Colorectal cancer (CRC) arises from chromosomal instability, resulting from aberrant hypermethylation in tumor suppressor genes. This study identified hypermethylated genes in CRC and investigated how they affect clinical outcomes. Methylation levels of specific genes were analyzed from The Cancer Genome Atlas dataset and 20 breast cancer, 16 esophageal cancer, 33 lung cancer, 15 uterine cancer, 504 CRC, and 9 colon polyp tissues and 102 CRC plasma samples from a Taiwanese cohort. In the Asian cohort, Eps15 homology domain-containing protein 3 (EHD3) had twofold higher methylation in 44.4% of patients with colonic polyps, 37.3% of plasma from CRC patients, and 72.6% of CRC tissues, which was connected to vascular invasion and high microsatellite instability. Furthermore, EHD3 hypermethylation was detected in other gastrointestinal cancers. In the Asian CRC cohort, low EHD3 mRNA expression was found in 45.1% of patients and was connected to lymph node metastasis. Multivariate Cox proportional-hazards survival analysis revealed that hypermethylation in women and low mRNA expression were associated with overall survival. In the Western CRC cohort, EHD3 hypermethylation was also connected to overall survival and lower chemotherapy and antimetabolite response rates. In conclusion, EHD3 hypermethylation contributes to the development of CRC in both Asian and Western populations.

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Development and Validation of a Prognostic Nomogram Model for Recurrence of Non-Muscle Invasive Bladder Cancer

10.21203/rs.3.rs-691739/v1 ◽

2021 ◽

Author(s):

Sanhe Liu ◽

Yongzhi Li ◽

Diansheng Cui ◽

Yuexia Jiao ◽

Liqun Duan ◽

...

Keyword(s):

Bladder Cancer ◽

Risk Stratification ◽

Proportional Hazards ◽

Cox Proportional Hazards ◽

Muscle Invasive Bladder Cancer ◽

Clinicopathologic Characteristics ◽

Cox Proportional Hazards Regression ◽

Proportional Hazards Regression ◽

Muscle Invasive

Abstract BackgroundDifferent recurrence probability of non-muscle invasive bladder cancer (NMIBC) requests different adjuvant treatments and follow-up strategies. However, there is no simple, intuitive, and generally accepted clinical recurrence predictive model available for NMIBC. This study aims to construct a predictive model for the recurrence of NMIBC based on demographics and clinicopathologic characteristics from two independent centers. MethodsDemographics and clinicopathologic characteristics of 511 patients with NMIBC were retrospectively collected. Recurrence free survival (RFS) was estimated using the Kaplan-Meier method and log-rank tests. Univariate Cox proportional hazards regression analysis was used to screen variables associated with RFS, and a multivariate Cox proportional hazards regression model with a stepwise procedure was used to identify those factors of significance. A final nomogram model was built using the multivariable Cox method. The performance of the nomogram model was evaluated with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed with bootstrap resampling. X-tile software was used for risk stratification calculated by the nomogram model. ResultsIndependent prognostic factors including tumor stage, recurrence status, and European Association of Urology (EAU) risk stratification group were introduced to the nomogram model. The model showed acceptable calibration and discrimination (area under the receiver operating characteristic [ROC] curve was 0.85; the consistency index [C-index] was 0.79 [95% CI: 0.76 to 0.82]), which was superior to the EAU risk stratification group alone. The decision curve also proved well clinical usefulness. Moreover, all populations could be stratified into three distinct risk groups by the nomogram model. ConclusionsWe established and validated a novel nomogram model that can provide individual prediction of RFS for patients with NMIBC. This intuitively prognostic nomogram model may help clinicians in postoperative treatment and follow-up decision-making.

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Prognostic Effect of Preoperative Psoas Muscle Hounsfield Unit at Radical Cystectomy for Bladder Cancer

Cancers ◽

10.3390/cancers13225629 ◽

2021 ◽

Vol 13 (22) ◽

pp. 5629

Author(s):

Yusuke Sugino ◽

Takeshi Sasaki ◽

Manabu Kato ◽

Satoru Masui ◽

Kouhei Nishikawa ◽

...

Keyword(s):

Bladder Cancer ◽

Radical Cystectomy ◽

Proportional Hazards ◽

Proportional Hazards Model ◽

Univariate Analysis ◽

Psoas Muscle ◽

Hounsfield Unit ◽

Cox Proportional Hazards ◽

Cox Proportional Hazards Model ◽

Cross Sectional

Radical cystectomy (RC) is the standard treatment for patients with advanced bladder cancer. Since RC is a highly invasive procedure, the surgical indications in an aging society must be carefully judged. In recent years, the concept of “frailty” has been attracting attention as a term used to describe fragility due to aging. We focused on the psoas muscle Hounsfield unit (PMHU) and analyzed its appropriateness as a prognostic factor together with other clinical factors in patients after RC. We retrospectively analyzed the preoperative prognostic factors in 177 patients with bladder cancer who underwent RC between 2008 and 2020. Preoperative non-contrast computed tomography axial image at the third lumbar vertebral level was used to measure the mean Hounsfield unit (HU) and cross-sectional area (mm2) of the psoas muscle. Univariate analysis showed significant differences in age, sex, clinical T stage, and PMHU. In multivariate analysis using the Cox proportional hazards model, age (hazard ratio (HR) = 1.734), sex (HR = 2.116), cT stage (HR = 1.665), and PMHU (HR = 1.758) were significant predictors for overall survival. Furthermore, using these four predictors, it was possible to stratify the prognosis of patients after RC. Finally, PMHU was useful as a simple and significant preoperative factor that correlated with prognosis after RC.

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