A novel focal adhesion-related risk model predicts prognosis of bladder cancer —— a bioinformatic study based on TCGA and GEO database

Hu, Jiyuan; Wang, Linhui; Li, Luanfeng; Wang, Yutao; Bi, Jianbin

doi:10.1186/s12885-022-10264-5

Cited by 5 publications

(6 citation statements)

References 54 publications

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“…Although newer methods like fluid biopsy and genetic testing seem to increase the accuracy of BC prognosis predictions, they have drawbacks including a hefty price tag and labor-intensive analysis [ 26 – 28 ]. The majority of them are also still through clinical studies.…”

Section: Discussionmentioning

confidence: 99%

Nomogram for customized recurrence prediction in primary non-muscle-invasive bladder cancer based on routine blood and urine parameters

He,

Pan,

Zhang

et al. 2024

BMC Urol

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Purpose A prevalent condition with a high probability of recurrence, non-muscle invasive bladder cancer (NMIBC) necessitates lifetime surveillance. In patients with pathologically confirmed NMIBC, our goal was to create a unique nomogram to predict recurrence after transurethral resection of bladder tumor (TURBT). Methods Our institution’s 91 NMIBC patients with complete follow-up data between January 2017 and February 2021 were included in the retrospective analysis. The nomogram predicting the 0.5, 1, 2 and 3-year likelihood of recurrence was created using multivariate Cox proportional hazard models to find the significant determinants of recurrence. Using the concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curves, and decision curve analyses (DCA), we internally validated the nomogram. Results The significant factors related to NMIBC recurrence were age, blood platelet count, especially for the urine leukocyte count and mucus filament. The constructed nomogram performed well in the customized prediction of NMIBC recurrence at 6th, 12th, 24th and 36th month, of which the C-index was 0.724. The calibration curve and the ROC curve both validated the prediction accuracy. On DCA, the nomogram presented good net benefit gains across a wide range of threshold probabilities. Furthermore, the Nomogram-related risk score was used to divide the patient population into two groups with significant recurrence disparities. Conclusion For the prediction of NMIBC recurrence, our unique nomogram demonstrated a respectable degree of discriminative capacity, sufficient calibration, and considerable net benefit gain. There will be a need for additional internal and external validation.

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Section: Discussionmentioning

confidence: 99%

Nomogram for customized recurrence prediction in primary non-muscle-invasive bladder cancer based on routine blood and urine parameters

He,

Pan,

Zhang

et al. 2024

BMC Urol

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show abstract

“…Although newer methods like uid biopsy and genetic testing seem to increase the accuracy of BC prognosis predictions, they have drawbacks including a hefty price tag and labor-intensive analysis [23][24][25]. The majority of them are also still through clinical studies.…”

Section: Discussionmentioning

confidence: 99%

Novel nomogram based on routine blood and urine parameters for customized prediction in recurrence of primary non-muscle-invasive bladder cancer

Pan

Zhang

et al. 2023

Preprint

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Purpose A prevalent condition with a high probability of recurrence, non-muscle invasive bladder cancer (NMIBC) necessitates lifetime surveillance. In patients with pathologically confirmed NMIBC, our goal was to create a unique nomogram to predict recurrence after transurethral resection of bladder tumor (TURBT). Methods Our institution's 91 NMIBC patients with complete follow-up data between January 2017 and February 2021 were included in the retrospective analysis. The nomogram predicting the 0.5, 1, 2 and 3-year likelihood of recurrence was created using multivariate Cox proportional hazard models to find the significant determinants of recurrence. Using the concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curves, and decision curve analyses (DCA), we internally validated the nomogram. Results The significant factors related to NMIBC recurrence were age, blood platelet count, especially for the urine leukocyte count and mucus filament. The constructed nomogram performed well in the customized prediction of NMIBC recurrence at 6th, 12th, 24th and 36th month, of which the C-index was 0.724. The calibration curve and the ROC curve both validated the prediction accuracy. On DCA, the nomogram presented good net benefit gains across a wide range of threshold probabilities. Furthermore, the Nomogram-related risk score was used to divide the patient population into two groups with significant recurrence disparities. Conclusion For the prediction of NMIBC recurrence, our unique nomogram demonstrated a respectable degree of discriminative capacity, sufficient calibration, and considerable net benefit gain. There will be a need for additional internal and external validation.

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“…In summary, we identified biomarkers of HCC based on computational biology in oncologymethods ( 42 , 43 ), and constructed prognostic models using machine learning methods ( 44 – 46 ). Wehave established an 11-DDR gene signature that can accurately forecast the prognosis ofhepatocellular carcinoma (HCC).…”

Section: Discussionmentioning

confidence: 99%

Identification of DNA repair gene signature and potential molecular subtypes in hepatocellular carcinoma

Bai¹,

He²,

Ma³

et al. 2023

Front. Oncol.

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DNA repair is a critical factor in tumor progression as it impacts tumor mutational burden, genome stability, PD-L1 expression, immunotherapy response, and tumor-infiltrating lymphocytes (TILs). In this study, we present a prognostic model for hepatocellular carcinoma (HCC) that utilizes genes related to the DNA damage response (DDR). Patients were stratified based on their risk score, and groups with lower risk scores demonstrated better survival rates compared to those with higher risk scores. The prognostic model’s accuracy in predicting 1-, 3-, and 5-year survival rates for HCC patients was analyzed using receiver operator curve analysis (ROC). Results showed good accuracy in predicting survival rates. Additionally, we evaluated the prognostic model’s potential as an independent factor for HCC prognosis, along with tumor stage. Furthermore, nomogram was employed to determine the overall survival year of patients with HCC based on this independent factor. Gene set enrichment analysis (GSEA) revealed that in the high-risk group, apoptosis, cell cycle, MAPK, mTOR, and WNT cascades were highly enriched. We used training and validation datasets to identify potential molecular subtypes of HCC based on the expression of DDR genes. The two subtypes differed in terms of checkpoint receptors for immunity and immune cell filtration capacity.Collectively, our study identified potential biomarkers of HCC prognosis, providing novel insights into the molecular mechanisms underlying HCC.

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A novel focal adhesion-related risk model predicts prognosis of bladder cancer —— a bioinformatic study based on TCGA and GEO database

Cited by 5 publications

References 54 publications

Nomogram for customized recurrence prediction in primary non-muscle-invasive bladder cancer based on routine blood and urine parameters

Nomogram for customized recurrence prediction in primary non-muscle-invasive bladder cancer based on routine blood and urine parameters

Novel nomogram based on routine blood and urine parameters for customized prediction in recurrence of primary non-muscle-invasive bladder cancer

Identification of DNA repair gene signature and potential molecular subtypes in hepatocellular carcinoma

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