An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis

Xiong, Yaoyi; Yuan, Lushun; Xiong, Jing; Xu, Huimin; Luo, Yongwen; Wang, Gang; Ju, Lingao; Xiao, Yu; Wang, Xinghuan

doi:10.1111/jcmm.14918

Cited by 33 publications

(23 citation statements)

References 32 publications

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“…In this study, we find that the model variables were screened by LASSO Cox regression has better accuracy and resolution than the model variables were screened by Forward Stepwise Cox regression. Number of studies have shown that it plays an important role in cancer research: especially in lung adenocarcinoma [ 18 ]; bladder cancer [ 19 ]; gastric cancer [ 20 ]; and pancreatic cancer [ 21 ]. Meanwhile, Liu et al applied LASSO Cox regression to the establishment of a prognostic model for hepatocellular carcinoma [ 22 , 23 ].…”

Section: Discussionmentioning

confidence: 99%

“…LASSO Cox regression analysis constructs a penalty function to obtain a more refined model. And a number of studies have shown that it plays an important role in cancer research: Li et al applied LASSO Cox regression to the establishment of a prognostic model for lung adenocarcinoma [ 18 ]; Xiong et al establishment of an outcome model for bladder cancer [ 19 ]; Jiang et al establishment of a prognostic model gastric cancer [ 20 ]; Wu et al establishment of a prognostic model pancreatic cancer [ 21 ]. Meanwhile, Liu et al applied LASSO Cox regression to the establishment of a prognostic model for hepatocellular carcinoma [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

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A prognostic nomogram based on LASSO Cox regression in patients with alpha-fetoprotein-negative hepatocellular carcinoma following non-surgical therapy

et al. 2021

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Background Alpha-fetoprotein-negative hepatocellular carcinoma (AFP-NHCC) (< 8.78 ng/mL) have special clinicopathologic characteristics and prognosis. The aim of this study was to apply a new method to establish and validate a new model for predicting the prognosis of patients with AFP-NHCC. Methods A total of 410 AFP-negative patients with clinical diagnosed with HCC following non-surgical therapy as a primary cohort; 148 patients with AFP-NHCC following non-surgical therapy as an independent validation cohort. In primary cohort, independent factors for overall survival (OS) by LASSO Cox regression were all contained into the nomogram1; by Forward Stepwise Cox regression were all contained into the nomogram2. Nomograms performance and discriminative power were assessed with concordance index (C-index) values, area under curve (AUC), Calibration curve and decision curve analyses (DCA). The results were validated in the validation cohort. Results The C-index of nomogram1was 0.708 (95%CI: 0.673–0.743), which was superior to nomogram2 (0.706) and traditional modes (0.606–0.629). The AUC of nomogram1 was 0.736 (95%CI: 0.690–0.778). In the validation cohort, the nomogram1 still gave good discrimination (C-index: 0.752, 95%CI: 0.691–0.813; AUC: 0.784, 95%CI: 0.709–0.847). The calibration curve for probability of OS showed good homogeneity between prediction by nomogram1 and actual observation. DCA demonstrated that nomogram1 was clinically useful. Moreover, patients were divided into three distinct risk groups for OS by the nomogram1: low-risk group, middle-risk group and high-risk group, respectively. Conclusions Novel nomogram based on LASSO Cox regression presents more accurate and useful prognostic prediction for patients with AFP-NHCC following non-surgical therapy. This model could help patients with AFP-NHCC following non-surgical therapy facilitate a personalized prognostic evaluation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A prognostic nomogram based on LASSO Cox regression in patients with alpha-fetoprotein-negative hepatocellular carcinoma following non-surgical therapy

et al. 2021

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“…The forest was used to show the P-values , HR and 95% confidence interval (CI) of each variable through 'forestplot' R package. A nomogram was developed based on the results of multivariate Cox proportional hazards analysis to predict the 1-year, 2-year, and 3-year overall recurrence 43 . The nomogram provided a graphical representation of the factors, which can be used to calculate the risk of recurrence for an individual patient by the points associated with each risk factor through 'rms' R package 44 .…”

Section: Methodsmentioning

confidence: 99%

Centromere Protein F (CENPF) Serves as a Potential Prognostic Biomarker and Target for Human Hepatocellular Carcinoma

Huang¹,

Chen²,

Wang³

et al. 2021

J. Cancer

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Overexpression of Centromere Protein F ( CENPF ) is associated with tumorigenesis of many human malignant tumors. But the molecular mechanism and prognostic value of CENPF in patients with hepatocellular carcinoma (HCC) are still unclear. In this essay, expression of CENPF in HCC tumors were evaluated in a series of databases, including GEO, TCGA, Oncomine, GEPIA, The Human Protein Atlas and Kaplan-Meier plotter. It was apparent that mRNA and protein expression levels of CENPF were significantly increased in patients with HCC and were manifestly associated with the tumor stage of HCC. Aberrant expressions of CENPF were significantly linked with worse overall survival (OS) and progression-free survival (PFS) in HCC patients. Then, immunohistochemistry of CENPF in human HCC samples was carried out to suggest that CENPF protein was over-expressed in HCC tissues, compared with paired adjacent non-cancerous samples. And small interfering RNAs of CENPF in the human HepG2 cells were further performed to reveal that down-regulation of CENPF significantly inhibited cell proliferation, cell migration, and cell invasion, but slightly promoted cell apoptosis in human HepG2 cells. Moreover, the gene-set enrichment analysis (GSEA) was conducted to probe the biology process and molecular signaling pathway of CENPF in HCC. The GSEA analysis pointed out that CENPF was principally enriched in cell cycle and closely related to E2F1 and CDK1 in the regulation of cell cycle, especially during G2/M transition of mitosis in HCC. Additionally, immune infiltration analysis by CIBERSORTx revealed that mutilpe immune cells, including T reg , etc., were significantly different in HCC samples with CENPF high , compared with CENPF low . These results collectively demonstrated that CENPF might serve as a potential prognostic biomarker and novel therapeutic target for HCC. However, further research is needed to validate our findings and promote the clinical application of CENPF in HCC.

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“…At present, it has been widely used to compare differentially expressed genes (DEGs) and help investigating the interactions among genes in different modules [24]. Besides, it is also used to identify rules for predicting survival of patients by investigating the relationship between clinical traits and tissue microarray data [25].…”

Section: Introductionmentioning

confidence: 99%

WGCNA reveals key gene modules regulated by the combined treatment of colon cancer with PHY906 and CPT11

Xing

Wang

et al. 2020

Bioscience Reports

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Irinotecan (CPT11) is one of the most effective drugs for treating colon cancer, but its severe side effects limit its application. Recently, a traditional Chinese herbal preparation, named PHY906, has been proved to be effective for improving therapeutic effect and reducing side effects of CPT11. The aim of this study was to provide novel insight to understand the molecular mechanism underlying PHY906-CPT11 intervention of colon cancer. Based on the GSE25192 dataset, for different three treatments (PHY906, CPT11, and PHY906-CPT11), we screened out differentially expressed genes (DEGs) and constructed a co-expression network by weighted gene co-expression network analysis (WGCNA) to identify hub genes. The key genes of the three treatments were obtained by merging the DEGs and hub genes. For the PHY906-CPT11 treatment, a total of 18 key genes including Eif4e, Prr15, Anxa2, Ddx5, Tardbp, Skint5, Prss12 and Hnrnpa3, were identified. The results of functional enrichment analysis indicated that the key genes associated with PHY906-CPT11 treatment were mainly enriched in “superoxide anion generation” and “complement and coagulation cascades”. Finally, we validated the key genes by GEPIA and RT-PCR analysis, the results indicated that EIF4E, PRR15, ANXA2, HNRNPA3, NCF1, C3AR1, PFDN2, RGS10, GNG11, and TMSB4X might play an important role in the treatment of colon cancer with PHY906-CPT11. In conclusion, a total of 18 key genes were identified in this study. These genes showed strong correlation with PHY906-CPT11 treatment in colon cancer, which may help elucidate the underlying molecular mechanism of PHY906-CPT11 treatment in colon cancer.

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An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis

Cited by 33 publications

References 32 publications

A prognostic nomogram based on LASSO Cox regression in patients with alpha-fetoprotein-negative hepatocellular carcinoma following non-surgical therapy

A prognostic nomogram based on LASSO Cox regression in patients with alpha-fetoprotein-negative hepatocellular carcinoma following non-surgical therapy

Centromere Protein F (CENPF) Serves as a Potential Prognostic Biomarker and Target for Human Hepatocellular Carcinoma

WGCNA reveals key gene modules regulated by the combined treatment of colon cancer with PHY906 and CPT11

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