Image6_Systematic Elucidation of the Aneuploidy Landscape and Identification of Aneuploidy Driver Genes in Prostate Cancer.TIF

Aneuploidy is widely identified as a remarkable feature of malignancy genomes. Increasing evidences suggested aneuploidy was involved in the progression and metastasis of prostate cancer (PCa). Nevertheless, no comprehensive analysis was conducted in PCa about the effects of aneuploidy on different omics and, especially, about the driver genes of aneuploidy. Here, we validated the association of aneuploidy with the progression and prognosis of PCa and performed a systematic analysis in mutation profile, methylation profile, and gene expression profile, which detailed the molecular process aneuploidy implicated. By multi-omics analysis, we managed to identify 11 potential aneuploidy driver genes (GSTM2, HAAO, C2orf88, CYP27A1, FAXDC2, HFE, C8orf88, GSTP1, EFS, HIF3A, and WFDC2), all of which were related to the development and metastasis of PCa. Meanwhile, we also found aneuploidy and its driver genes were correlated with the immune microenvironment of PCa. Our findings could shed light on the tumorigenesis of PCa and provide a better understanding of the development and metastasis of PCa; additionally, the driver genes could be promising and actionable therapeutic targets pointing to aneuploidy.</p

Image5_Systematic Elucidation of the Aneuploidy Landscape and Identification of Aneuploidy Driver Genes in Prostate Cancer.TIF

Aneuploidy is widely identified as a remarkable feature of malignancy genomes. Increasing evidences suggested aneuploidy was involved in the progression and metastasis of prostate cancer (PCa). Nevertheless, no comprehensive analysis was conducted in PCa about the effects of aneuploidy on different omics and, especially, about the driver genes of aneuploidy. Here, we validated the association of aneuploidy with the progression and prognosis of PCa and performed a systematic analysis in mutation profile, methylation profile, and gene expression profile, which detailed the molecular process aneuploidy implicated. By multi-omics analysis, we managed to identify 11 potential aneuploidy driver genes (GSTM2, HAAO, C2orf88, CYP27A1, FAXDC2, HFE, C8orf88, GSTP1, EFS, HIF3A, and WFDC2), all of which were related to the development and metastasis of PCa. Meanwhile, we also found aneuploidy and its driver genes were correlated with the immune microenvironment of PCa. Our findings could shed light on the tumorigenesis of PCa and provide a better understanding of the development and metastasis of PCa; additionally, the driver genes could be promising and actionable therapeutic targets pointing to aneuploidy.</p

Image4_Systematic Elucidation of the Aneuploidy Landscape and Identification of Aneuploidy Driver Genes in Prostate Cancer.TIF

Aneuploidy is widely identified as a remarkable feature of malignancy genomes. Increasing evidences suggested aneuploidy was involved in the progression and metastasis of prostate cancer (PCa). Nevertheless, no comprehensive analysis was conducted in PCa about the effects of aneuploidy on different omics and, especially, about the driver genes of aneuploidy. Here, we validated the association of aneuploidy with the progression and prognosis of PCa and performed a systematic analysis in mutation profile, methylation profile, and gene expression profile, which detailed the molecular process aneuploidy implicated. By multi-omics analysis, we managed to identify 11 potential aneuploidy driver genes (GSTM2, HAAO, C2orf88, CYP27A1, FAXDC2, HFE, C8orf88, GSTP1, EFS, HIF3A, and WFDC2), all of which were related to the development and metastasis of PCa. Meanwhile, we also found aneuploidy and its driver genes were correlated with the immune microenvironment of PCa. Our findings could shed light on the tumorigenesis of PCa and provide a better understanding of the development and metastasis of PCa; additionally, the driver genes could be promising and actionable therapeutic targets pointing to aneuploidy.</p

Image1_Systematic Elucidation of the Aneuploidy Landscape and Identification of Aneuploidy Driver Genes in Prostate Cancer.TIF

Aneuploidy is widely identified as a remarkable feature of malignancy genomes. Increasing evidences suggested aneuploidy was involved in the progression and metastasis of prostate cancer (PCa). Nevertheless, no comprehensive analysis was conducted in PCa about the effects of aneuploidy on different omics and, especially, about the driver genes of aneuploidy. Here, we validated the association of aneuploidy with the progression and prognosis of PCa and performed a systematic analysis in mutation profile, methylation profile, and gene expression profile, which detailed the molecular process aneuploidy implicated. By multi-omics analysis, we managed to identify 11 potential aneuploidy driver genes (GSTM2, HAAO, C2orf88, CYP27A1, FAXDC2, HFE, C8orf88, GSTP1, EFS, HIF3A, and WFDC2), all of which were related to the development and metastasis of PCa. Meanwhile, we also found aneuploidy and its driver genes were correlated with the immune microenvironment of PCa. Our findings could shed light on the tumorigenesis of PCa and provide a better understanding of the development and metastasis of PCa; additionally, the driver genes could be promising and actionable therapeutic targets pointing to aneuploidy.</p

Image3_Systematic Elucidation of the Aneuploidy Landscape and Identification of Aneuploidy Driver Genes in Prostate Cancer.TIF

Aneuploidy is widely identified as a remarkable feature of malignancy genomes. Increasing evidences suggested aneuploidy was involved in the progression and metastasis of prostate cancer (PCa). Nevertheless, no comprehensive analysis was conducted in PCa about the effects of aneuploidy on different omics and, especially, about the driver genes of aneuploidy. Here, we validated the association of aneuploidy with the progression and prognosis of PCa and performed a systematic analysis in mutation profile, methylation profile, and gene expression profile, which detailed the molecular process aneuploidy implicated. By multi-omics analysis, we managed to identify 11 potential aneuploidy driver genes (GSTM2, HAAO, C2orf88, CYP27A1, FAXDC2, HFE, C8orf88, GSTP1, EFS, HIF3A, and WFDC2), all of which were related to the development and metastasis of PCa. Meanwhile, we also found aneuploidy and its driver genes were correlated with the immune microenvironment of PCa. Our findings could shed light on the tumorigenesis of PCa and provide a better understanding of the development and metastasis of PCa; additionally, the driver genes could be promising and actionable therapeutic targets pointing to aneuploidy.</p

Image2_Systematic Elucidation of the Aneuploidy Landscape and Identification of Aneuploidy Driver Genes in Prostate Cancer.TIF

Aneuploidy is widely identified as a remarkable feature of malignancy genomes. Increasing evidences suggested aneuploidy was involved in the progression and metastasis of prostate cancer (PCa). Nevertheless, no comprehensive analysis was conducted in PCa about the effects of aneuploidy on different omics and, especially, about the driver genes of aneuploidy. Here, we validated the association of aneuploidy with the progression and prognosis of PCa and performed a systematic analysis in mutation profile, methylation profile, and gene expression profile, which detailed the molecular process aneuploidy implicated. By multi-omics analysis, we managed to identify 11 potential aneuploidy driver genes (GSTM2, HAAO, C2orf88, CYP27A1, FAXDC2, HFE, C8orf88, GSTP1, EFS, HIF3A, and WFDC2), all of which were related to the development and metastasis of PCa. Meanwhile, we also found aneuploidy and its driver genes were correlated with the immune microenvironment of PCa. Our findings could shed light on the tumorigenesis of PCa and provide a better understanding of the development and metastasis of PCa; additionally, the driver genes could be promising and actionable therapeutic targets pointing to aneuploidy.</p

A simulation-based risk interaction network model for risk management in international construction projects

The inherent complexity in International Construction Projects (ICPs) brings forth the intricate interactions between risks, invalidating the risk analysis under the conventional perception of independent risks. The diverse phases of ICPs feature the temporal dynamics of risks and interactions, exacerbating the challenge of pinpointing critical risks. This study aims to model how the risks are interconnected and amplified by proposing an ICP risk management framework, in which the project phase-based Risk Interaction Network (RIN) is constructed. An integrated index is designed to evaluate each risk’s criticality and discern key risks within a two-part procedure, where the simulation-based model estimates risk sensitivity to predecessor risks and captures the stochasticity of risk occurring, and network analysis gauges risk spread power on the entire network. An example of an application is provided to illustrate the utility of the proposed framework.</p

Sequencing chromatograms of different genotypes at g.2244A>G locus.

<p>Sequencing chromatograms of different genotypes at g.2244A>G locus.</p

Three versus four cycles of neoadjuvant chemotherapy for muscle-invasive bladder cancer: a systematic review and meta-analysis

The optimal cycle of neoadjuvant chemotherapy (NAC) for muscle-invasive bladder cancer (MIBC) remains controversial. This study aimed to compare the efficacy of three and four cycles of NAC in the treatment of MIBC through a systematic review and meta-analysis of the literature. Relevant studies were systematically collected and reviewed in PubMed, Medline, Embase, Web of Science Databases, and the Cochrane Library. Relative ratios (RRs), Hazard ratios (HRs) and their 95% confidence intervals (CIs) were used to estimate outcome measures. Studies comparing the pathological response and prognosis of three versus four cycles of NAC for MIBC were included. Five studies were included in this meta-analysis, including 2190 patients, of whom 1016 underwent three cycles of NAC and 1174 underwent four cycles of NAC. All studies were retrospective cohort studies. We found that 4 cycles of NAC had significantly better cancer-specific survival than 3 cycles (HR = 1.31, 95%CI,1.03–1.67, p = 0.029). There was no significant difference in overall survival between patients who received 3 and 4 cycles of chemotherapy (HR = 1.18, 95%CI = 0.83–1.69, p = 0.345). Similarly, no significant difference was observed in pathological objective response (RR = 0.95, 95%CI= 0.81–1.11, p = 0.515) and complete response rates (RR = 0.87, 95%CI = 0.69–1.11, p = 0.256) in MIBC after 3 or 4 cycles of NAC. Three and four cycles of NAC had similar pathological responses and prognosis for MIBC, although the cancer-specific survival rate of four cycles was better than that of three cycles. The pathological response rate and overall survival of three and four cycles of neoadjuvant chemotherapy for muscle-invasive bladder cancer were similar.Four cycles of neoadjuvant chemotherapy may improve the cancer-specific survival of patients with muscle-invasive bladder cancerIt is reasonable and feasible for clinicians to use three or four cycles of neoadjuvant chemotherapy. The pathological response rate and overall survival of three and four cycles of neoadjuvant chemotherapy for muscle-invasive bladder cancer were similar. Four cycles of neoadjuvant chemotherapy may improve the cancer-specific survival of patients with muscle-invasive bladder cancer It is reasonable and feasible for clinicians to use three or four cycles of neoadjuvant chemotherapy.</p

Sequencing chromatograms of different genotypes at g.2264A>G locus.

<p>Sequencing chromatograms of different genotypes at g.2264A>G locus.</p