Construction of PPI and module networks in CRC subtypes.

Nodes of a module forming subnetworks of PPI were colored differently on a subtype basis (same color across different subtypes didn’t represent similar groupings of module function). The images of CMS1(A), CMS2(B), CMS3(C), CMS4(D) were shown accordingly.</p

DataSheet_1_Effects of the Kuroshio on internal tides in the Luzon Strait: A model study.pdf

Internal tides have a great impact on the meridional overturning circulation and climate variability through contributing to diapycnal mixing. The Luzon Strait (LS) is one of the most important sites of internal tide generation in the global ocean. In this study, we evaluate the effect of the Kuroshio on the M2 and K1 internal tides in both summer and winter seasons in the South China Sea (SCS), particularly within the LS. High-resolution ocean numerical simulations with the Kuroshio Current were compared with those without. We found that the Kuroshio has negligible impact on the generation site of internal tides. Compared to seasonal variability in the total barotropic to baroclinic conversion rate over the LS, the Kuroshio has relatively little influence. However, the Kuroshio flow strongly guides the propagating direction of the internal tides from the LS into the SCS. The Kuroshio also substantially decreases the southward energy fluxes going out of the LS. For both M2 and K1 tides, turning off the Kuroshio leads to a weaker energy exchange between the background shear and internal tides. Turning off the Kuroshio also weakens the divergence of internal tide energy due to the advection of background flow. Thus, our results reveal a non-negligible effect of the Kuroshio on the internal tides in the LS. If one aims to realistically simulate, or better understand, internal tides, these results indicate that one should include realistic oceanic circulation fields.</p

Image_1_Incidence and associated factors of developing second pelvic malignant neoplasms among prostate cancer patients treated with radiotherapy.tif

ObjectiveTo identify risk factors of secondary pelvic malignant neoplasms (SPMNs) among prostate cancer (PCa) patients treated with radiotherapy. Simultaneously, population-based data were used to validate the high risk of SPMNs in PCa patients with radiotherapy.Materials and methodsWe identified male patients diagnosed with PCa (localized and regional) as the first primary cancer and pelvic malignant neoplasm (including bladder and rectal cancer) as secondary cancer from Surveillance, Epidemiology, and End Results database (1975-2020). An external validation cohort was obtained from the First Affiliated Hospital of Nanchang University. The Fine-Gray competing risk regression and Poisson regression were utilized to evaluate the risk of SPMNs development. Poisson regression was also performed to calculate the standardized incidence ratio (SIR). The Kaplan-Meier method was used to assess the overall survival (OS) of patients with SPMNs.Results89397 PCa patients treated with radiotherapy were enrolled. We identified associated factors of SPMNs, including age at diagnosis, race, year of diagnosis, marital status, radiation strategy and latency. In the multivariable competing risk regression model and Poisson regression model, a significantly higher risk of SPMNs development was observed in patients over 50 years(PConclusionWe identified several risk factors associated with SPMNs and confirmed a relatively higher incidence of bladder and rectal cancer among PCa patients with radiotherapy. These results help tailor treatment and surveillance strategies.</p

Enriched modules (score>3) found in each subtype network.

Enriched modules (score>3) found in each subtype network.</p

HCAR3 module contained two subgroups.

The color and size of the nodes indicated the input p value and the frequency of the GO term generated from GOA database; edges that linked nodes in the graph indicated similarity.</p

Heat maps visualization of the pattern of expression change for the top 50 differentially expressed genes.

The upper row of each heat map consisted of the detailed configuration of the left 19 normal samples and corresponding CRC subtype samples. The right column was the 50 up-regulated and 50 down-regulated DEGs among each subtype vs normal samples, represented in red and blue respectively in the map. Software meV. (version 4.7, http://mev.tm4.org/) was used for heat map display.</p

Table_2_Incidence and associated factors of developing second pelvic malignant neoplasms among prostate cancer patients treated with radiotherapy.docx

ObjectiveTo identify risk factors of secondary pelvic malignant neoplasms (SPMNs) among prostate cancer (PCa) patients treated with radiotherapy. Simultaneously, population-based data were used to validate the high risk of SPMNs in PCa patients with radiotherapy.Materials and methodsWe identified male patients diagnosed with PCa (localized and regional) as the first primary cancer and pelvic malignant neoplasm (including bladder and rectal cancer) as secondary cancer from Surveillance, Epidemiology, and End Results database (1975-2020). An external validation cohort was obtained from the First Affiliated Hospital of Nanchang University. The Fine-Gray competing risk regression and Poisson regression were utilized to evaluate the risk of SPMNs development. Poisson regression was also performed to calculate the standardized incidence ratio (SIR). The Kaplan-Meier method was used to assess the overall survival (OS) of patients with SPMNs.Results89397 PCa patients treated with radiotherapy were enrolled. We identified associated factors of SPMNs, including age at diagnosis, race, year of diagnosis, marital status, radiation strategy and latency. In the multivariable competing risk regression model and Poisson regression model, a significantly higher risk of SPMNs development was observed in patients over 50 years(PConclusionWe identified several risk factors associated with SPMNs and confirmed a relatively higher incidence of bladder and rectal cancer among PCa patients with radiotherapy. These results help tailor treatment and surveillance strategies.</p

Venn diagram of overlapping results of gene compositions among subtypes.

Overlaps of gene organization of three modules from each molecular subtype were shown as NUSAP1(A), CD44(B) and COL4A1(C); a number represented the number of genes shared between/among subtypes or unique to its’ own.</p

Module configuration in each subtype.

Module configuration in each subtype.</p

Kaplan–Meier Plotter of relapse-free survival evaluating genes expressed in HCARS module from CMS4 using TCGA COAD clinical data.

Red curve represented high expression level and indicated the poor prognosis, while green curve represented low expression level of the prognosis signature.</p