3D spatial distribution of soil pollutants based on geo-shadowing anisotropic RBF-PCA

Research on soil contamination has become increasingly important, but there is limited information about where to sample for pollutants. Thus, the use of three-dimensional (3D) spatial interpolation techniques has been promoted in this area of study. However, the application of traditional interpolation methods is limited in geography, especially in the expression of anisotropy, and it is not associated with geographical properties. To address this issue, we used a test site (a factory in Nanjing) to develop a new research method based on the geographical shading radial basis function (RBF) interpolation method, which considers 3D anisotropy and geographical attribute expression. Drilling and uniform sampling were used to sample the contaminated area at this test site. This approach included two steps: i) An ellipsoid with anisotropic properties was constructed. Thus, the first step was to determine the shape of the ellipsoid using principal component analysis (PCA) to determine the main orientations and construct a rotational and stretched matrix. The second step was determining the ellipsoid size by computing the range using the variogram method for orientations. ii) During field measurement, the geospatial direction influences soil attribute values, so a shadowing calculation method was derived for quadratic weight determination. Then, the weight of the attribute value of known points can be assigned to meet the field conditions. Lastly, the model was evaluated using the root mean square error (RMSE). For the 2D space, the RMSE values of Kriging, RBF, and the proposed method are 6.09, 7.12, and 5.02, respectively. The R2 values of Kriging, RBF, and the proposed method are 0.871, 0.832, and 0.946, respectively. For the 3D space, the RMSE values of Kriging, RBF, and the proposed method are 2.65, 2.23, and 2.58, respectively. The R2 values of Kriging, RBF, and the proposed method are 0.934, 0.912, and 0.953, respectively. The resulting fitted model was relatively smooth and met experimental needs. Thus, we believe that the interpolation method can be applied as a new method to predict the distribution of soil pollutants

The dynamic dysregulated network identifies stage-specific markers during lung adenocarcinoma malignant progression and metastasis

Brain metastasis occurs in approximately 30% of patients with lung adenocarcinoma (LUAD) and is closely associated with poor prognosis, recurrence, and death. However, dynamic gene regulation and molecular mechanism driving LUAD progression remain poorly understood. In this study, we performed a comprehensive single-cell transcriptome analysis using data from normal, early stage, advanced stage, and brain metastasis LUAD. Our single-cell-level analysis reveals the cellular composition heterogeneity at different stages during LUAD progression. We identified stage-specific risk genes that could contribute to LUAD progression and metastasis by reprogramming immune-related and metabolic-related functions. We constructed an early advanced metastatic dysregulated network and revealed the dynamic changes in gene regulations during LUAD progression. We identified 6 early advanced (HLA-DRB1, HLA-DQB1, SFTPB, SFTPC, PLA2G1B, and FOLR1), 8 advanced metastasis (RPS15, RPS11, RPL13A, RPS24, HLA-DRB5, LYPLA1, KCNJ15, and PSMA3), and 2 common risk genes in different stages (SFTPD and HLA-DRA) as prognostic markers in LUAD. Particularly, decreased expression of HLA-DRA, HLA-DRB1, HLA-DQB1, and HLA-DRB5 refer poor prognosis in LUAD by controlling antigen processing and presentation and T cell activation. Increased expression of PSMA3 and LYPLA1 refer poor prognosis by reprogramming fatty acid metabolism and RNA catabolic process. Our findings will help further understanding the pathobiology of brain metastases in LUAD

Additional file 1 of Clinical features and treatment outcome of lymphoepithelioma-like carcinoma from multiple primary sites: a population-based, multicentre, real-world study

Additional file 1. Figure S1: Kaplan–Meier survival analysis for OS in patients receiving adjuvant chemotherapy at stage I (A). DFS(B) and OS(C) in patients receiving adjuvant radiotherapy at stage

Additional file 4 of Clinical features and treatment outcome of lymphoepithelioma-like carcinoma from multiple primary sites: a population-based, multicentre, real-world study

Additional file 4. Figure S4: Kaplan–Meier survival analysis for PFS (A) and OS (B) in patients receiving paclitaxel-based chemotherapy as first-line regimen at stage IV or after relapsed. PFS (C) and OS (D) in patients receiving gemcitabine-based chemotherapy as first-line regimen at stage IV or after relapsed. OS in patients at stage IV or after relapsed receiving radiotherapy (E

Additional file 2 of Clinical features and treatment outcome of lymphoepithelioma-like carcinoma from multiple primary sites: a population-based, multicentre, real-world study

Additional file 2. Figure S2: Kaplan–Meier survival analysis for OS in patients receiving adjuvant chemotherapy at stage II (A). DFS(B) and OS(C) in patients receiving adjuvant radiotherapy at stage I

Additional file 6 of Clinical features and treatment outcome of lymphoepithelioma-like carcinoma from multiple primary sites: a population-based, multicentre, real-world study

Additional file 6. Figure S6: Kaplan–Meier survival analysis for OS according to ECOG PS≥2 (A), stage III-IV (B) , high level of EBV-DNA (C), liver metastasis (D) and bone metastasis (E

Additional file 5 of Clinical features and treatment outcome of lymphoepithelioma-like carcinoma from multiple primary sites: a population-based, multicentre, real-world study

Additional file 5. Figure S5: Kaplan–Meier survival analysis for OS in patients receiving anti- angiogenesis (A) and anti- EGFR (B) therapy at stage IV or after relapsed. Kaplan–Meier survival analysis for PFS in patients receiving anti-angiogenesis (C) and anti-EGFR (D) therapy at stage IV or after relapse

Additional file 3 of Clinical features and treatment outcome of lymphoepithelioma-like carcinoma from multiple primary sites: a population-based, multicentre, real-world study

Additional file 3 Supplement Figure 3 Kaplan–Meier survival analysis for OS in patients receiving adjuvant radiotherapy at stage III (A). DFS(B) and OS(C) in patients receiving adjuvant chemotherapy at stage II

What is the appropriate genetic testing criteria for breast cancer in the Chinese population?—Analysis of genetic and clinical features from a single cancer center database

Abstract Background Genetic testing plays an important role in guiding screening, diagnosis, and precision treatment of breast cancer (BC). However, the appropriate genetic testing criteria remain controversial. The current study aims to facilitate the development of suitable strategies by analyzing the germline mutational profiles and clinicopathological features of large‐scale Chinese BC patients. Methods BC patients who had undergone genetic testing at the Sun Yat‐sen University Cancer Center (SYSUCC) from September 2014 to March 2022 were retrospectively reviewed. Different screening criteria were applied and compared in the population cohort. Results A total of 1035 BC patients were enrolled, 237 pathogenic or likely pathogenic variants (P/LPV) were identified in 235 patients, including 41 out of 203 (19.6%) patients tested only for BRCA1/2 genes, and 194 out of 832 (23.3%) received 21 genes panel testing. Among the 235 P/LPV carriers, 222 (94.5%) met the NCCN high‐risk criteria, and 13 (5.5%) did not. While using Desai's criteria of testing, all females diagnosed with BC by 60 years and NCCN criteria for older patients, 234 (99.6%) met the high‐risk standard, and only one did not. The 21 genes panel testing identified 4.9% of non‐BRCA P/LPVs and a significantly high rate of variants of uncertain significance (VUSs) (33.9%). The most common non‐BRCA P/LPVs were PALB2 (11, 1.3%), TP53 (10, 1.2%), PTEN (3, 0.4%), CHEK2 (3, 0.4%), ATM (3, 0.4%), BARD1 (3, 0.4%), and RAD51C (2, 0.2%). Compared with BRCA1/2 P/LPVs, non‐BRCA P/LPVs showed a significantly low incidence of NCCN criteria listed family history, second primary cancer, and different molecular subtypes. Conclusions Desai's criteria might be a more appropriate genetic testing strategy for Chinese BC patients. Panel testing could identify more non‐BRCA P/LPVs than BRCA1/2 testing alone. Compared with BRCA1/2 P/LPVs, non‐BRCA P/LPVs exhibited different personal and family histories of cancer and molecular subtype distributions. The optimal genetic testing strategy for BC still needs to be investigated with larger continuous population studies