Search

Additional file 1: Figure S1. TGCA early LUAD mutation cohort analyzed by SomaticSniper (A) Overview of TGCA Stage IA LUAD cohort mutations analyzed with the tool of SomaticSniper. (B) Waterfall of the top 150 mutated genes in the TCGA Stage IA LUAD cohort analyzed with the tool of SomaticSniper.Figure S2. TGCA early LUAD mutation cohort analyzed by MuTect (A) Overview of TGCA Stage IA LUAD cohort mutations analyzed with the tool of MuTect. (B) Waterfall of the top 150 mutated genes in the TCGA Stage IA LUAD cohort analyzed with the tool of MuTect.Figure S3. TGCA early LUAD mutation cohort analyzed by MuSE (A) Overview of TGCA Stage IA LUAD cohort mutations analyzed with the tool of MuSE. (B) Waterfall of the top 150 mutated genes in the TCGA Stage IA LUAD cohort analyzed with the tool of MuSE.Figure S4. The TMB of nuclear and mitochondrial genomes between groups with different clinical characteristics. (A) The TMB of nuclear genomes in tumor tissues between the age groups of ≤60 years and >60. (B) The TMB of nuclear genomes in tumor tissues between the groups of male and female. (C) The TMB of nuclear genomes in tumor tissues between the MIAs and IA groups. (D) The TMB of mitochondrial genomes in tumor tissues between the age groups of ≤60 years and >60. (E) The TMB of mitochondrial genomes in tumor tissues between the groups of male and female. (F) The TMB of mitochondrial genomes in tumor tissues between the MIAs and IA groups. (G) The TMB of nuclear genomes in cfDNA from plasma samples between the age groups of ≤60 years and >60. (H) The TMB of nuclear genomes in cfDNA from plasma samples between the groups of male and female. (I) The TMB of nuclear genomes in cfDNA from plasma samples between the MIAs and IA groups. (J) The TMB of mitochondrial genomes in cfDNA from plasma samples between the age groups of ≤60 years and >60. (K) The TMB of mitochondrial genomes in cfDNA from plasma samples between the groups of male and female. (L) The TMB of mitochondrial genomes in cfDNA from plasma samples between the MIAs and IA groups.

Purpose: Epidermal growth factor receptor (EGFR) mutation is the most common target for precision treatment in metastatic lung adenocarcinoma. We investigated the predictive role of 18F-FDG PET/CT and clinicopathological features for EGFR mutations in lung adenocarcinoma with bone metastasis. Methods: Seventy-five lung adenocarcinoma patients with histologically confirmed bone metastasis were included. They all received EGFR status test and PET/CT before systemic treatment. The differences of maximum standardized uptake value (SUVmax) in primary tumor (pSUVmax), regional lymph node (nSUVmax) and bone metastasis (bmSUVmax) between different EGFR status groups were compared, alongside with common clinicopathological features. Multivariate logistic regression analysis was performed to evaluate predictors of EGFR mutations. Results: EGFR mutations were found in 37 patients (49.3%). EGFR mutations were more common in females, non-smokers, expression of Thyroid Transcription Factor-1 (TTF-1) and NaspinA. Low bmSUVmax was significantly associated with EGFR mutations, while no significant difference was observed in pSUVmax and nSUVmax. Multivariate analysis showed that bmSUVmax ≤7, non-smoking, expression of TTF-1 were predictors of EGFR mutations. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was 0.84 for the combination of the three factors. Conclusion: Low bmSUVmax is more frequently in EGFR mutations, and bmSUVmax is an independent predictor of EGFR mutations. Combining bmSUVmax with other clinicopathological features could forecast the EGFR status in lung adenocarcinoma with unavailable EGFR gene testing.

Purpose: We evaluated the imaging and clinical features for discriminating the possibility of metastasis among FDG-avid bone lesions in 18F-FDG PET/CT in patients who have received bone biopsy. Methods: The retrospective study included patients who underwent both 18F-FDG PET/CT and bone biopsy for FDG-avid bone lesions. Bone lesions maximum standardized uptake value (SUVmax), CT findings, alongside with common clinical features were analyzed. Results: From the 338 patients enrolled in the final study, all of them were received bone biopsy. Biopsies confirm metastasis in 256 cases (75.74%) and benign tissue in 82 cases (24.26%). Metastasis group had higher bone SUVmax than benign group (median 7.9 vs 4.5, p

OBJECTIVE: To investigate the value of texture analysis in magnetic resonance images for the evaluation of Gleason scores (GS) of prostate cancer. METHODS: Sixty-six prostate cancer patients are retrospective enrolled, which are divided into five groups namely, GS = 6, 3 + 4, 4 + 3, 8 and 9–10 according to postoperative pathological results. Extraction and analysis of texture features in T2-weighted MR imaging defined tumor region based on pathological specimen after operation are performed by texture software OmniKinetics. The values of texture are analyzed by single factor analysis of variance (ANOVA), and Spearman correlation analysis is used to study the correlation between the value of texture and Gleason classification. Receiver operating characteristic (ROC) curve is then used to assess the ability of applying texture parameters to predict Gleason score of prostate cancer. RESULTS: Entropy value increases and energy value decreases as the elevation of Gleason score, both with statistical difference among five groups (F = 10.826, F = 2.796, P

Despite recent advances in targeted and immune‐based therapies, the poor prognosis of lung adenocarcinoma (LUAD) with bone metastasis (BM) remains a challenge. First, two‐dimensional gel electrophoresis (2‐DE) was used to identify proteins that were differentially expressed in LUAD with BM, and then matrix‐assisted laser desorption/ionization time of flight mass spectrometry (MALDI‐TOF‐MS) was used to identify these proteins. Second, the Cancer Genome Atlas (TCGA) was used to identify mutations in these differentially expressed proteins and Kaplan–Meier plotter (KM Plotter) was used to generate survival curves for the analyzed cases. Immunohistochemistry (IHC) was used to check the expression of proteins in 28 patients with BM and nine patients with LUAD. Lastly, the results were analyzed with respect to clinical features and patient's follow‐up. We identified a number of matched proteins from 2‐DE. High expression of enolase 1 (ENO1) (HR = 1.67, logrank P = 1.9E‐05), ribosomal protein lateral stalk subunit P2 (RPLP2) (HR = 1.77, logrank P = 2.9e‐06), and NME/NM23 nucleoside diphosphate kinase 2 (NME1‐NME2) (HR = 2.65, logrank P = 3.9E‐15) was all significantly associated with poor survival (P