Triggering receptor expressed on myeloid cells 1 (TREM1) participates in the development of endometritis. This study aims at identifying the effects and interaction of TREM1 and upstream stimulatory factor 2 (USF2) in endometritis by using a model of lipopolysaccharide (LPS)-induced human endometrial epithelial cells (HEnEpCs). ELISA was performed to determine the levels of interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF-α) after LPS stimulation. TREM1 and USF2 expression was examined with RT-qPCR and Western blot....

The dysregulated circular RNAs (circRNAs) are linked to progression and chemoresistance in colorectal cancer (CRC). However, the role of circRNA protein tyrosine kinase 2 (circPTK2) in CRC progression and chemoresistance is uncertain. The circPTK2, microRNA (miR)-136-5p, m6A ‘reader’ protein YTH domain family protein 1 (YTHDF1), β-catenin and cyclin D1 abundances were examined via quantitative reverse transcription PCR or Western blotting. The progression was investigated by cell counting kit-8 (CCK-8), colony formation, transwell and xenograft analysis....

Additional file 3: Figure S1. (A, D, G) The heat map of 10 prognostic DEOSG expressions in the train (A), testing (D), and entire sets (G), respectively. (B, E, H) The comparison of 10 prognostic DEOSG expressions between high- and low-risk groups in the training (B), testing (E), and entire sets (H), respectively. (C, F, I) The correlations among 10 prognostic DEOSG between high- and low-risk groups in the training (C), testing (F), and entire...

Additional file 1: Supplementary Fig. 1. Microscopic image of a representative tissue sample with original magnification.

Additional file 1: Fig. S1. Flowchart of the study design. A. Merge of WES cohorts from five published studies (Hellman et al. [10], Rizvi et al. [11], Miao et al [12, 13], Allen et al. [14], Liu et al. [15]). B. MSKCC cohort from the published study (Samstein et al [16]). C. The TCGA dataset was used to perform DDR-related gene mutation, tumor-infiltrating immune cells and prognostic analyses.

Additional file 3: Fig. S3. Kaplan–Meier curves of OS between the MED12-Mut and wildtype groups in the TCGA cohort.

Additional file 3: Fig. S3. Kaplan–Meier curves of OS between the MED12-Mut and wildtype groups in the TCGA cohort.

Additional file 6: Figure S2. (A, F, K) Comparison of the relationship between the clinical characteristics of patients between high- and low-risk groups in the training, testing, and entire sets, respectively. (B–E) The scatter diagram showed the relationship between gender (B), clinical stage (C), T stage (D), N stage (E) and the risk score in the training set. (G–J) The scatter diagram showed the relationship between gender (G), clinical stage (E), T stage (F), N...

Additional file 4: Supplementary Fig. 4. Difference in VAP-1 expression between alive and deceased NEC infants. No significant difference in VAP-1 Area % was found (Alive = 0.065 ± 0.034; Deceased = 0.065 ± 0.038, t(1,40) = − 0.0061, p = 0.99). NS signifies p > 0.05.

Additional file 4: Supplementary Fig. 4. Difference in VAP-1 expression between alive and deceased NEC infants. No significant difference in VAP-1 Area % was found (Alive = 0.065 ± 0.034; Deceased = 0.065 ± 0.038, t(1,40) = − 0.0061, p = 0.99). NS signifies p > 0.05.

Additional file 5. Fig. S2a: Optimization of random forest at various mtry and ntree values using dipeptide frequency and pepstats features as inputs, (a) performance using top 5% features, (b) performance using top 10% features, (c) performance using top 15% features, (d) performance using top 20% features.

Additional file 6. Fig. S2b: Optimization of random forest at various mtry and ntree values using dipeptide frequency and pepstats features as inputs, (a) performance using top 30% features (b) performance using top 50% features, (c) performance using top 70% features and (d) performance using top 90% features.

Additional file 3. Fig. S1: Mean decrease in accuracy of top 30 features selected through random forest algorithm.

Additional file 5. Fig. S2a: Optimization of random forest at various mtry and ntree values using dipeptide frequency and pepstats features as inputs, (a) performance using top 5% features, (b) performance using top 10% features, (c) performance using top 15% features, (d) performance using top 20% features.

Additional file 6. Fig. S2b: Optimization of random forest at various mtry and ntree values using dipeptide frequency and pepstats features as inputs, (a) performance using top 30% features (b) performance using top 50% features, (c) performance using top 70% features and (d) performance using top 90% features.

Additional file 1: Fig. S1. Flowchart of the study design. A. Merge of WES cohorts from five published studies (Hellman et al. [10], Rizvi et al. [11], Miao et al [12, 13], Allen et al. [14], Liu et al. [15]). B. MSKCC cohort from the published study (Samstein et al [16]). C. The TCGA dataset was used to perform DDR-related gene mutation, tumor-infiltrating immune cells and prognostic analyses.

Additional file 2: Fig. S2. The pan-cancer landscape of MED12 mutations across human tumors. The proportion of MED12 mutated tumors identified for each cancer type with alteration frequency in TCGA pan-cancer cohorts.

Additional file 3: Figure S1. (A, D, G) The heat map of 10 prognostic DEOSG expressions in the train (A), testing (D), and entire sets (G), respectively. (B, E, H) The comparison of 10 prognostic DEOSG expressions between high- and low-risk groups in the training (B), testing (E), and entire sets (H), respectively. (C, F, I) The correlations among 10 prognostic DEOSG between high- and low-risk groups in the training (C), testing (F), and entire...

The dysregulated circular RNAs (circRNAs) are linked to progression and chemoresistance in colorectal cancer (CRC). However, the role of circRNA protein tyrosine kinase 2 (circPTK2) in CRC progression and chemoresistance is uncertain. The circPTK2, microRNA (miR)-136-5p, m6A ‘reader’ protein YTH domain family protein 1 (YTHDF1), β-catenin and cyclin D1 abundances were examined via quantitative reverse transcription PCR or Western blotting. The progression was investigated by cell counting kit-8 (CCK-8), colony formation, transwell and xenograft analysis....

Triggering receptor expressed on myeloid cells 1 (TREM1) participates in the development of endometritis. This study aims at identifying the effects and interaction of TREM1 and upstream stimulatory factor 2 (USF2) in endometritis by using a model of lipopolysaccharide (LPS)-induced human endometrial epithelial cells (HEnEpCs). ELISA was performed to determine the levels of interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF-α) after LPS stimulation. TREM1 and USF2 expression was examined with RT-qPCR and Western blot....

Additional file 2: Fig. S2. The pan-cancer landscape of MED12 mutations across human tumors. The proportion of MED12 mutated tumors identified for each cancer type with alteration frequency in TCGA pan-cancer cohorts.

Additional file 1: Supplementary Fig. 1. Microscopic image of a representative tissue sample with original magnification.

Additional file 6: Figure S2. (A, F, K) Comparison of the relationship between the clinical characteristics of patients between high- and low-risk groups in the training, testing, and entire sets, respectively. (B–E) The scatter diagram showed the relationship between gender (B), clinical stage (C), T stage (D), N stage (E) and the risk score in the training set. (G–J) The scatter diagram showed the relationship between gender (G), clinical stage (E), T stage (F), N...

Additional file 3. Fig. S1: Mean decrease in accuracy of top 30 features selected through random forest algorithm.

Additional file 2: Supplementary Fig. 2. Significant difference in VAP-1 expression. There were significant differences in a) VAP-1 area % (NEC = 0,065 ± 0,035; controls = 0,11 ± 0,042, t(1,66) = 4.89, p < 0.001), b) VAP-1 mean (NEC = 80,74 ± 3,81; controls = 86,29 ± 6,06, t(1,66) = 4.65, p < 0.001), c) VAP-1 median (NEC = 76,44 ± 3,39; controls = 81,92 ± 6,09, t(1,66) = 4.77, p < 0.001), d)...