MOESM5 of Exploration of methylation-driven genes for monitoring and prognosis of patients with lung adenocarcinoma

Additional file 5. All relevant methylated sites of the five methylation-driven genes obtained from the TCGA database. (1–16) methylated sites of the gene CCDC181; (17–33) methylated sites of the gene ELF3; (34–47) methylated sites of the gene KLHDC9; (48) methylated site of the gene PLAU; (49–57) methylated sites of the gene S1PR1

Supplementary Tables. Analysis of the expression patterns and clinical relevance of m6A regulators in 33 cancer types

Supplementary Table 1. Analysis of the expression patterns and clinical relevance of m6A regulators in 33 cancer types Correspondence between 20 M6A RNA methylation regulatory factors and 18 cancers in differential expression-related (A) P value and (B) Log FC (fold change) Supplementary Table 2. Analysis of the expression patterns and clinical relevance of m6A regulators in 33 cancer types COX analysis between the expression levels of 20 M6A RNA methylation regulatory factors and the prognosis of different cancers Supplementary Table 3. Analysis of the expression patterns and clinical relevance of m6A regulators in 33 cancer types Differences of 20 M6A RNA methylation regulatory factors among different immune subtypes of pan-cancer Supplementary Table 4. Analysis of the expression patterns and clinical relevance of m6A regulators in 33 cancer types The difference between 20 M6A RNA methylation regulatory factors in different immune subtypes and different stages of breast cancer Supplementary Table 5. Analysis of the expression patterns and clinical relevance of m6A regulators in 33 cancer types The correlation between 20 regulatory factors and the prognosis of different cancers. </div

Additional file 1: of Deciphering the mechanism of Indirubin and its derivatives in the inhibition of Imatinib resistance using a “drug target prediction-gene microarray analysis-protein network construction” strategy

Figure S1. Heat maps of differentially expressed genes associated with imatinib resistance (we selected 100 genes with the most significant differential expression) (P < 0.05). The color from blue to red shows a trend from low to high expression. (JPG 298 kb

Image_1_7-lncRNA Assessment Model for Monitoring and Prognosis of Breast Cancer Patients: Based on Cox Regression and Co-expression Analysis.TIF

Background: Breast cancer is one of the deadliest malignant tumors worldwide. Due to its complex molecular and cellular heterogeneity, the efficacy of existing breast cancer risk prediction models is unsatisfactory. In this study, we developed a new lncRNA model to predict the prognosis of patients with BRCA.Methods: BRCA-related differentially-expressed long non-coding RNA were screened from the Cancer Genome Atlas database. A novel lncRNA model was developed by univariate and multivariate analyses to predict the prognosis of patients with BRCA. The efficacy of the model was verified by TCGA-based breast cancer samples. Identified lncRNA-related mRNA based on the co-expression method.Results: We constructed a 7-lncRNA breast cancer prediction model including LINC00377, LINC00536, LINC01224, LINC00668, LINC01234, LINC02037, and LINC01456. The breast cancer samples were divided into high-risk and low-risk groups based on the model, which verified the specificity and sensitivity of the model. The Area Under Curve (AUC) of the 3- and 5-year Receiver Operating Characteristic curve were 0.711 and 0.734, respectively, indicating that the model has good performance.Conclusion: We constructed a 7-lncRNA model to predict the prognosis of patients with BRCA, and suggest that these lncRNAs may play a specific role in the carcinogenesis of BRCA.</p

Table_1_From tumor mutational burden to characteristic targets analysis: Identifying the predictive biomarkers and natural product interventions in cancer management.DOCX

High-throughput next-generation sequencing (NGS) provides insights into genome-wide mutations and can be used to identify biomarkers for the prediction of immune and targeted responses. A deeper understanding of the molecular biological significance of genetic variation and effective interventions is required and ultimately needs to be associated with clinical benefits. We conducted a retrospective observational study of patients in two cancer cohorts who underwent NGS in a “real-world” setting. The association between differences in tumor mutational burden (TMB) and clinical presentation was evaluated. We aimed to identify several key mutation targets and describe their biological characteristics and potential clinical value. A pan-cancer dataset was downloaded as a verification set for further analysis and summary. Natural product screening for the targeted intervention of key markers was also achieved. The majority of tumor patients were younger adult males with advanced cancer. The gene identified with the highest mutation rate was TP53, followed by PIK3CA, EGFR, and LRP1B. The association of TMB (0–103.7 muts/Mb) with various clinical subgroups was determined. More frequent mutations, such as in LRP1B, as well as higher levels of ferritin and neuron-specific enolase, led to higher TMB levels. Further analysis of the key targets, LRP1B and APC, was performed, and mutations in LRP1B led to better immune benefits compared to APC. APC, one of the most frequently mutated genes in gastrointestinal tumors, was further investigated, and the potential interventions by cochinchinone B and rottlerin were clarified. In summary, based on the analysis of the characteristics of gene mutations in the “real world,” we obtained the potential association indicators of TMB, found the key signatures LRP1B and APC, and further described their biological significance and potential interventions.</p

Table_7_From tumor mutational burden to characteristic targets analysis: Identifying the predictive biomarkers and natural product interventions in cancer management.DOCX

High-throughput next-generation sequencing (NGS) provides insights into genome-wide mutations and can be used to identify biomarkers for the prediction of immune and targeted responses. A deeper understanding of the molecular biological significance of genetic variation and effective interventions is required and ultimately needs to be associated with clinical benefits. We conducted a retrospective observational study of patients in two cancer cohorts who underwent NGS in a “real-world” setting. The association between differences in tumor mutational burden (TMB) and clinical presentation was evaluated. We aimed to identify several key mutation targets and describe their biological characteristics and potential clinical value. A pan-cancer dataset was downloaded as a verification set for further analysis and summary. Natural product screening for the targeted intervention of key markers was also achieved. The majority of tumor patients were younger adult males with advanced cancer. The gene identified with the highest mutation rate was TP53, followed by PIK3CA, EGFR, and LRP1B. The association of TMB (0–103.7 muts/Mb) with various clinical subgroups was determined. More frequent mutations, such as in LRP1B, as well as higher levels of ferritin and neuron-specific enolase, led to higher TMB levels. Further analysis of the key targets, LRP1B and APC, was performed, and mutations in LRP1B led to better immune benefits compared to APC. APC, one of the most frequently mutated genes in gastrointestinal tumors, was further investigated, and the potential interventions by cochinchinone B and rottlerin were clarified. In summary, based on the analysis of the characteristics of gene mutations in the “real world,” we obtained the potential association indicators of TMB, found the key signatures LRP1B and APC, and further described their biological significance and potential interventions.</p

Image_6_From tumor mutational burden to characteristic targets analysis: Identifying the predictive biomarkers and natural product interventions in cancer management.JPEG

High-throughput next-generation sequencing (NGS) provides insights into genome-wide mutations and can be used to identify biomarkers for the prediction of immune and targeted responses. A deeper understanding of the molecular biological significance of genetic variation and effective interventions is required and ultimately needs to be associated with clinical benefits. We conducted a retrospective observational study of patients in two cancer cohorts who underwent NGS in a “real-world” setting. The association between differences in tumor mutational burden (TMB) and clinical presentation was evaluated. We aimed to identify several key mutation targets and describe their biological characteristics and potential clinical value. A pan-cancer dataset was downloaded as a verification set for further analysis and summary. Natural product screening for the targeted intervention of key markers was also achieved. The majority of tumor patients were younger adult males with advanced cancer. The gene identified with the highest mutation rate was TP53, followed by PIK3CA, EGFR, and LRP1B. The association of TMB (0–103.7 muts/Mb) with various clinical subgroups was determined. More frequent mutations, such as in LRP1B, as well as higher levels of ferritin and neuron-specific enolase, led to higher TMB levels. Further analysis of the key targets, LRP1B and APC, was performed, and mutations in LRP1B led to better immune benefits compared to APC. APC, one of the most frequently mutated genes in gastrointestinal tumors, was further investigated, and the potential interventions by cochinchinone B and rottlerin were clarified. In summary, based on the analysis of the characteristics of gene mutations in the “real world,” we obtained the potential association indicators of TMB, found the key signatures LRP1B and APC, and further described their biological significance and potential interventions.</p

Image_1_Identifying the Antiproliferative Effect of Astragalus Polysaccharides on Breast Cancer: Coupling Network Pharmacology With Targetable Screening From the Cancer Genome Atlas.TIF

Background:Astragalus polysaccharides (APS), natural plant compounds, have recently emerged as a promising strategy for cancer treatment, but little is known concerning their effects on breast cancer (BC) tumorigenesis.Methods: We obtained breast cancer genetic data from The Cancer Genome Atlas (TCGA) database, network pharmacology to further clarify its biological properties. Survival analysis and molecular docking techniques were implemented for the final screening to obtain key target information. Our experiments focused on the detection of intervention effects of APS on BC cells (MCF-7 and MDA-MB-231), and quantitative RT-PCR (qRT-PCR) was used to assess the expression of key targets.Results: A total of 1,439 differentially expressed genes (DEGs) were identified by TCGA and used to build disease networks. Module analysis, gene ontology and pathway analysis revealed characteristic of the DEGs network. Topological properties were used to identify key targets, survival analysis and molecular docking finally found that the targets of APS regulation of BC cells may be CCNB1, CDC6, and p53. Through cell viability, migration and invasion assays, we found that APS interferes with the development of breast cancer in MCF7 and MDA-MB-231 cells in a dose-dependent manner. Furthermore, qRT-PCR verification suggested that the expression of CCNB1 and CDC6 in breast cancer cells was significantly downregulated in response to APS, while expression of the tumor suppressor gene P53 was significantly increased.Conclusion: Results of this study suggest therapeutic potential for APS in BC treatment, possibly through interventions with CCNB1, CDC6, and P53. Furthermore, these findings illustrate the feasibility of using network pharmacology to connect large-scale target data as a way to discover the mechanism of natural products interfering with disease.</p

Table_2_From tumor mutational burden to characteristic targets analysis: Identifying the predictive biomarkers and natural product interventions in cancer management.DOC

High-throughput next-generation sequencing (NGS) provides insights into genome-wide mutations and can be used to identify biomarkers for the prediction of immune and targeted responses. A deeper understanding of the molecular biological significance of genetic variation and effective interventions is required and ultimately needs to be associated with clinical benefits. We conducted a retrospective observational study of patients in two cancer cohorts who underwent NGS in a “real-world” setting. The association between differences in tumor mutational burden (TMB) and clinical presentation was evaluated. We aimed to identify several key mutation targets and describe their biological characteristics and potential clinical value. A pan-cancer dataset was downloaded as a verification set for further analysis and summary. Natural product screening for the targeted intervention of key markers was also achieved. The majority of tumor patients were younger adult males with advanced cancer. The gene identified with the highest mutation rate was TP53, followed by PIK3CA, EGFR, and LRP1B. The association of TMB (0–103.7 muts/Mb) with various clinical subgroups was determined. More frequent mutations, such as in LRP1B, as well as higher levels of ferritin and neuron-specific enolase, led to higher TMB levels. Further analysis of the key targets, LRP1B and APC, was performed, and mutations in LRP1B led to better immune benefits compared to APC. APC, one of the most frequently mutated genes in gastrointestinal tumors, was further investigated, and the potential interventions by cochinchinone B and rottlerin were clarified. In summary, based on the analysis of the characteristics of gene mutations in the “real world,” we obtained the potential association indicators of TMB, found the key signatures LRP1B and APC, and further described their biological significance and potential interventions.</p

Table_4_From tumor mutational burden to characteristic targets analysis: Identifying the predictive biomarkers and natural product interventions in cancer management.DOCX

High-throughput next-generation sequencing (NGS) provides insights into genome-wide mutations and can be used to identify biomarkers for the prediction of immune and targeted responses. A deeper understanding of the molecular biological significance of genetic variation and effective interventions is required and ultimately needs to be associated with clinical benefits. We conducted a retrospective observational study of patients in two cancer cohorts who underwent NGS in a “real-world” setting. The association between differences in tumor mutational burden (TMB) and clinical presentation was evaluated. We aimed to identify several key mutation targets and describe their biological characteristics and potential clinical value. A pan-cancer dataset was downloaded as a verification set for further analysis and summary. Natural product screening for the targeted intervention of key markers was also achieved. The majority of tumor patients were younger adult males with advanced cancer. The gene identified with the highest mutation rate was TP53, followed by PIK3CA, EGFR, and LRP1B. The association of TMB (0–103.7 muts/Mb) with various clinical subgroups was determined. More frequent mutations, such as in LRP1B, as well as higher levels of ferritin and neuron-specific enolase, led to higher TMB levels. Further analysis of the key targets, LRP1B and APC, was performed, and mutations in LRP1B led to better immune benefits compared to APC. APC, one of the most frequently mutated genes in gastrointestinal tumors, was further investigated, and the potential interventions by cochinchinone B and rottlerin were clarified. In summary, based on the analysis of the characteristics of gene mutations in the “real world,” we obtained the potential association indicators of TMB, found the key signatures LRP1B and APC, and further described their biological significance and potential interventions.</p