Background: This study sought to assess the prognostic factors for leiomyosarcoma (LMS) patients with lung metastasis and construct web-based nomograms to predict overall survival (OS) and cancer-specific survival (CSS). Method: Patients diagnosed with LMS combined with lung metastasis between 2010 and 2016 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly divided into a training set and a testing set. The X-tile analysis provides the best age and tumor size cut-off point, and changes continuous variables into categorical variables. The independent prognostic factors were determined by Cox regression analysis, and 2 nomograms were established. Receiver operating characteristic curves and calibration curves were used to evaluate the nomograms. Based on the nomograms, 2 web-based nomograms were established. Results: Two hundred and twenty-eight cases were included in the OS nomogram construction, and were randomly divided into a training set (n=160) and a validation set (n=68). Age, T stage, bone metastasis, surgery, chemotherapy, marital status, tumor size, and tumor site were found to be correlated with OS. One hundred and eighty-three cases were enrolled in the CSS nomogram construction, and randomly divided into a training set (n=129) and a validation set (n=54). Age, bone metastasis, surgery, chemotherapy, tumor size, and tumor site were found to be correlated with CSS. Two nomograms were established to predict OS and CSS. In the training set, the areas under the curve of the nomogram for predicting 1-, 2-, and 3-year OS were 0.783, 0.830, and 0.832, respectively, and those for predicting 1-, 2-, and 3-year CSS were 0.889, 0.777, and 0.884, respectively. Two web-based nomograms were established to predict OS (https://wenn23. shinyapps.io/lmslmosapp/), and CSS (https://wenn23.shinyapps.io/lmslmcssapp/). Conclusion:The developed web-based nomogram is a useful tool for accurately analyzing the prognosis of LMS patients with lung metastasis, and could help clinical doctors to make personalized clinical decisions.

Osteosarcoma (OS) is a malignant bone tumor of mesenchymal origin. Angelica dahurica is a typical traditional Chinese herb. Angelica dahurica is used in the treatment of a variety of tumors. However, the studies of Angelica dahurica for OS have not been reported. To investigate Angelica dahurica's potential mechanism of action in the treatment of OS, we used network pharmacology and molecular docking methods in this study. Of which the network pharmacology includes the collection of active ingredients of Angelica dahurica, the collection of predicted targets of Angelica dahurica and predicted targets of OS, the analysis of therapeutic targets of Angelica dahurica, gene ontology (GO) enrichment, and Kyoto encyclopedia of genes and genomes (KEGG) enrichment. The Venn plot performance showed that there were 225 predicted targets of Angelica dahurica for the treatment of OS. The therapeutic targets enrichment analysis results showed that Angelica dahurica treated OS through multiple targets and pathways. Angelica dahurica could affect OS's proliferation, apoptosis, migration, infiltration, and angiogenesis through a signaling network formed by pivotal genes crosstalking numerous signaling pathways. In addition, molecular docking results showed that senbyakangelicol, beta-sitosterol, and Prangenin, have a relatively high potential to become a treatment for patients with OS and improve 5-year survival in OS patients. We used network pharmacology and molecular docking methods to predict the active ingredients and significant targets of Angelica dahurica for the treatment of OS and, to a certain extent, elucidated the potential molecular mechanism of Angelica dahurica in the treatment of OS. This study provided a theoretical basis for Angelica dahurica in the treatment of OS. Abbreviations: BP = biological process, CC = cellular component, ESR1 = estrogen receptor, ETCM = The Encyclopedia of Traditional Chinese Medicine, GAPDH = glyceraldehyde-3-phosphate dehydrogenase, GO = gene ontology, IL1B = interleukin-1beta, IL6 = interleukin-6, INS = insulin, KEGG = Kyoto encyclopedia of genes and genomes, MF = molecular function, OS = osteosarcoma, PPARG = peroxisome proliferator-activated receptor gamma, KM = Kaplan-Meier, PPI = protein-protein interaction, PRKACA = cAMP-dependent protein kinase catalytic subunit alpha, RELA = transcription factor p65, RXRA = retinoic acid receptor RAR-alpha, TCMSP = the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, TNF = tumor necrosis factor.

Background: Studies have shown that the RNA N 6 -methyladenosine (m 6 A) modification patterns are extensively involved in the development of multiple tumors. However, the association between the m 6 A regulator expression patterns and the sarcoma tumor immune microenvironment (TIME) remains unclear. Methods: We systematically evaluated the m 6 A regulator expression patterns in patients with sarcoma based on known 23 m 6 A regulators. Different m 6 A regulator expression patterns were analyzed using gene set variation analysis and a single-sample gene set enrichment analysis algorithm. According to the results of consensus clustering, we classified the patients into four different clusters. Next, we subjected the four clusters to differential genetic analysis and established m 6 A-related differentially expressed genes (DEGs). We then calculated the m 6 A-related DEGs score and constructed the m 6 A-related gene signature, named m 6 A score. Finally, the 259 sarcoma samples were divided into high- and low-m 6 A score groups. We further evaluated the TIME landscape between the high- and low-m 6 A score groups. Results: We identified four different m 6 A modification clusters and found that each cluster had unique metabolic and immunological characteristics. Based on the 19 prognosis-related DEGs, we calculated the principal component analysis scores for each patient with sarcoma and classified them into high- and low-m 6 A score groups. Conclusions: The m 6 A regulator expression patterns and complexity of the sarcoma TIME landscape are closely related to each other. Systematic evaluation of m 6 A regulator expression patterns and m 6 A scores in patients with sarcoma will enhance our understanding of TIME characteristics.

Autophagy-related long non-coding RNAs (arlncRNAs) play a crucial role in the pathogenesis and development of the tumor. However, there is a lack of systematic analysis of arlncRNAs in melanoma patients. Melanoma data for analysis were obtained from The Cancer Genome Atlas (TCGA) database. By establishing a co-expression network of autophagy-related mRNAs-lncRNAs, we identified arlncRNAs in melanoma patients. We evaluated the prognostic value of arlncRNAs by univariate and multivariate Cox analysis and constructed an arlncRNAs risk model. Patients were divided into high- and low-risk groups based on the arlncRNAs risk score. This model was evaluated by Kaplan–Meier (K–M) analysis, univariate-multivariate Cox regression analysis, and receiver operating characteristic (ROC) curve analysis. Characteristics of autophagy genes and co-expressive tendency were analyzed by principal component analysis and Gene Set Enrichment Analysis (GSEA) functional annotation. Nine arlncRNAs (USP30-AS1, LINC00665, PCED1B-AS1, LINC00324, LINC01871, ZEB1-AS1, LINC01527, AC018553.1, and HLA-DQB1-AS1) were identified to be related to the prognosis of melanoma patients. Otherwise, the 9 arlncRNAs constituted an arlncRNAs prognostic risk model. K–M analysis and ROC curve analysis showed that the arlncRNAs risk model has good discrimination. Univariate and multivariate Cox regression analysis showed that arlncRNAs risk model was an independent prognostic factor in melanoma patients. Principal component analysis and GSEA functional annotation showed different autophagy and carcinogenic status in the high- and low-risk groups. This novel arlncRNAs risk model plays an essential role in predicting of the prognosis of melanoma patients. The model reveals new prognosis-related biomarkers for autophagy, promotes precision medicine, and provides a lurking target for melanoma's autophagy-related treatment.

Osteosarcoma (OS) is a malignant bone tumor of mesenchymal origin. Tripterygii Wilfordii (TW) is a traditional Chinese medicine widely used for its anti-inflammatory and immunomodulatory effects. Various components of TW have been shown to have antitumor effects, however, no systematic study has been conducted to prove the anti-OS effects of TW. This study aimed to investigate the effects of TW on OS and its mechanism based on network pharmacology and molecular docking. The web pharmacology section includes the gathering of the active components of TW, the collection of predicted targets of TW and OS-related targets, the analysis of therapeutic targets of TW, the enrichment of gene ontology (GO), and the enrichment of Kyoto Encyclopedia of Genes and Genomes (KEGG). The Veen diagram showed 451 targets for OS treatment in TW. The therapeutic target enrichment analysis results showed that TW treated OS via multiple targets and pathways. TW can affect OS proliferation, apoptosis, migration, infiltration, and angiogenesis through a signaling network formed by hub genes that cascade through numerous signaling pathways. In addition, molecular docking results showed that triptolide, kaempferol, and 5,8-Dihydroxy-7-(4-hydroxy-5-methyl-coumarin-3)-coumarin have relatively high potential to become drugs for patients with OS and improve the 5-year survival rate of patients with OS. Network pharmacology and molecular docking suggest that TW affects the biological behavior of OS through multiple pathways involving multiple targets, such as proliferation, apoptosis, migration, and infiltration. Upregulation of the cellular tumor antigen p53 (TP53) gene and downregulation of peroxisome proliferator-activated receptor gamma (PPARG) and signal transducer and activator of transcription 1-alpha/beta (STAT1) genes can prolong the survival time of patients with OS. Triptolide, kaempferol, and 5,8-Dihydroxy-7-(4-hydroxy-5 methyl-coumarin-3)-coumarin have a relatively high potential to become a treatment for patients with OS and improve 5-year survival of OS patients.