Network pharmacology-based research on the effect of Radix Astragali on osteosarcoma and the underlying mechanism.

To explore the anti-tumor effects of Radix Astragali on osteosarcoma and its mechanism. We analyzed the PPI network of Radix Astragali's potential targets for treating osteosarcoma and got the hub targets. We used KM curves to screen hub targets that could prolong sarcoma patients' survival time. We performed GO and KEGG enrichment analysis of Radix Astragali's potential targets and predicted Radix Astragali's molecular mechanism and function in treating osteosarcoma. The binding process between the hub targets, which could prolong sarcoma patients' survival time, and Radix Astragali was simulated through molecular docking. PPI network analysis of potential therapeutic targets discriminated 25 hub targets. The KM curves of the hub targets showed there were 13 hub targets that were effective in improving the 5-year survival rate of sarcoma patients. GO and KEGG enrichment demonstrated that Radix Astragali regulates multiple signaling pathways of osteosarcoma. Molecular docking results indicated that Radix Astragali could bind freely to the hub target, which could prolong the sarcoma patient's survival time. Radix Astragali act on osteosarcoma by regulating a signaling network formed by hub targets connecting multiple signaling pathways. Radix Astragali has the potential to become a drug for treating osteosarcoma and prolonging the sarcoma patient's survival time.

A web-based nomogram to predict overall survival for postresection leiomyosarcoma patients with lung metastasis.

To investigate the overall survival of post-resection leiomyosarcoma (LMS) patients with lung metastasis, data of post-resection LMS patients with lung metastasis between 2010 and 2016 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The clinical characteristics and survival data for post-resection LMS patients with lung metastasis at Tianjin Medical University Cancer Hospital & Institute (TJMUCH) between October 2010 and July 2018 were collected. Patients derived from the SEER database and TJMUCH were divided into training and validation cohorts, respectively. Univariate and multivariate Cox regression analyses were performed and a nomogram was established. The area under the curve (AUC) and the calibration curve were used to evaluate the nomogram. A web-based nomogram was developed based on the established nomogram. Eventually, 226 patients from the SEER database who were diagnosed with LMS and underwent primary lesion resection combined with lung metastasis were enrolled in the training cohort, and 17 patients from TJMUCH were enrolled in the validation cohort. Sex, race, grade, tumor size, chemotherapy, and bone metastasis were correlated with overall survival in patients with LMS. The C-index were 0.65 and 0.75 in the SEER and Chinese set, respectively. Furthermore, the applicable AUC values of the ROC curve in the SEER cohort to predict the 1-, 3-, 5- years survival rate were 0.646, 0.682, and 0.689, respectively. The corresponding AUC values in the Chinese cohort were 0.970, 0.913, and 0.881, respectively. The calibration curve showed that the nomogram performed well in predicting the overall survival in post-resection LMS patients with lung metastasis. A web-based nomogram (https://weijunqiang.shinyapps.io/survival_lms_lungmet/) was established. The web-based nomogram (https://weijunqiang.shinyapps.io/survival_lms_lungmet/) is an accurate and personalized tool for predicting the overall survival of post-resection LMS with lung metastasis.

Web-based nomogram to predict postresection risk of distant metastasis in patients with leiomyosarcoma: retrospective analysis of the SEER database and a Chinese cohort.

OBJECTIVES: This study investigated risk factors and constructed an online tool to predict distant metastasis (DM) risk in patients with leiomyosarcoma (LMS) after surgical resection. METHODS: Data regarding patients with LMS who underwent surgical resection between 2010 and 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Data were collected regarding patients with LMS who underwent surgical resection at Tianjin Medical University Cancer Hospital and Institute (TJMUCH) between October 2010 and July 2018. Patients were randomly divided into training and validation sets. Logistic regression analyses were performed; a nomogram was established. The area under the curve (AUC) and calibration curve were used to evaluate the nomogram, which served as the basis for a web-based nomogram. RESULTS: This study included 4461 and 76 patients from the SEER database and TJMUCH, respectively. Age, ethnicity, grade, T stage, N stage, radiotherapy, and chemotherapy were associated with DM incidence. C-index values were 0.815 and 0.782 in the SEER and Chinese datasets, respectively; corresponding AUC values were 0.814 and 0.773, respectively. A web-based nomogram (https://weijunqiang-leimyosarcoma-seer.shinyapps.io/dynnomapp/) was established. CONCLUSIONS: Our web-based nomogram is an accurate and user-friendly tool to predict DM risk in patients with LMS; it can aid clinical decision-making.

Network pharmacology-based research on the effect of angelicin on osteosarcoma and the underlying mechanism.

To explore the antitumor effects of angelicin on osteosarcoma and the underlying mechanism. We aimed to elucidate the mechanism by network pharmacology, molecular docking, and in vitro experiments. We analyzed a PPI network of potential angelicin targets in the treatment of osteosarcoma and identified hub targets. We systematically performed GO and KEGG enrichment analyses of the potential targets of angelicin, and we predicted it function in osteosarcoma treatment and the underlying molecular mechanism. Through molecular docking, the interactions between hub targets and angelicin were simulated, and then, the hub targets of angelicin were identified. Based on these results, we validated the effects of angelicin on osteosarcoma cells by conducting in vitro experiments. The PPI network analysis of potential therapeutic targets identified four apoptosis-related hub targets, namely, BCL-2, Casp9, BAX and BIRC 2. GO and KEGG enrichment analyses demonstrated that angelicin regulates osteosarcoma cell apoptosis. Molecular docking results indicated that angelicin can freely bind to the hub targets listed above. In vitro experiments showed that angelicin promoted osteosarcoma cell apoptosis in a dose-dependent manner and inhibited osteosarcoma cell migration and proliferation in a time- and dose-dependent manner. The RT-PCR results showed that angelicin simultaneously promoted the mRNA expression of Bcl-2 and Casp9 and inhibited the mRNA expression of BAX and BIRC 2. Angelicin promotes osteosarcoma cell apoptosis and inhibits osteosarcoma cell proliferation and migration by activating a signaling network that is composed of hub targets that link multiple signaling pathways. Angelicin could become an alternative drug for the treatment of osteosarcoma.

Metformin regulates chondrocyte senescence and proliferation through microRNA-34a/SIRT1 pathway in osteoarthritis.

BACKGROUND: Osteoarthritis (OA) is the most common degenerative disease in joints among elderly patients. Senescence is deeply involved in the pathogenesis of osteoarthritis. Metformin is widely used as the first-line drug for Type 2 diabetes mellitus (T2DM), and has great potential for the treatment of other aging-related disorders, including OA. However, the role of metformin in OA is not fully elucidated. Therefore, our aim here was to investigate the effects of metformin on human chondrocytes. METHODS: After metformin treatment, expression level of microRNA-34a and SIRT1 in chondrocyte were detected with quantitative real-time PCR and immunofluorescence staining. Then, microRNA-34a mimic and small interfering RNA (siRNA) against SIRT1 (siRNA-SIRT1) were transfected into chondrocyte. Senescence-associated ß-galactosidase (SA-ß-gal) staining was performed to assess chondrocyte senescence. Chondrocyte viability was illustrated with MTT and colony formation assays. Western blot was conducted to detect the expression of P16, IL-6, matrix metalloproteinase-13 (MMP-13), Collagen type II (COL2A1) and Aggrecan (ACAN). RESULTS: We found that metformin treatment (1 mM) inhibited microRNA-34a while promoted SIRT1 expression in OA chondrocytes. Both miR-34a mimics and siRNA against SIRT1 inhibited SIRT1 expression in chondrocytes. SA-ß-gal staining assay confirmed that metformin reduced SA-ß-gal-positive rate of chondrocytes, while transfection with miR-34a mimics or siRNA-SIRT1 reversed it. MTT assay and colony formation assay showed that metformin accelerated chondrocyte proliferation, while miR-34a mimics or siRNA-SIRT1 weakened this effect. Furthermore, results from western blot demonstrated that metformin suppressed expression of senescence-associated protein P16, proinflammatory cytokine IL-6 and catabolic gene MMP-13 while elevated expression of anabolic proteins such as Collagen type II and Aggrecan, which could be attenuated by transfection with miR-34a mimics. CONCLUSION: Overall, our data suggest that metformin regulates chondrocyte senescence and proliferation through microRNA-34a/SIRT1 pathway, indicating it could be a novel strategy for OA treatment.

Network pharmacology and molecular docking reveal the mechanism of Angelica dahurica against Osteosarcoma.

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 sen-byakangelicol, 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.

A single-cell analysis reveals tumor heterogeneity and immune environment of acral melanoma.

Acral melanoma is a dismal subtype of melanoma occurring in glabrous acral skin, and has a higher incidence in East Asians. We perform single-cell RNA sequencing for 63,394 cells obtained from 5 acral and 3 cutaneous melanoma samples to investigate tumor heterogeneity and immune environment. We define 5 orthogonal functional cell clusters that are involved in TGF-beta signaling, Type I interferon, Wnt signaling, Cell cycle, and Cholesterol efflux signaling. Signatures of enriched TGF-beta, Type I interferon, and cholesterol efflux signaling are significantly associated with good prognosis of melanoma. Compared with cutaneous melanoma, acral melanoma samples have significantly severe immunosuppressive state including depletion of cytotoxic CD8+ T cells, enrichment of Treg cells, and exhausted CD8+ T cells. PD1 and TIM-3 have higher expression in the exhaustive CD8+ T cells of acral melanoma. Key findings are verified in two independent validation sets. This study contributes to our better understanding of acral melanoma.

Identification of a clinical web-based nomogram to predict overall survival in elderly retroperitoneal sarcoma patients: A population-based study.

The purpose of this study was to develop a web-based nomogram and risk stratification system to predict overall survival (OS) in elderly patients with retroperitoneal sarcoma (RPS). Elderly patients diagnosed with RPS between 2004 and 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. We used univariate and multivariate Cox analysis to identify independent prognostic factors. We plotted the nomogram for predicting the OS of elderly RPS patients at 1, 3, and 5 years by integrating independent prognostic factors. The nomograms were subsequently validated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). By calculating the Nomogram score for each patient, we build a risk stratification model to evaluate the survival benefit of elderly RPS patients. A total of 722 elderly RPS patients were included in our study. The nomogram includes 5 clinicopathological variables as independent prognostic factors: age, histological subtype, grade, metastasis status, and surgery. Through the validation, we found that the nomogram has excellent prediction performance. Then web-based nomograms were established. We performed a web-based nomogram and a risk stratification model to assess the prognosis of elderly RPS patients, which are essential for prognostic clustering and decision-making about treatment.

A novel inflammatory signature for evaluating immune microenvironment status in soft tissue sarcoma.

Background: Tumorigenesis and progression are intimately associated with inflammation. However, the inflammatory landscape in soft tissue sarcoma (STS) and its clinical consequences are yet unknown, and more investigation is needed. Methods: RNA-seq expression data for STS and corresponding normal tissues were downloaded from The Cancer Genome Atlas database and the Genotype-Tissue Expression Portal. Differential and prognostic analyses were performed based on known inflammatory response genes from Gene Set Enrichment Analysis (GSEA). We utilized LASSO-Cox analysis to determine hub genes and built an inflammatory score (INFscore) and risk stratification model. Furthermore, a nomogram, including the risk stratification model, was established to predict the prognosis. We further elucidated the characteristics among different risk STS patients by GSEA, gene set variation analysis, and detailed immune infiltration analysis. Finally, the INFscore and risk stratification model in predicting prognosis and depicting immune microenvironment status were verified by pan-cancer analysis. Results: Five hub genes (HAS2, IL1R1, NMI, SERPINE1, and TACR1) were identified and were used to develop the INFscore. The risk stratification model distinguished the immune microenvironment status and evaluated the efficacy of immunotherapy and chemotherapy in STS. The novel nomogram had good efficacy in predicting the prognosis of STS patients. Finally, a pan-cancer investigation verified the association of INFscore with prognosis and immunity. Conclusions: According to the present study, the risk stratification model can be used to evaluate STS prognosis, tumor microenvironment status, immunotherapy, and chemotherapy efficacy. The novel nomogram has an excellent predictive value. Thus, the INFscore and risk stratification model has potential value in assessing the prognosis and immune status of multiple malignancies.

m6A regulator-mediated methylation modification patterns and tumor immune microenvironment in sarcoma.

BACKGROUND: Studies have shown that the RNA N6-methyladenosine (m6A) modification patterns are extensively involved in the development of multiple tumors. However, the association between the m6A regulator expression patterns and the sarcoma tumor immune microenvironment (TIME) remains unclear. METHODS: We systematically evaluated the m6A regulator expression patterns in patients with sarcoma based on known 23 m6A regulators. Different m6A 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 m6A-related differentially expressed genes (DEGs). We then calculated the m6A-related DEGs score and constructed the m6A-related gene signature, named m6A score. Finally, the 259 sarcoma samples were divided into high- and low-m6A score groups. We further evaluated the TIME landscape between the high- and low-m6A score groups. RESULTS: We identified four different m6A 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-m6A score groups. CONCLUSIONS: The m6A regulator expression patterns and complexity of the sarcoma TIME landscape are closely related to each other. Systematic evaluation of m6A regulator expression patterns and m6A scores in patients with sarcoma will enhance our understanding of TIME characteristics.

Immune-related lncRNA pairs as novel signature to predict prognosis and immune landscape in melanoma patients.

ABSTRACT: To investigate immune-related long non-coding RNA (irlncRNA) signatures for predicting survival and the immune landscape in melanoma patients.We retrieved gene expression files from The Cancer Genome Atlas and the Genotype-Tissue Expression database and extracted all the long non-coding RNAs from the original data. Then, we selected immune-related long non-coding RNAs (irlncRNAs) using co-expression networks and screened differentially expressed irlncRNAs (DEirlncRNAs) to form pairs. We also performed univariate analysis and Least absolute shrinkage and selection operator (LASSO) penalized regression analysis to identify prognostic DEirlncRNA pairs, constructed receiver operating characteristic curves, compared the areas under the curves, and calculated the optimal cut-off point to divide patients into high-risk and low-risk groups. Finally, we performed multivariate Cox regression analysis, Kaplan-Meier (K-M) survival analysis, clinical correlation analysis, and investigated correlations with tumor-infiltrating immune cells, chemotherapeutic effectiveness, and immunogene biomarkers.A total of 297 DEirlncRNAs were identified, of which 16 DEirlncRNA pairs were associated with prognosis in melanoma. After grouping patients by the optimal cut-off value, we could better distinguish melanoma patients with different survival outcomes, clinical characteristics, tumor immune status changes, chemotherapeutic drug sensitivity, and specific immunogene biomarkers.The DEirlncRNA pairs showed potential as novel biomarkers to predict the prognosis of melanoma patients. Furthermore, these DEirlncRNA pairs could be used to evaluate treatment efficacy in the future.

The new horizon of biomarker in melanoma patients: A study based on autophagy-related long non-coding RNA.

ABSTRACT: 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.

Development, validation, and visualization of a web-based nomogram for predicting the incidence of leiomyosarcoma patients with distant metastasis.

BACKGROUND: Leiomyosarcoma (LMS) is one of the most common soft tissue sarcomas. LMS is prone to distant metastasis (DM), and patients with DM have a poor prognosis. AIM: In this study, we investigated the risk factors of DM in LMS patients and the prognostic factors of LMS patients with DM. METHODS AND RESULTS: LMS patients diagnosed between 2010 and 2016 were extracted from the Surveillance, Epidemiology, and End Result (SEER) database. Patients were randomly divided into the training set and validation set. Univariate and multivariate logistic regression analyses were performed, and a nomogram was established. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram. Based on the nomogram, a web-based nomogram is established. The univariate and multivariate Cox regression analyses were used to assess the prognostic risk factors of LMS patients with DM. Eventually, 2184 patients diagnosed with LMS were enrolled, randomly divided into the training set (n = 1532, 70.14%) and validation set (n = 652, 29.86%). Race, primary site, grade, T stage, and tumor size were correlated with DM incidence in LMS patients. The AUC of the nomogram is 0.715 in training and 0.713 in the validation set. The calibration curve and DCA results showed that the nomogram performed well in predicting the DM risk. A web-based nomogram was established to predict DM's risk in LMS patients (https://wenn23.shinyapps.io/riskoflmsdm/). Epithelioid LMS, in uterus, older age, giant tumor, multiple organ metastasis, without surgery, and chemotherapy had a poor prognosis. CONCLUSIONS: The established web-based nomogram (https://wenn23.shinyapps.io/riskoflmsdm/) is an accurate and personalized tool to predict the risks of LMS developing DM. Advanced age, larger tumor, multiple organ metastasis, epithelioid type, uterine LMS, no surgery, and no chemotherapy were associated with poor prognosis in LMS patients with DM.

Construction, validation and, visualization of a web-based nomogram to identify the best candidates for primary tumor resection in advanced cutaneous melanoma patients.

Background: Existing studies have shown whether primary site resection (PSR) in cutaneous melanoma (CM) patients with stage IV is controversial. Our study aimed to identify the clinical characteristics of CM patients with stage IV who benefited from PSR on a population-based study. Methods: We retrospectively reviewed stage IV CM patients in the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015. Patients were divided into surgical and non-surgical groups according to whether PSR was performed or not. According to the median cancer-specific survival (CSS) time of the non-surgery group, the surgical group was divided into the surgery-benefit group and the non-surgery-benefit group. Multivariate cox regression analysis was used to explore independent CSS prognostic factors in the surgical group. Then, based on the independent prognostic factors of the surgical group, we established a web-based nomogram based on logistics regression. Results: A total of 574 stage IV CM patients were included in our study, and 491 (85.60%) patients were included in the surgical group. The clinical characteristics (benefit group and non-benefit group) included age, M stage, lesion location, and ulceration status. These independent prognostic factors were includeed to construct a web-based nomogram. Conclusions: We constructed a web-based nomogram. This model was suitable for identifying the best candidates suitable for PSR in stage IV CM patients.

Network pharmacology, molecular docking and bioinformatics reveal the mechanism of Tripterygii Wilfordii against Osteosarcoma.

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.

The diagnostic pitfalls of lumbar disc herniation---- malignant sciatic nerve tumour: two case reports and literature review.

BACKGROUND: Sciatica pain is a typical symptom of lumbar disc herniation (LDH), but some neurogenic and malignant tumours surrounding the sciatic nerve can also cause similar symptoms. These tumours are often misdiagnosed or even mistreated as LDH in clinical practice. CASE PRESENTATION: In our clinical practice, we found two patients with malignant tumours who were misdiagnosed with LDH. One patient complained of pain and numbness in the right lower limb. The primary diagnosis was LDH, and the patient underwent posterior lumbar interbody fusion surgery. After the operation, the symptoms were not alleviated. Then, diffuse large B-cell lymphoma involving the soft tissue and the sciatic nerve was identified. Another patient who manifested with radiating pain in the right lower limb was diagnosed with LDH at Chengde Central Hospital. He received regular conservative treatment for approximately 6 months, but his symptoms were not relieved, and then he was referred to our hospital. A malignant peripheral nerve sheath tumour (MPNST) of the sciatic nerve was diagnosed, and he received cisplatin (DDP) chemohyperthermia. CONCLUSIONS: Descriptions of tumour lesions involving the sciatic nerve and misdiagnosed as LDH in the literature are rare. In the reported literature, 7 patients were misdiagnosed with LDH, and all patients presented with sciatica. Among them, 4 patients only received surgical treatment, 1 patient only underwent neurolysis, and 2 patients received both surgical and chemotherapy treatment. Their low incidence and similar clinical manifestations to LDH make malignant tumours involving the sciatic nerve easy to misdiagnose. When the clinical symptoms and signs are inconsistent with the imaging findings, we need to be aware of non-discogenic sciatica, including tumours involving the sciatic nerve. Furthermore, tumours that grow near the exit of the sciatic notch may be misdiagnosed because of their deeper location and because they are covered with gluteal muscles. Sometimes sciatica caused by sciatic nerve tumours is only distal, without any radicular distribution. This pain is more severe than that caused by LDH, and this pain is not related to the position of the lumbar spine. Thus, it is beneficial to perform a detailed physical examination of the sciatic nerve to avoid this kind of misdiagnosis.

A predictive web-based nomogram for the early death of patients with lung adenocarcinoma and bone metastasis: a population-based study.

OBJECTIVE: To identify risk factors and develop predictive web-based nomograms for the early death of patients with bone metastasis of lung adenocarcinoma (LUAD). METHODS: Patients in the Surveillance, Epidemiology, and End Results database diagnosed with bone metastasis of LUAD between 2010 and 2016 were included and randomly divided into training and validation sets. Early death-related risk factors (survival time ≤7 months) were evaluated by logistic regression. Two predictive nomograms were established and validated by calibration curves, receiver operating characteristic curves, and decision curve analysis. RESULTS: A total of 9189 patients (56.59%) died from all causes within 7 months of being diagnosed, including 8585 patients (56.67%) who died from cancer-specific causes. Age >65 years, sex (men), T stage (T3 and T4), N stage (N2 and N3), brain metastasis, and liver metastasis were risk factors for all-cause and cancer-specific early death. The area under the curves of the nomograms for all-cause and cancer-specific early death prediction were 0.754 and 0.753 (training set) and 0.747 and 0.754 (validation set), respectively. Further analysis showed that the two nomograms performed well. CONCLUSIONS: Our two web-based nomograms for all-cause and cancer-specific early death provide valuable tools for predicting early death in these patients.

Construction, validation and, visualization of a web-based nomogram for predicting the overall survival and cancer-specific survival of leiomyosarcoma patients with lung metastasis.

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.

The new horizon of liquid biopsy in sarcoma: the potential utility of circulating tumor nucleic acids.

The diagnosis, treatment and prognosis of sarcoma are mainly dependent on tissue biopsy, which is limited in its ability to provide a panoramic view into the dynamics of tumor progression. In addition, effective biomarkers to monitor the progression and therapeutic response of sarcoma are lacking. Liquid biopsy, a recent technological breakthrough, has gained great attention in the last few decades. Nucleic acids (such as DNA, mRNAs, microRNAs, and long non-coding RNAs) that are released from tumors circulate in the blood of cancer patients and can be evaluated through liquid biopsy. Circulating tumor nucleic acids reflect the intertumoral and intratumoral heterogeneity, and thus liquid biopsy provides a noninvasive strategy to examine these molecules compared with traditional tissue biopsy. Over the past decade, a great deal of information on the potential utilization of circulating tumor nucleic acids in sarcoma screening, prognosis and therapy efficacy monitoring has emerged. Several specific gene mutations in sarcoma can be detected in peripheral blood samples from patients and can be found in circulating tumor DNA to monitor sarcoma. In addition, circulating tumor non-coding RNA may also be a promising biomarker in sarcoma. In this review, we discuss the clinical application of circulating tumor nucleic acids as blood-borne biomarkers in sarcoma.