Characterizing player's playing styles based on player vectors for each playing position in the Chinese Football Super League.

Characterizing playing style is important for football clubs on scouting, monitoring and match preparation. Previous studies considered a player's style as a combination of technical performances, failing to consider the spatial information. Therefore, this study aimed to characterize the playing styles of each playing position in the Chinese Football Super League (CSL) matches, integrating a recently adopted Player Vectors framework. Data of 960 matches from 2016-2019 CSL were used. Match ratings, and 10 types of match events with the corresponding coordinates for all the line-up players whose on-pitch time exceeded 45 minutes were extracted. Players were first clustered into eight positions. A player vector was constructed for each player in each match based on the Player Vectors using Nonnegative Matrix Factorization (NMF). Another NMF process was run on the player vectors to extract different types of playing styles. The resulting player vectors discovered 18 different playing styles in the CSL. Six performance indicators of each style were investigated to observe their contributions. In general, the playing styles of forwards and midfielders are in line with football performance evolution trends, while the styles of defenders should be reconsidered. Multifunctional playing styles were also found in high-rated CSL players.

Immune-Associated Gene Signatures Serve as a Promising Biomarker of Immunotherapeutic Prognosis for Renal Clear Cell Carcinoma.

As the most common type of renal cell carcinoma (RCC), the renal clear cell carcinoma (ccRCC) is highly malignant and insensitive to chemotherapy or radiotherapy. Although systemic immunotherapies have been successfully applied to ccRCC in recent years, screening for patients who can benefit most from these therapies is still essential and challenging due to immunological heterogeneity of ccRCC patients. To this end, we implemented a series of deep investigation on the expression and clinic data of ccRCC from The Cancer Genome Atlas (TCGA) International Consortium for Cancer Genomics (ICGC). We identified a total of 946 immune-related genes that were differentially expressed. Among them, five independent genes, including SHC1, WNT5A, NRP1, TGFA, and IL4R, were significantly associated with survival and used to construct the immune-related prognostic differential gene signature (IRPDGs). Then the ccRCC patients were categorized into high-risk and low-risk subgroups based on the median risk score of the IRPDGs. IRPDGs subgroups displays distinct genomic and immunological characteristics. Known immunotherapy-related genes show different mutation burden, wherein the mutation rate of VHL was higher than 40% in the two IRPDGs subgroups, and SETD2 and BAP1 mutations differed most between two groups with higher frequency in the high-risk subgroup. Moreover, IRPDGs subgroups had different abundance in tumor-infiltrating immune cells (TIICs) with distinct immunotherapy efficacy. Plasma cells, regulatory cells (Tregs), follicular helper T cells (Tfh), and M0 macrophages were enriched in the high-risk group with a higher tumor immune dysfunction and rejection (TIDE) score. In contrast, the low-risk group had abundant M1 macrophages, mast cell resting and dendritic cell resting infiltrates with lower TIDE score and benefited more from immune checkpoint inhibitors (ICI) treatment. Compared with other biomarkers, such as TIDE and tumor inflammatory signatures (TIS), IRPDGs demonstrated to be a better biomarker for assessing the prognosis of ccRCC and the efficacy of ICI treatment with the promise in screening precise patients for specific immunotherapies.

A Novel Necroptosis-Related lncRNA Signature Predicts the Prognosis of Lung Adenocarcinoma.

Background: Necroptosis is closely related to the tumorigenesis and development of cancer. An increasing number of studies have demonstrated that targeting necroptosis could be a novel treatment strategy for cancer. However, the predictive potential of necroptosis-related long noncoding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) still needs to be clarified. This study aimed to construct a prognostic signature based on necroptosis-related lncRNAs to predict the prognosis of LUAD. Methods: We downloaded RNA sequencing data from The Cancer Genome Atlas database. Co-expression network analysis, univariate Cox regression, and least absolute shrinkage and selection operator were adopted to identify necroptosis-related prognostic lncRNAs. We constructed the predictive signature by multivariate Cox regression. Kaplan-Meier analysis, time-dependent receiver operating characteristics, nomogram, and calibration curves were used to validate and evaluate the signature. Subsequently, we used gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) to explore the relationship between the predictive signature and tumor immune microenvironment of risk groups. Finally, the correlation between the predictive signature and immune checkpoint expression of LUAD patients was also analyzed. Results: We constructed a signature composed of 7 necroptosis-related lncRNAs (AC026355.2, AC099850.3, AF131215.5, UST-AS2, ARHGAP26-AS1, FAM83A-AS1, and AC010999.2). The signature could serve as an independent predictor for LUAD patients. Compared with clinicopathological variables, the necroptosis-related lncRNA signature has a higher diagnostic efficiency, with the area under the receiver operating characteristic curve being 0.723. Meanwhile, when patients were stratified according to different clinicopathological variables, the overall survival of patients in the high-risk group was shorter than that of those in the low-risk group. GSEA showed that tumor- and immune-related pathways were mainly enriched in the low-risk group. ssGSEA further confirmed that the predictive signature was significantly related to the immune status of LUAD patients. The immune checkpoint analysis displayed that low-risk patients had a higher immune checkpoint expression, such as CTLA-4, HAVCR2, PD-1, and TIGIT. This suggested that immunological function is more active in the low-risk group LUAD patients who might benefit from checkpoint blockade immunotherapies. Conclusion: The predictive signature can independently predict the prognosis of LUAD, helps elucidate the mechanism of necroptosis-related lncRNAs in LUAD, and provides immunotherapy guidance for patients with LUAD.

A Novel Immune Gene-Related Prognostic Score Predicts Survival and Immunotherapy Response in Glioma.

Background and Objectives: The clinical prognosis and survival prediction of glioma based on gene signatures derived from heterogeneous tumor cells are unsatisfactory. This study aimed to construct an immune gene-related prognostic score model to predict the prognosis of glioma and identify patients who may benefit from immunotherapy. Methods: 23 immune-related genes (IRGs) associated with glioma prognosis were identified through weighted gene co-expression network analysis (WGCNA) and Univariate Cox regression analysis based on large-scale RNA-seq data. Eight IRGs were retained as candidate predictors and formed an immune gene-related prognostic score (IGRPS) by multifactorial Cox regression analysis. The potential efficacy of immune checkpoint blockade (ICB) therapy of different subgroups was compared by The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. We further adopted a series of bioinformatic methods to characterize the differences in clinicopathological features and the immune microenvironment between the different risk groups. Finally, a nomogram integrating IGRPS and clinicopathological characteristics was built to accurately predict the prognosis of glioma. Results: Patients in the low-risk group had a better prognosis than those in the high-risk group. Patients in the high-risk group showed higher TIDE scores and poorer responses to ICB therapy, while patients in the low-risk group may benefit more from ICB therapy. The distribution of age and tumor grade between the two subgroups was significantly different. Patients with low IGRPS harbor a high proportion of natural killer cells and are sensitive to ICB treatment. While patients with high IGRPS display relatively poor prognosis, a higher expression level of DNA mismatch repair genes, high infiltrating of immunosuppressive cells, and poor ICB therapeutic outcomes. Conclusions: We demonstrated that the IGRPS model can independently predict the clinical prognosis as well as the ICB therapy responses of glioma patients, thus having important implications on the design of immune-based therapeutic strategies.

An Apoptosis-Related Gene Prognostic Index for Colon Cancer.

Purpose: To construct an apoptosis-related gene prognostic index (ARGPI) for colon cancer, and clarify the molecular and immune characteristics of the risk subgroup as defined by the prognostic index and the benefits of adjuvant chemotherapy. Integrating the prognostic index and clinicopathological risk factors to better evaluate the prognosis of patients with colon cancer. Methods: Based on the colon adenocarcinoma data in the TCGA database, 20 apoptosis-related hub genes were screened by weighted gene co-expression network analysis (WGCNA). Five genes constituting the prognosis model were determined by Cox regression and verified by the Gene Expression Omnibus (GEO) dataset. Then the molecular and immune characteristics of risk subgroups defined by the prognostic index and the benefits of adjuvant chemotherapy were analyzed. Finally, nomograms integrating ARGPI and four clinicopathological risk factors were used to evaluate the prognosis of patients with colon cancer. Results: The ARGPI was constructed based on the FAS, VWA5A, SPTBN2, PCK1, and TIMP1 genes. In the TCGA cohort, patients in the low-risk subgroup had a longer progression-free interval (PFI) than patients in the high-risk subgroup, which coincided with the results of the GEO cohort. The comprehensive results showed that the high-risk score was related to the enrichment of the cell cycle pathway, high mutation rate of TP53 and KRAS, high infiltration of T regulatory cells (Tregs), immunosuppressive state, and less chemotherapeutic benefit. However, low-risk scores are related to drug metabolism-related pathways, low TP53 and KRAS mutation rates, high infiltration of plasma cells, more resting CD4 memory cells and eosinophils, active immune function, and better chemotherapeutic benefits. Receiver operating characteristic curve of two-year progress prediction evaluation showed that the ARGPI had higher prognostic accuracy than TNM staging. Nomograms integrating ARGPI and clinicopathological risk factors can better evaluate the prognosis of patients with colon cancer. Conclusions: The ARGPI is a promising biomarker for determining risk of colon cancer progression, molecular and immune characteristics, and chemotherapeutic benefit. This is a reliable method to predict the prognosis of colon cancer patients. It also can assist doctors in formulating more effective treatment strategies.