One Stone, Two Birds: A Peptide-Au(I) Infinite Coordination Supermolecule for the Confederate Physical and Biological Radiosensitization in Cancer Radiation Therapy.

Over half of cancer patients are subjected to radiotherapy, but owing to the deficient amount of reactive oxygen radicals (ROS) and DNA double-strand breaks (DSBs), a fair number of them suffer from radiotherapy resistance and the subsequent short-term survival opportunity. To overcome it, many successes have been achieved in radiosensitizer discovery using physical strategy and/or biological strategy, but significant challenges remain regarding developing clinically translational radiosensitizers. Herein, a peptide-Au(I) infinite coordination supermolecule termed PAICS is developed that combined both physical and biological radiosensitization and possessed pharmaceutical characteristics including adequate circulatory stability, controllable drug release, tumor-prioritized accumulation, and the favorable body eliminability. As expected, monovalent gold ion endowed this supermolecule with high X-ray absorption and the subsequent radiosensitization. Furthermore, a peptide targeting CRM1, is assembled into the supermolecule, which successfully activates p53 and apoptosis pathway, thereby further sensitizing radiotherapy. As a result, PAICS showed superior ability for radiotherapy sensitization in vivo and maintained a favorable safety profile. Thus, the PAICS reported here will offer a feasible solution to simultaneously overcome both the pharmaceutical obstacles of physical and biological radiosensitizers and will enable the development of a class of nanomedicines for tumor radiotherapy sensitization.

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 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.

Integrative, genome-wide association study identifies chemicals associated with common women's malignancies.

BACKGROUND: Breast cancer, cervical cancer, and ovarian cancer are three of the most commonly diagnosed malignancies in women, and more cancer prevention research is urgently needed. METHODS: Summary data of a large genome-wide association study of female cancers were derived from the UK biobank. We performed a transcriptome-wide association study and a gene set enrichment analysis to identify correlations between chemical exposure and aberrant expression, repression, or mutation of genes related to cancer using the Comparative Toxicogenomics Database. RESULTS: We identified five chemicals (NSC668394, glafenine, methylnitronitrosoguanidine, fenofibrate, and methylparaben) that were associated with the incidence of both breast cancer and cervical cancer. CONCLUSION: Using a transcriptome-wide association study and gene set enrichment analysis we identified environmental chemicals that are associated with an increased risk of breast cancer, cervical cancer, and ovarian cancer.

Tamoxifen induces fatty liver disease in breast cancer through the MAPK8/FoxO pathway.

BACKGROUND: Prevention of metabolic complications of long-term adjuvant endocrine therapy in breast cancers remained a challenge. We aimed to investigate the molecular mechanism in the development of tamoxifen (TAM)-induced fatty liver in both estrogen receptor (ER)-positive and ER-negative breast cancer. METHODS AND RESULTS: First, the direct protein targets (DPTs) of TAM were identified using DrugBank5.1.7. We found that mitogen-activated protein kinase 8 (MAPK8) was one DPT of TAM. We identified significant genes in breast cancer and fatty liver disease (FLD) using the MalaCards human disease database. Next, we analyzed the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of those significant genes in breast cancer and FLD using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). We found that overlapping KEGG pathways in these two diseases were MAPK signaling pathway, Forkhead box O (FoxO) signaling pathway, HIF-1 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and PI3K-Akt signaling pathway. Furthermore, the KEGG Mapper showed that the MAPK signaling pathway was related to the FoxO signaling pathway. Finally, the functional relevance of breast cancer and TAM-induced FLD was validated by Western blot analysis. We verified that TAM may induce fatty liver in breast cancer through the MAPK8/FoxO signaling pathway. CONCLUSION: Bioinformatics analysis combined with conventional experiments may improve our understanding of the molecular mechanisms underlying side effects of cancer drugs, thereby making this method a new paradigm for guiding future studies on this issue.

Integrating Genome-Wide Association Studies and Gene Expression Profiles With Chemical-Genes Interaction Networks to Identify Chemicals Associated With Colorectal Cancer.

Colorectal cancer (CRC) is the third most common cancer and has the second highest mortality rate in global cancer. Exploring the associations between chemicals and CRC has great significance in prophylaxis and therapy of tumor diseases. This study aims to explore the relationships between CRC and environmental chemicals on genetic basis by bioinformatics analysis. The genome-wide association study (GWAS) datasets for CRC were obtained from the UK Biobank. The GWAS data for colon cancer (category C18) includes 2,581 individuals and 449,683 controls, while that of rectal cancer (category C20) includes 1,244 individuals and 451,020 controls. In addition, we derived CRC gene expression datasets from the NCBI-GEO (GSE106582). The chemicals related gene sets were acquired from the comparative toxicogenomics database (CTD). Transcriptome-wide association study (TWAS) analysis was applied to CRC GWAS summary data and calculated the expression association testing statistics by FUSION software. We performed chemicals related gene set enrichment analysis (GSEA) by integrating GWAS summary data, mRNA expression profiles of CRC and the CTD chemical-gene interaction networks to identify relationships between chemicals and genes of CRC. We observed several significant correlations between chemicals and CRC. Meanwhile, we also detected 5 common chemicals between colon and rectal cancer, including methylnitronitrosoguanidine, isoniazid, PD 0325901, sulindac sulfide, and importazole. Our study performed TWAS and GSEA analysis, linked prior knowledge to newly generated data and thereby helped identifying chemicals related to tumor genes, which provides new clues for revealing the associations between environmental chemicals and cancer.