Background: Cancer is the second cause of death worldwide. Copperoptosis is a new mode of regulated cell death and is strongly associated with metabolic pathways. FDX1 is a key gene that promotes copperoptosis, and its impact on tumor pathogenesis and tumor immune response is indistinct and needs further exploration.Methods: Data was mined from the Cancer Genome Atlas database, the Broad Institute Cancer Cell Line Encyclopedia database, and the International Cancer Genome Consortium. Survival analyses included the Kaplan–Meier method for calculating the cumulative incidence of survival events and the log-rank method for comparing survival curves between groups. Immune cell infiltration levels were calculated using the Spearman correlation test and correlated with FDX1 expression to assess significance. More correlation analyses between FDX1 expression and mutational markers, such as tumor mutational burden (TMB) and microsatellite instability (MSI), were also examined via Spearman assay to explore the relation between FDX1 expression and the sensitivity of common antitumor drugs.Results: FDX1 expression was downregulated in most kinds of cancers, and this high expression indicated better overall survival and death-specific survival. For several cancer types, FDX1 expression had a positive correlation with immune cell infiltration, and FDX1 also had a positive correlation with TMB and MSI in some cancer types, linking its expression to the assessment of possible treatment responses.Conclusion: The correlations between FDX1 expression and cancer in varioustissues, including clear links to cancer survival and prognosis, make FDX1 aninteresting biomarker and potential therapeutic target for cancer surveillance and futureresearch.

Purpose: Establish a suitable machine learning model to identify its primary lesions for primary metastatic tumors in an integrated learning approach, making it more accurate to improve primary lesions’ diagnostic efficiency.Methods: After deleting the features whose expression level is lower than the threshold, we use two methods to perform feature selection and use XGBoost for classification. After the optimal model is selected through 10-fold cross-validation, it is verified on an independent test set.Results: Selecting features with around 800 genes for training, theR²-score of a 10-fold CV of training data can reach 96.38%, and theR²-score of test data can reach 83.3%.Conclusion: These findings suggest that by combining tumor data with machine learning methods, each cancer has its corresponding classification accuracy, which can be used to predict primary metastatic tumors’ location. The machine-learning-based method can be used as an orthogonal diagnostic method to judge the machine learning model processing and clinical actual pathological conditions.

Oral disorders are major global health issues, affecting 3.5 billion people and imposing significant economic burdens. This study analyzed the distribution and trends of oral disorder burden globally and in China from 1990 to 2021, aiming to inform resource allocation and prevention strategies. Data from the Global Burden of Disease (GBD) 2021 database were used to evaluate the incidence, prevalence, and years lived with disability (YLDs) of oral disorders. All statistical analyses and data visualizations were conducted using R. In 2021, the burden of oral disorders in China accounted for a significant proportion of the global burden. The incidence, prevalence, and YLDs increased by 15.49%, 52.44%, and 86.86% respectively compared with 1990. The global burden also showed an upward trend during the same period. The relevant indicators in China are at a relatively low level, and disparities were observed among regions with different sociodemographic indices (SDI). Oral disorder burden is on the rise globally, with females, children, adolescents, and the elderly being the key affected groups. Regions with middle SDI bear a heavier burden. This study provides a scientific basis for the formulation of relevant policies and emphasizes the necessity of interventions for specific groups.

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus causes novel coronavirus disease 2019 (COVID-19), which is characterized by pneumonia, cytokine storms, and lymphopenia. Due to immunosuppression, cancer patients may be more susceptible to SARS-CoV-2 and have more serious complications. According to recent research, cyclic GMP-AMP synthase (cGAS) could be a potential SARS-CoV-2 sensor. However, at present, no studies have been conducted on cGAS gene alterations in pan-cancer. This study aimed to discover therapeutic implications for COVID-19-infected tumor patients by performing a comprehensive analysis of cGAS in malignant tumors. Methods: cGAS expression matrices were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases, which were used to evaluate cGAS expression in various tumors, its prognostic value, and its relationship to the immune microenvironment, microsatellite instability (MSI), immune neoantigens, gene mutations, immune checkpoints, MSI, tumor mutational burden (TMB), mismatch repair (MMR) genes, and DNA methyltransferases (DNMT). We also used the cBioPortal, Human Protein Atlas (HPA), and GeneMANIA databases to explore the types of changes, gene networks and immunofluorescence localization, and protein expression of these genes. Results: Compared to normal tissues, cGAS was highly expressed in 13 types of cancer (e.g., lung cancer) and lowly expressed in other cancers (e.g., pancreatic cancer). cGAS expression was associated with prognosis in nine cancers, such as renal clear cell carcinoma (P<0.05). Furthermore, deep deletion was the most common type of cGAS genomic mutation. DNMT, immune infiltration levels, TMB, MSI, MMR genes, neoantigens, and immune checkpoints were all correlated with cGAS expression. Moreover, we used the GSE30589 dataset to investigate the post-SARS-CoV infection changes in cGAS expression in vitro. Finally, mithramycin, MI219, AFP464, aminoflavone, kahalide F, AT13387, doxorubicin, and other drugs increased the sensitivity of cGAS expression. According to the evidence presented above, cGAS may become an important target for cancer therapy. Conclusions: This study discovered that SARS-CoV-2-infected cancer patients might experience changes in their tumor environment as a result of cGAS, making patients with tumors expressing high cGAS more susceptible to COVID-19 and possibly a worsening prognosis. Furthermore, cGAS may be a novel biomarker for diagnosing and treating COVID-19-infected tumor patients.

Background: Gastric cancer (GC) is the fifth commonest cancer and the third commonest reason of death causing by cancer worldwide. Currently, tumor immunology and ferropotosis develop rapidly that has made gastric cancer be treated in new directions. So, finding the potential targets and prognostic biomarkers for immunotherapy combined with ferropotosis is urgent. Methods: By mining TCGA, immune-related genes, ferropotosis-related genes and immune-ferropotosis-related differentially expressed genes (IFR-DEGs) were identified. The independent prognostic value of IFR-DEGs was determined by differential expression analysis, prognostic analysis, and univariate and lasso regression analysis. Then, based on the prognostic risk model, the correlation between IFR-DEGs and immune scores, immune checkpoints were evaluated. Besides, we predicted the response of high and low risk groups to drugs. Results: A 15-gene prognostic feature was constructed. The high-risk group had a poorer prognosis than the low-risk group. High-risk group had higher level of Treg immune cell infiltration compared with that in the low-risk group, and the tumor purity, immune checkpoint PD-1 and CTLA4, and immunity in the high-risk group were higher than those in the low-risk group. These results indicate that immune ferropotosis-related genes migh be potential predictors of STAD’s response to ICI immunotherapy biomarkers. In addition, the response of small molecule drugs such as Nilotini, Sunitinib, Imatinib, etc. for high and low risk groups was predicted. Conclusion: IFRSig can be regarded as an independent prognostic feature and may estimate OS and clinical treatment response in patients with STAD. IFRSig also has important correlation with immune microenvironment. A new understanding of the immune-ferropotosis-related genes during the occurrence and development of STAD is provided in this study.