Discovering and Validating Cuproptosis-Associated Marker Genes for Accurate Keloid Diagnosis Through Multiple Machine Learning Models.

Background: Keloid is a common condition characterized by abnormal scarring of the skin, affecting a significant number of individuals worldwide. Objective: The occurrence of keloids may be related to the reduction of cell death. Recently, a new cell death mode that relies on copper ions has been discovered. This study aimed to identify novel cuproptosis-related genes that are associated with keloid diagnosis. Methods: We utilized several gene expression datasets, including GSE44270 and GSE145725 as the training group, and GSE7890, GSE92566, and GSE121618 as the testing group. We integrated machine learning models (SVM, RF, GLM, and XGB) to identify 10 cuproptosis-related genes (CRGs) for keloid diagnosis in the training group. The diagnostic capability of the identified CRGs was validated using independent datasets, RT-qPCR, Western blotting, and IHC analysis. Results: Our study successfully categorized keloid samples into two clusters based on the expression of cuproptosis-related genes. Utilizing WGCNA analysis, we identified 110 candidate genes associated with cuproptosis. Subsequent functional enrichment analysis results revealed that these genes may play a regulatory role in cell growth within keloid tissue through the MAPK pathway. By integrating machine learning models, we identified CRGs that can be used for diagnosing keloid. The diagnostic efficacy of CRGs was confirmed using independent datasets, RT-qPCR, Western blotting, and IHC analysis. GSVA analysis indicated that high expression of CRGs influenced the gene set related to ECM receptor interaction. Conclusion: This study identified 10 cuproptosis-related genes that provide insights into the molecular mechanisms underlying keloid development and may have implications for the development of targeted therapies.

Leveraging a Genomic Instability-Derived Signature to Predict the Prognosis and Therapy Sensitivity of Clear Cell Renal Cell Carcinoma.

BACKGROUND: Kidney cancer is a significant health concern with growing treatment resistance, often linked to genomic instability. This study used datasets from 72 renal and 952 clear cell renal cell carcinoma samples to identify genomic instability-derived lncRNAs and develop a prognostic index (GILPI). METHODS: The study involved differential expression analysis, weighted gene co-expression network analysis, Cox analyses to construct GILPI, and its validation through survival analysis. SNP, TMB, and MSI data were integrated, and GSEA analysis explored associated pathways. A predictive nomogram was created, and immune cell infiltration was assessed. Targeted treatments for low-GILPI patients were identified through molecular docking and network pharmacology. RESULTS: GILPI proved reliable in predicting prognosis (P<0.001, AUC=0.68) and in combination with other factors. GSEA revealed distinct pathway enrichments for different GILPI subgroups. The nomogram exhibited strong predictive performance (AUC=0.902). Immune cell differences suggest potential for immunotherapy in high-GILPI patients and targeted treatment in low-GILPI patients. Lapatinib and nilotinib were identified as effective drugs for low-GILPI patients. CONCLUSION: This study identified a GILPI for kidney cancer prognosis, integrating various factors for a comprehensive assessment. It highlighted potential treatment strategies based on GILPI subgroups, enhancing personalized treatment approaches.

A tumor mutational burden-derived immune computational framework selects sensitive immunotherapy/chemotherapy for lung adenocarcinoma populations with different prognoses.

Background: Lung adenocarcinoma (LUAD) kills millions of people every year. Recently, FDA and researchers proved the significance of high tumor mutational burden (TMB) in treating solid tumors. But no scholar has constructed a TMB-derived computing framework to select sensitive immunotherapy/chemotherapy for the LUAD population with different prognoses. Methods: The datasets were collected from TCGA, GTEx, and GEO. We constructed the TMB-derived immune lncRNA prognostic index (TILPI) computing framework based on TMB-related genes identified by weighted gene co-expression network analysis (WGCNA), oncogenes, and immune-related genes. Furthermore, we mapped the immune landscape based on eight algorithms. We explored the immunotherapy sensitivity of different prognostic populations based on immunotherapy response, tumor immune dysfunction and exclusion (TIDE), and tumor inflammation signature (TIS) model. Furthermore, the molecular docking models were constructed for sensitive drugs identified by the pRRophetic package, oncopredict package, and connectivity map (CMap). Results: The TILPI computing framework was based on the expression of TMB-derived immune lncRNA signature (TILncSig), which consisted of AC091057.1, AC112721.1, AC114763.1, AC129492.1, LINC00592, and TARID. TILPI divided all LUAD patients into two populations with different prognoses. The random grouping verification, survival analysis, 3D PCA, and ROC curve (AUC=0.74) firmly proved the reliability of TILPI. TILPI was associated with clinical characteristics, including smoking and pathological stage. Furthermore, we estimated three types of immune cells threatening the survival of patients based on multiple algorithms. They were macrophage M0, T cell CD4 Th2, and T cell CD4 memory activated. Nevertheless, five immune cells, including B cell, endothelial cell, eosinophil, mast cell, and T cell CD4 memory resting, prolonged the survival. In addition, the immunotherapy response and TIDE model proved the sensitivity of the low-TILPI population to immunotherapy. We also identified seven intersected drugs for the LUAD population with poor prognosis, which included docetaxel, gemcitabine, paclitaxel, palbociclib, pyrimethamine, thapsigargin, and vinorelbine. Their molecular docking models and best binding energy were also constructed and calculated. Conclusions: We divided all LUAD patients into two populations with different prognoses. The good prognosis population was sensitive to immunotherapy, while the people with poor prognosis benefitted from 7 drugs.

Histone Deacetylase 1 Expression and Regulatory Network in Lung Adenocarcinoma Based on Data Mining and Implications for Targeted Treatment.

Background and Aims: Histone deacetylase 1 (HDAC1) codes a protein that is a component of the histone deacetylase complex. The abnormal expression of HDAC1 is strongly correlated with cell proliferation, differentiation, transcription, and translation. Through continuous screening of genes associated with changes in lung adenocarcinoma (LUAD), gene networks are formed to explore tumor pathogenesis and new therapeutic targets. Methods: We evaluated HDAC1 gene survival analysis and its expression of LUAD using relevant websites and databases (TCGA and GEO databases). Through data mining, we determined the frequency and type of HDAC1 mutation, obtained the relevant heat map of the gene interaction network, completed the analysis of gene ontology and function enrichment, and understood the pharmaceutic of HDAC1. Results: We found that HDAC1 expression was associated with the prognosis of patients with LUAD. In gene expression analysis, HDAC1 was highly expressed in LUAD, and the HDAC1 interaction gene network (MARCKSL, eIF3I) was closely related to cellular gene expression. Functional network analysis shows that the expression of HDAC1 is related to the monitoring point of the G1-S phase of the cell cycle and the activation of the Notch signaling pathway (CSL transcription factor), which is involved in the process of cell proliferation and differentiation and gene expression associated with new therapeutic targets. Conclusion: Our data revealed the expression and potential regulatory factors of HDAC1 in LUAD of data mining, which laid a foundation for the study of the occurrence, development, and treatment of HDAC1 in LUAD.

Genomic instability drives tumorigenesis and metastasis and its implications for cancer therapy.

Genetic instability can be caused by external factors and may also be associated with intracellular damage. At the same time, there is a large body of research investigating the mechanisms by which genetic instability occurs and demonstrating the relationship between genomic stability and tumors. Nowadays, tumorigenesis development is one of the hottest research areas. It is a vital factor affecting tumor treatment. Mechanisms of genomic stability and tumorigenesis development are relatively complex. Researchers have been working on these aspects of research. To explore the research progress of genomic stability and tumorigenesis, development, and treatment, the authors searched PubMed with the keywords "genome instability" "chromosome instability" "DNA damage" "tumor spread" and "cancer treatment". This extracts the information relevant to this study. Results: This review introduces genomic stability, drivers of tumor development, tumor cell characteristics, tumor metastasis, and tumor treatment. Among them, immunotherapy is more important in tumor treatment, which can effectively inhibit tumor metastasis and kill tumor cells. Breakthroughs in tumorigenesis development studies and discoveries in tumor metastasis will provide new therapeutic techniques. New tumor treatment methods can effectively prevent tumor metastasis and improve the cure rate of tumors.

The Mechanisms of Ferroptosis and the Applications in Tumor Treatment: Enemies or Friends?

Ferroptosis, as a newly discovered non-apoptotic cell death mode, is beginning to be explored in different cancer. The particularity of ferroptosis lies in the accumulation of iron dependence and lipid peroxides, and it is different from the classical cell death modes such as apoptosis and necrosis in terms of action mode, biochemical characteristics, and genetics. The mechanism of ferroptosis can be divided into many different pathways, so it is particularly important to identify the key sites of ferroptosis in the disease. Herein, based on ferroptosis, we analyze the main pathways in detail. More importantly, ferroptosis is linked to the development of different systems of the tumor, providing personalized plans for the examination, treatment, and prognosis of cancer patients. Although some mechanisms and side effects of ferroptosis still need to be studied, it is still a promising method for cancer treatment.

Identified Gefitinib Metabolism-Related lncRNAs can be Applied to Predict Prognosis, Tumor Microenvironment, and Drug Sensitivity in Non-Small Cell Lung Cancer.

Gefitinib has shown promising efficacy in the treatment of patients with locally advanced or metastatic EGFR-mutated non-small cell lung cancer (NSCLC). Molecular biomarkers for gefitinib metabolism-related lncRNAs have not yet been elucidated. Here, we downloaded relevant genes and matched them to relevant lncRNAs. We then used univariate, LASSO, and multivariate regression to screen for significant genes to construct prognostic models. We investigated TME and drug sensitivity by risk score data. All lncRNAs with differential expression were selected for GO/KEGG analysis. Imvigor210 cohort was used to validate the value of the prognostic model. Finally, we performed a stemness indices difference analysis. lncRNA-constructed prognostic models were significant in the high-risk and low-risk subgroups. Immune pathways were identified in both groups at low risk. The higher the risk score the greater the value of exclusion, MDSC, and CAF. PRRophetic algorithm screened a total of 58 compounds. In conclusion, the prognostic model we constructed can accurately predict OS in NSCLC patients. Two groups of low-risk immune pathways are beneficial to patients. Gefitinib metabolism was again validated to be related to cytochrome P450 and lipid metabolism. Finally, drugs that might be used to treat NSCLC patients were screened.

Phase Separation: "The Master Key" to Deciphering the Physiological and Pathological Functions of Cells.

Phase separation is a hot research field at present. It involves almost all aspects of cells and plays a significant role in cells, promising to be "a master key" in unlocking the mysteries of nature. In this review, the factors that affect phase separation are introduced, such as own component, electrostatic interaction, and chemical modification. Furthermore, the physiological roles of phase separation in cells, including molecules transport channel, gene expression and regulation, cellular division and differentiation, stress response, proteins refolding and degradation, cell connections, construction of skin barrier, and cell signals transmission, are highlighted. However, the disorder of phase separation leads to pathological condensates, which are associated with neurodegenerative diseases, tumors, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This relationship is considered the potential target for developing corresponded drugs and therapy. Some drugs targeting phase separation have improved meaningful, such as tankyrase, lipoamide, oligonucleotides, elvitagravir, nilotinib, CVL218, PJ34. All in all, mystery phase separation provides a new viewpoint for researchers to explore cells, and is expected to solve many unknown phenomena in nature.

Mesenchymal stem cells: ideal seeds for treating diseases.

Mesenchymal stem cells (MSCs), a kind of multipotent stem cells with self-renewal ability and multi-differentiation ability, have become the "practical stem cells" for the treatment of diseases. MSCs have immunomodulatory properties and can be used to treat autoimmune diseases, such as systemic lupus erythematosus (SLE) and Crohn's disease. MSCs also can be used in cancer and aging. At present, many clinical experiments are using MSCs. MSCs can reduce the occurrence of inflammation and apoptosis of tissue cells, and promote the proliferation of endogenous tissue and organ cells, so as to achieve the effect of repairing tissue and organs. MSCs presumably also play an important role in Corona Virus Disease 2019 (COVID-19) infection.