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INTRODUCTION: Tongue squamous cell carcinoma (TSCC) is a common malignant tumour type with aggressive invasion and a poor prognosis. To date, invasion-related gene expression signatures for the prognostic stratification of TSCC patients are unavailable in clinical practice. This study aimed to assess the impact of invasion-related genes on the prognosis of TSCC patients. METHODS: We obtained mRNA profiles and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (TCGA-TSCC and GSE41116, respectively). The TSCC samples from the TCGA-TSCC cohort were randomly divided into TCGA training and TCGA test datasets at a 7:3 ratio. Next, a disease-free survival (DFS) prognostic risk model was established on the basis of univariate and stepwise multivariate Cox regression analyses of the TCGA training cohort. Moreover, prognostic genes were screened. The model was subsequently evaluated and validated using the TCGA test and GSE41116 datasets. In addition, the prognostic genes were validated in the human TSCC cell line UM1 and the human oral keratinocyte (HOK) cell line using quantitative real-time polymerase chain reaction (qRT-PCR) analysis. RESULTS: A total of 70 candidate genes related to invasion were identified in the TCGA-TSCC cohort. DFS data were subsequently constructed, and 6 prognostic genes, HMGN2, MYL12B, ACTB, PPP1CA, PSMB9, and IFITM3, were identified. The TSCC samples were divided into high- and low-risk groups in the TCGA training, TCGA test, and GSE41116 cohorts, respectively. In particular, patients with TSCC in the low-risk group had longer DFS than those in the high-risk group. Furthermore, qRT-PCR analysis confirmed that the expression levels of the 6 prognostic genes were significantly greater in the TSCC cell line UM1 than in the HOK cell line. CONCLUSION: This study identified new invasion-related target genes related to poor prognosis in TSCC patients, providing new insights into the underlying mechanisms of TSCC invasion.

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This study aimed to develop a prognostic risk model based on immune-related long non-coding RNAs (lncRNAs). By analyzing the expression profiles of specific long non-coding RNAs, the objective was to construct a predictive model to accurately assess the survival prognosis of breast cancer (BC) patients. This effort seeks to provide personalized treatment strategies for patients and improve clinical outcomes. Based on the median risk value, 300 samples of triple-negative BC (TNBC) patients were rolled into a high-risk group (HR group, n = 140) and a low-risk group (LR group, n = 160). Multivariate Cox (MVC) analysis was performed by combining the patient risk score and clinical information to evaluate the prognostic value of the prognostic risk (PR) model. A total of 371 immune-related lncRNAs associated with the prognosis of TNBC were obtained from 300 TNBC samples. Nine associated with prognosis were obtained by univariate Cox (UVC) analysis, and 3 (AC090181.2, LINC01235, and LINC01943) were selected by MVC analysis for the construction of TNBC PR model. Survival analysis showed a great difference in TNBC patients in different groups (P < 0.001). The receiver operator characteristic (ROC) curve showed the model possessed a good area under ROC curve (AUC), which was 0.928. The patient RS jointing with clinical information as well as the MVC analysis revealed that RS was an independent risk factor (IRF) for prognosis of TNBC (P < 0.05, HR = 1.033286). Therefore, the lncRNAs associated with TNBC immunity can be screened by bioinformatics analysis, and the established PR model of TNBC could better predict the prognosis of patients with TNBC, exhibiting a high application value in clinic.

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ARN Largo no Codificante , Humanos , ARN Largo no Codificante/genética , Femenino , Pronóstico , Persona de Mediana Edad , Biomarcadores de Tumor/genética , Neoplasias de la Mama Triple Negativas/genética , Neoplasias de la Mama Triple Negativas/mortalidad , Neoplasias de la Mama Triple Negativas/inmunología , Regulación Neoplásica de la Expresión Génica , Neoplasias de la Mama/genética , Neoplasias de la Mama/mortalidad , Neoplasias de la Mama/inmunología , Medición de Riesgo/métodos , Curva ROC , Perfilación de la Expresión Génica , Análisis de Supervivencia , Factores de Riesgo

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Colorectal cancer (CRC) is a major contributor to global morbidity and mortality, necessitating more effective therapeutic approaches. T cells, prominent in the tumor microenvironment, exert a crucial role in modulating immunotherapeutic responses and clinical outcomes in CRC. This study introduces a pioneering method for characterizing the CRC immune microenvironment using single-cell sequencing data. Unlike previous approaches, which focused on individual T-cell signature genes, we utilized overall infiltration levels of colorectal cancer signature T-cells. Through weighted gene co-expression network analysis, Lasso regression, and StepCox analysis, we developed a prognostic risk model, TRGS (T-cell related genes signatures), based on six T cell-related genes. Multivariate Cox analysis identified TRGS as an independent prognostic factor for CRC, showcasing its superior predictive efficacy compared to existing immune-related prognostic models. Immunoreactivity analysis revealed higher Immunophenoscore and lower Tumor Immune Dysfunction and Exclusion scores in the low-risk group, indicating potential responsiveness to immune checkpoint inhibitor therapy. Additionally, patients in the low-risk group demonstrated heightened sensitivity to 5-fluorouracil-based chemotherapy regimens. In summary, TRGS emerges as a standalone prognostic biomarker for CRC, offering insights to optimize patient responses to immunotherapy and chemotherapy, thereby laying the groundwork for personalized tumor management strategies.

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Neoplasias Colorrectales , RNA-Seq , Análisis de la Célula Individual , Linfocitos T , Microambiente Tumoral , Humanos , Neoplasias Colorrectales/genética , Neoplasias Colorrectales/inmunología , Neoplasias Colorrectales/mortalidad , Análisis de la Célula Individual/métodos , Pronóstico , Microambiente Tumoral/inmunología , Microambiente Tumoral/genética , Linfocitos T/inmunología , Linfocitos T/metabolismo , Regulación Neoplásica de la Expresión Génica , Biomarcadores de Tumor/genética , Femenino , Masculino , Transcriptoma , Linfocitos Infiltrantes de Tumor/inmunología , Linfocitos Infiltrantes de Tumor/metabolismo

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Ovarian cancer (OV) is a malignant tumor that ranks first among gynecological cancers, thus posing a significant threat to women's health. Immunogenic cell death (ICD) can regulate cell death by activating the adaptive immune system. Here, we aimed to comprehensively characterize the features of ICD-associated genes in ovarian cancer, and to investigate their prognostic value and role in the response to immunotherapy. After analyzing datasets from The Cancer Genome Atlas, we utilized weighted gene coexpression network analysis to screen for hub genes strongly correlated with ICD genes in OV, which was subsequently validated with OV samples from the Gene Expression Omnibus (GEO) database. A prognostic risk model was then constructed after combining univariate, multivariate Cox regression and LASSO regression analysis to recognize nine ICD-associated molecules. Next, we stratified all OV patients into two subgroups according to the median value. The multivariate Cox regression analysis showed that the risk model could predict OV patient survival with good accuracy. The same results were also found in the validation set from GEO. We then compared the degree of immune cell infiltration in the tumor microenvironment between the two subgroups of OV patients, and revealed that the high-risk subtype had a higher degree of immune infiltration than the low-risk subtype. Additionally, in contrast to patients in the high-risk subgroup, those in the low-risk subgroup were more susceptible to chemotherapy. In conclusion, our research offers an independent and validated model concerning ICD-related molecules to estimate the prognosis, degree of immune infiltration, and chemotherapy susceptibility in patients with OV.

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Calcium ions (Ca2+) are crucial in tumorigenesis and progression, with their elevated levels indicating a negative prognosis in Kidney Renal Clear Cell Carcinoma (KIRC). The influence of genes regulating calcium ions on the survival outcomes of KIRC patients and their interaction with the tumor's immune microenvironment is yet to be fully understood. This study analyzed gene expression data from KIRC tumor and adjacent non-tumor tissues using the TCGA-KIRC dataset to pinpoint genes that are differentially expressed in KIRC. Intersection of these genes with those regulating calcium ions highlighted specific calcium ion-regulating genes that exhibit differential expression in KIRC. Subsequently, prognostic risk models were developed using univariate Cox and LASSO-Cox regression analyses to verify their diagnostic precision. Additionally, the study investigated the correlation between tumor immunity and KIRC patient outcomes, assessing the contribution of STAC3 genes to tumor immunity. Further exploration entailed SSGASE, single-cell analysis, pseudotime analysis and both in vivo and in vitro experiments to evaluate STAC3's role in tumor immunity and progression. Notably, STAC3 was significantly overexpressed in tumor specimens and positively correlated with the degree of malignancy of KIRC, affecting patients' prognosis. Elevated STAC3 expression correlated with enhanced immune infiltration in KIRC tumors. Furthermore, silencing STAC3 curtailed KIRC cell proliferation, migration, invasion, and stemness properties. Experimental models in mice confirmed that STAC3 knockdown led to a reduction in tumor growth. Elevated STAC3 expression is intricately linked with immune infiltration in KIRC tumors, as well as with the aggressive biological behaviors of tumor cells, including their proliferation, migration, and invasion. Targeting STAC3 presents a promising strategy to augment the efficacy of current therapeutic approaches and to better the survival outcomes of patients with KIRC.

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OBJECTIVES: Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC). METHODS: In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression. RESULTS: A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group. CONCLUSION: The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.

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Carcinoma de Células Renales , Neoplasias Renales , Nomogramas , Microambiente Tumoral , Humanos , Carcinoma de Células Renales/genética , Carcinoma de Células Renales/inmunología , Carcinoma de Células Renales/patología , Carcinoma de Células Renales/mortalidad , Microambiente Tumoral/inmunología , Microambiente Tumoral/genética , Neoplasias Renales/genética , Neoplasias Renales/inmunología , Neoplasias Renales/patología , Neoplasias Renales/mortalidad , Pronóstico , Estudios Retrospectivos , Femenino , Masculino , Persona de Mediana Edad , Medición de Riesgo/métodos , Biomarcadores de Tumor/genética , Anciano , Regulación Neoplásica de la Expresión Génica

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BACKGROUND: Breast cancer (BC) ranks as the most prevalent malignancy affecting women globally, with apoptosis playing a pivotal role in its pathological progression. Despite the crucial role of apoptosis in BC development, there is limited research exploring the relationship between BC prognosis and apoptosis-related genes (ARGs). Therefore, this study aimed to establish a BC-specific risk model centered on apoptosis-related factors, presenting a novel approach for predicting prognosis and immune responses in BC patients. METHODS: Utilizing data from The Cancer Gene Atlas (TCGA), Cox regression analysis was employed to identify differentially prognostic ARGs and construct prognostic models. The accuracy and clinical relevance of the model, along with its efficacy in predicting immunotherapy outcomes, were evaluated using independent datasets, Receiver Operator Characteristic (ROC) curves, and nomogram. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were used to predict potential mechanical pathways. The CellMiner database is used to assess drug sensitivity of model genes. RESULTS: A survival risk model comprising eight prognostically relevant apoptotic genes (PMAIP1, TP53AIP1, TUBA3D, TUBA1C, BCL2A1, EMP1, GSN, F2) was established based on BC patient samples from TCGA. Calibration curves validated the ROC curve and nomogram, demonstrating excellent accuracy and clinical utility. In samples from the Gene Expression Omnibus (GEO) datasets and immunotherapy groups, the low-risk group (LRG) demonstrated enhanced immune cell infiltration and improved immunotherapy responses. Model genes also displayed positive associations with sensitivity to multiple drugs, including vemurafenib, dabrafenib, PD-98059, and palbociclib. CONCLUSIONS: This study successfully developed and validated a prognostic model based on ARGs, offering new insights into prognosis and immune response prediction in BC patients. These findings hold promise as valuable references for future research endeavors in this field.

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Apoptosis , Neoplasias de la Mama , Nomogramas , Medicina de Precisión , Humanos , Neoplasias de la Mama/genética , Neoplasias de la Mama/inmunología , Neoplasias de la Mama/patología , Femenino , Apoptosis/genética , Pronóstico , Medicina de Precisión/métodos , Genómica/métodos , Biomarcadores de Tumor/genética , Regulación Neoplásica de la Expresión Génica , Bases de Datos Genéticas , Curva ROC , Medición de Riesgo/métodos

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BACKGROUND: Uveal melanoma (UVM) stands as the predominant type of primary intraocular malignancy among adults. The clinical significance of N7-methylguanosine (m7G), a prevalent RNA modifications, in UVM remains unclear. METHODS: Primary information from 80 UVM patients were analyzed as the training set, incorporating clinical information, mutation annotations and mRNA expression obtained from The Cancer Genome Atlas (TCGA) website. The validation set was carried out using Gene Expression Omnibus (GEO) database GSE22138 and GSE84976. Kaplan-Meier and Cox regression of univariate analyses were subjected to identify m7G-related regulators as prognostic genes. RESULT: A prognostic risk model comprising EIF4E2, NUDT16, SNUPN and WDR4 was established through Cox regression of LASSO. Evaluation of the model's predictability for UVM patients' prognosis by Receiver Operating Characteristic (ROC) curves in the training set, demonstrated excellent performance Area Under the Curve (AUC) > 0.75. The high-risk prognosis within the TCGA cohort exhibit a notable worse outcome. Additionally, an independent correlation between the risk score and overall survival (OS) among UVM patients were identified. External validation of this model was carried out using the validation sets (GSE22138 and GSE84976). Immune-related analysis revealed that patients with high score of m7G-related risk model exhibited elevated level of immune infiltration and immune checkpoint gene expression. CONCLUSION: We have developed a risk prediction model based on four m7G-related regulators, facilitating effective estimate UVM patients' survival by clinicians. Our findings shed novel light on essential role of m7G-related regulators in UVM and suggest potential novel targets for the diagnosis, prognosis and therapy of UVM.

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Guanosina , Melanoma , Neoplasias de la Úvea , Humanos , Neoplasias de la Úvea/genética , Neoplasias de la Úvea/mortalidad , Melanoma/genética , Pronóstico , Guanosina/análogos & derivados , Femenino , Masculino , Persona de Mediana Edad , Regulación Neoplásica de la Expresión Génica , Biomarcadores de Tumor/genética , Curva ROC , Estimación de Kaplan-Meier

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Liver cancer manifests as a profoundly heterogeneous malignancy, posing significant challenges in terms of both therapeutic intervention and prognostic evaluation. Given that the liver is the largest metabolic organ, a prognostic risk model grounded in single-cell transcriptome analysis and a metabolic perspective can facilitate precise prevention and treatment strategies for liver cancer. Hence, we identified 11 cell types in a scRNA-seq profile comprising 105,829 cells and found that the metabolic activity of malignant cells increased significantly. Subsequently, a prognostic risk model incorporating tumor heterogeneity, cell interactions, tumor cell metabolism, and differentially expressed genes was established based on eight genes; this model can accurately distinguish the survival outcomes of liver cancer patients and predict the response to immunotherapy. Analyzing the immune status and drug sensitivity of the high- and low-risk groups identified by the model revealed that the high-risk group had more active immune cell status and greater expression of immune checkpoints, indicating potential risks associated with liver cancer-targeted drugs. In summary, this study provides direct evidence for the stratification and precise treatment of liver cancer patients, and is an important step in establishing reliable predictors of treatment efficacy in liver cancer patients.

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Neoplasias Hepáticas , RNA-Seq , Análisis de la Célula Individual , Humanos , Neoplasias Hepáticas/genética , Neoplasias Hepáticas/metabolismo , Neoplasias Hepáticas/patología , Pronóstico , Análisis de la Célula Individual/métodos , Regulación Neoplásica de la Expresión Génica , Transcriptoma , Biomarcadores de Tumor/genética , Biomarcadores de Tumor/metabolismo , Perfilación de la Expresión Génica/métodos , Análisis de Expresión Génica de una Sola Célula , Reprogramación Metabólica

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OBJECTIVE: To develop a prognostic risk model for Bladder Cancer (BLCA) based on mitochondrial-related long non-coding RNAs (lncRNAs). METHODS: Transcriptome and clinical data of BLCA patients were retrieved from the TCGA database. Mitochondrial-related lncRNAs with independent prognostic significance were screened to develop a prognostic risk model. Patients were categorized into high- and low-risk groups using the model. Various methods including Kaplan-Meier (KM) analysis, ROC curve analysis, Gene Set Enrichment Analysis (GSEA), immune analysis, and chemotherapy drug analysis were used to verify and evaluate the model. RESULTS: A mitochondrial-associated lncRNA prognostic risk model with independent prognostic significance was developed. High-risk group (HRG) patients exhibited significantly shorter survival periods compared to low-risk group (LRG) patients (P < 0.01). The risk score from the model was an independent predictor of BLCA prognosis, correlating with tumor grade, pathological stage, and lymph node metastasis (P < 0.05). The HRG showed significant positive correlations with high expressions of immune checkpoints (CTLA4, LAG3, PD-1, TIGIT, PD-L1, PD-L2, and TIM-3) and lower IC50 for chemotherapy drugs (cisplatin, docetaxel, paclitaxel, methotrexate, and vinblastine) (P < 0.001). CONCLUSIONS: The mitochondrial-related lncRNA-based prognostic risk model effectively predicts BLCA prognosis and can guide individualized treatment for BLCA patients.

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Hepatocellular carcinoma (HCC) represents a substantial global health burden. Tumorinfiltrating B lymphocytes (TIL-Bs) contribute to tumor progression and significantly impact the efficacy of tumor therapy. However, the characteristics of TIL-Bs in HCC and their effect on HCC therapy remain elusive. Single-cell RNA sequencing (scRNAseq) was applied to investigate the heterogeneity, cellular differentiation and cell-cell communication of TIL-Bs in HCC. Further, the Cancer Genome Atlas-liver hepatocellular carcinoma (TCGA-LIHC) and liver cancer institutes (LCI) cohorts were applied to construct and validate the plasma cell marker-based prognostic risk model. The relationship between the prognostic risk model and the responsiveness of immunotherapy and chemotherapy in patients with HCC were estimated by OncoPredict and tumor immune dysfunction and exclusion (TIDE) algorithm. Finally, we established nomogram and calibration curves to evaluate the precision of the risk score in predicating survival probability. Our data identified five subtypes of TIL-Bs in HCC, each exhibiting varying levels of infiltration in tumor tissues. The interactions between TIL-Bs and other cell types contributed to shaping distinct tumor microenvironments (TME). Moreover, we found that TIL-Bs subtypes had disparate prognostic values in HCC patients. The prognostic risk model demonstrated exceptional predictive accuracy for overall survival and exhibited varying sensitivities to immunotherapy and chemotherapy among patients with HCC. Our data demonstrated that the risk score stood as an independent prognostic predictor and the nomogram results further affirmed its strong prognostic capability. This study reveals the heterogeneity of TIL-Bs and provides a prognostic risk model based on plasma cell markers in HCC, which could prove valuable in predicting prognosis and guiding the choice of suitable therapies for patients with HCC.

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Chemokines are key proteins that regulate cell migration and immune responses and are essential for modulating the tumor microenvironment. Despite their close association with colon cancer, the expression patterns, prognosis, immunity, and specific roles of chemokines in colon cancer are still not fully understood. In this study, we investigated the mutational features, differential expression, and immunological characteristics of chemokines in colon cancer (COAD) by analyzing the Tumor Genome Atlas (TCGA) database. We clarified the biological functions of these chemokines using Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. By univariate and multivariate COX regression analyses, we developed chemokine-based prognostic risk models. In addition, using Gene Set Enrichment Analysis (GSEA) and Gene Set Variant Analysis (GSVA), we analyzed the differences in immune responses and signaling pathways among different risk groups. The results showed that the mutation rate of chemokines was low in COAD, but 25 chemokines were significantly differentially expressed. These chemokines function in several immune-related biological processes and play key roles in signaling pathways including cytokine-cytokine receptor interactions, NF-kappa B, and IL-17. Prognostic risk models based on CCL22, CXCL1, CXCL8, CXCL9, and CXCL11 performed well. GSEA and GSVA analyses showed significant differences in immune responses and signaling pathways across risk groups. In conclusion, this study reveals the potential molecular mechanisms of chemokines in COAD and proposes a new prognostic risk model based on these insights.

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Quimiocinas , Neoplasias del Colon , Humanos , Quimiocinas/genética , Quimiocinas/metabolismo , Neoplasias del Colon/genética , Neoplasias del Colon/inmunología , Pronóstico , Regulación Neoplásica de la Expresión Génica , Mutación , Transducción de Señal , Microambiente Tumoral/inmunología , Microambiente Tumoral/genética , Ontología de Genes , Femenino , Masculino , Bases de Datos Genéticas , Biomarcadores de Tumor/genética , Perfilación de la Expresión Génica

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Cuproptosis and disulfidptosis, recently discovered mechanisms of cell death, have demonstrated that differential expression of key genes and long non-coding RNAs (lncRNAs) profoundly influences tumor development and affects their drug sensitivity. Clear cell renal cell carcinoma (ccRCC), the most common subtype of kidney cancer, presently lacks research utilizing cuproptosis and disulfidptosis-related lncRNAs (CDRLRs) as prognostic markers. In this study, we analyzed RNA-seq data, clinical information, and mutation data from The Cancer Genome Atlas (TCGA) on ccRCC and cross-referenced it with known cuproptosis and disulfidptosis-related genes (CDRGs). Using the LASSO machine learning algorithm, we identified four CDRLRs-ACVR2B-AS1, AC095055.1, AL161782.1, and MANEA-DT-that are strongly associated with prognosis and used them to construct a prognostic risk model. To verify the model's reliability and validate these four CDRLRs as significant prognostic factors, we performed dataset grouping validation, followed by RT-qPCR and external database validation for differential expression and prognosis of CDRLRs in ccRCC. Gene function and pathway analysis were conducted using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) for high- and low-risk groups. Additionally, we have analyzed the tumor mutation burden (TMB) and the immune microenvironment (TME), employing the oncoPredict and Immunophenoscore (IPS) algorithms to assess the sensitivity of diverse risk categories to targeted therapeutics and immunosuppressants. Our predominant objective is to refine prognostic predictions for patients with ccRCC and inform treatment decisions by conducting an exhaustive study on cuproptosis and disulfidptosis.

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Carcinoma de Células Renales , Neoplasias Renales , ARN Largo no Codificante , Humanos , Carcinoma de Células Renales/genética , ARN Largo no Codificante/genética , Pronóstico , Reproducibilidad de los Resultados , Medicina de Precisión , Neoplasias Renales/genética , Apoptosis , Microambiente Tumoral

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Ovarian cancer is the most lethal and aggressive gynecological cancer with a high recurrence rate and is often diagnosed late. In ovarian cancer, multiple metabolic enzymes of lipid metabolism are abnormally expressed, resulting in metabolism disorder. As a characteristic pathway in polyunsaturated fatty acid (PUFA) metabolism, arachidonic acid (AA) metabolism is disturbed in ovarian cancer. Therefore, we established a 10-gene signature model to evaluate the prognostic risk of PUFA-related genes. This 10-gene signature has strong robustness and can play a stable predictive role in datasets of various platforms (TCGA, ICGC, and GSE17260). The high association between the risk subgroups and clinical characteristics indicated a good performance of the model. Our data further indicated that the high expression of LTA4H was positively correlated with poor prognosis in ovarian cancer. Deficiency of LTA4H enhanced sensitivity to Cisplatin and modified the characteristics of immune cell infiltration in ovarian cancer. Additionally, our results indicate that CCL5 was involved in the aberrant metabolism of the AA/LTA4H axis, which contributes to the reduction of tumor-infiltrating CD8+ T cells and immune escape in ovarian cancer. These findings provide new insights into the prognosis and potential target of LTA4H/CCL5 in treating ovarian cancer.

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Quimiocina CCL5 , Cisplatino , Epóxido Hidrolasas , Neoplasias Ováricas , Microambiente Tumoral , Femenino , Neoplasias Ováricas/tratamiento farmacológico , Neoplasias Ováricas/inmunología , Neoplasias Ováricas/genética , Neoplasias Ováricas/metabolismo , Humanos , Quimiocina CCL5/metabolismo , Quimiocina CCL5/genética , Microambiente Tumoral/inmunología , Microambiente Tumoral/efectos de los fármacos , Cisplatino/uso terapéutico , Cisplatino/farmacología , Epóxido Hidrolasas/metabolismo , Epóxido Hidrolasas/genética , Línea Celular Tumoral , Pronóstico , Regulación Neoplásica de la Expresión Génica , Ácido Araquidónico/metabolismo , Linfocitos T CD8-positivos/inmunología , Linfocitos T CD8-positivos/metabolismo , Antineoplásicos/uso terapéutico , Antineoplásicos/farmacología , Animales , Linfocitos Infiltrantes de Tumor/inmunología , Linfocitos Infiltrantes de Tumor/metabolismo , Linfocitos Infiltrantes de Tumor/efectos de los fármacos , Ratones

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Cholesterol metabolism is crucial for cell survival and cancer progression. The prognostic patterns of genes linked to cholesterol metabolism (CMAGs) in CESC, however, have received very little attention in research. From public databases, TCGA-CESC cohorts with mRNA expression patterns and the accompanying clinical information of patients were gathered. Consensus clustering was used to find the molecular subtype connected to cholesterol metabolism. In the TCGA-CESC cohort, a predictive risk model with 28 CMAGs was created using Lasso-Cox regression. The function enrichment analysis between groups with high-and low-risk were investigated by employing GO, KEGG, and GSVA software. The immune cell infiltration was analyzed using ESTIMATE, CIBERSORT, and MCPCOUNTER methods. Finally, we select 7 genes in risk model for further multivariate Cox analysis, and ultimately a hub gene, CHIT1, was identified. Meanwhile, the function of CHIT1 was preliminarily verified in cell and mice tumor model. In conclusion, the abundance of the CHIT1 gene might be beneficial for forecasting the prognosis of CESC, demonstrating that cholesterol metabolism could be a promising treatment target for CESC.

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Neoplasias del Cuello Uterino , Humanos , Animales , Ratones , Femenino , Metabolismo de los Lípidos , Supervivencia Celular , Colesterol

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The current study performed bioinformatics and in vitro and in vivo experiments to explore the effects of ADAM8 on the malignant behaviors and immunotherapeutic efficacy of renal clear cell carcinoma (ccRCC) Cells. The modular genes most associated with immune cells were screened. Then, prognostic risk models were constructed by univariate COX analysis, LASSO regression analysis and multivariate COX analysis, and their diagnostic value was determined. The correlation between tumor mutation load (TMB) scores and the prognosis of ccRCC patients was clarified. Finally, six key genes (ABI3, ADAM8, APOL3, MX2, CCDC69, and STAC3) were analyzed for immunotherapy efficacy. Human and mouse ccRCC cell lines and human proximal tubular epithelial cell lines were used for in vitro cell experiments. The effect of ADAM8 overexpression or knockdown on tumor formation and survival in ccRCC cells was examined by constructing subcutaneous transplanted tumor model. Totally, 636 Black module genes were screened as being most associated with immune cell infiltration. Six genes were subsequently confirmed for the construction of prognostic risk models, of which ABI3, APOL3 and CCDC69 were low-risk factors, while ADAM8, MX2 and STAC3 were high-risk factors. The constructed risk model based on the identified six genes could accurately predict the prognosis of ccRCC patients. Besides, TMB was significantly associated with the prognosis of ccRCC patients. Furthermore, ABI3, ADAM8, APOL3, MX2, CCDC69 and STAC3 might play important roles in treatment concerning CTLA4 inhibitors or PD-1 inhibitors or combined inhibitors. Finally, we confirmed that ADAM8 could promote the proliferation, migration and invasion of ccRCC cells through in vitro experiments, and further found that in in vivo experiments, ADAM8 knockdown could inhibit tumor formation in ccRCC cells, improve the therapeutic effect of anti-PD1, and prolong the survival of mice. Our study highlighted the alleviative role of silencing ADAM8 in ccRCC patients.

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Carcinoma de Células Renales , Carcinoma , Neoplasias Renales , Humanos , Animales , Ratones , Carcinoma de Células Renales/genética , Carcinoma de Células Renales/terapia , Carcinogénesis , Inmunoterapia , Neoplasias Renales/genética , Neoplasias Renales/terapia , Proliferación Celular/genética , Pronóstico , Proteínas de la Membrana/genética , Proteínas ADAM , Proteínas Adaptadoras Transductoras de Señales

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Objective To construct and evaluate a disulfidptosis-related genes(DRGs)prognostic risk model for hepatocellular carcinoma(HCC)based on the cancer genome atlas(TCGA)database.Methods The expression of 15 DRGs in 371 HCC samples and 50 adjacent cancer samples in the TCGA database was analyzed using bioinformatics methods,and then gene ontology(GO)functional annotation,Kyoto encyclopedia of genes and genomes(KEGG)enrichment analysis and Kaplan-Meier(KM)survival analysis were performed.Statistical significant DRGs were screened through univariate COX regression analysis,and key DRGs were selected through LASSO regression analysis and multivariate COX regression analysis to construct a prognostic risk model.HCC patients were divided into high-risk and low-risk groups based on risk scores,and the KM survival curves and time-dependent receiver operator characteristic(ROC)curves were created to validate and evaluate prognostic risk models.Results Compared with the adjacent cancer samples,FLNA,MYH9,TLN1,ACTB,MYL6,CAPZB,DSTN,ACTN4,SLC7A11,INF2,CD2AP,PDLIM1,and FLNB were all upregulated in the 15 DRGs of HCC samples,and the differences were significant(t=1 793～6 310,all P<0.001).According to GO functional annotation and KEGG enrichment analysis,DRGs were closely related to biological processes or pathways related to actin cytoskeleton and cell adhesion.The results of KM survival analysis showed that the survival rate of the high expression group of SLC7A11,INF2,CD2AP,MYL6,and ACTB were lower than that of the low expression group[HR=1.46(1.03～2.07)～1.93(1.36～2.75),all P<0.05].Univariate COX regression analysis,LASSO analysis,and multivariate COX regression analysis were used to construct a prognostic risk model,with risk score=(0.247×SLC7A11)+(0.289×INF2)+(0.076×CD2AP)+(0.06×MYL6).The risk score of the sample in this model was calculated,and the higher the risk score,the more HCC patients with poor prognosis.KM survival analysis showed that the overall survival rate of the high-risk group was lower than that of the low-risk group.The AUC values for 1,3,and 5 years were 0.709,0.661,and 0.648,respectively.Multivariate COX regression analysis showed that SLC7A11[HR=1.832(1.274～2.636),P=0.001]was an independent prognostic risk factor.Conclusion The prognostic risk model was constructed by four DRGs,which has a certain role in predicting the prognosis of HCC patients.

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Background: One of the most prevalent hematological system cancers is acute myeloid leukemia (AML). Efferocytosis-related genes (ERGs) and N6-methyladenosine (m6A) have an important significance in the progression of cancer, and the metastasis of tumors. Methods: The AML-related data were collected from The Cancer Genome Atlas (TCGA; TCGA-AML) database and Gene Expression Omnibus (GEO; GSE9476, GSE71014, and GSE13159) database. The "limma" R package and Venn diagram were adopted to identify differentially expressed ERGs (DE-ERGs). The m6A related-DE-ERGs were obtained by Spearman analysis. Subsequently, univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) were used to construct an m6A related-ERGs risk signature for AML patients. The possibility of immunotherapy for AML was explored. The pRRophetic package was adopted to calculate the IC50 of drugs for the treatment of AML. Finally, the expression of characterized genes was validated by quantitative reverse transcription-PCR (qRT-PCR). Results: Based on m6A related-DE-ERGs, a prognostic model with four characteristic genes (UCP2, DOCK1, SLC14A1, and SLC25A1) was constructed. The risk score of model was significantly associated with the immune microenvironment of AML, with four immune cell types, 14 immune checkpoints, 20 HLA family genes and, immunophenoscore (IPS) all showing differences between the high- and low-risk groups. A total of 56 drugs were predicted to differ between the two groups, of which Erlotinib, Dasatinib, BI.2536, and bortezomib have been reported to be associated with AML treatment. The qRT-PCR results showed that the expression trends of DOCK1, SLC14A1 and SLC25A1 were consistent with the bioinformatics analysis. Conclusion: In summary, 4 m6A related- ERGs were identified and the corresponding prognostic model was constructed for AML patients. This prognostic model effectively stratified the risk of AML patients.

Asunto(s)

Neoplasias Hematológicas , Leucemia Mieloide Aguda , Humanos , Pronóstico , Genes Reguladores , Leucemia Mieloide Aguda/genética , Factores de Transcripción , Microambiente Tumoral

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Background: Lung adenocarcinoma (LUAD) is a group of cancers with poor prognosis. The combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) can identify important genes involved in cancer development and progression from a broader perspective. Methods: The scRNA-seq data and bulk RNA-seq data of LUAD were downloaded from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Analyzing scRNA-seq for core cells in the GSE131907 dataset, and the uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Macrophage polarization-associated subtypes were acquired from the TCGA-LUAD dataset after analysis, followed by further identification of differentially expressed genes (DEGs) in the TCGA-LUAD dataset (normal/LUAD tissue samples, two subtypes). Venn diagrams were utilized to visualize differentially expressed and highly variable macrophage polarization-related genes. Subsequently, a prognostic risk model for LUAD patients was constructed by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO), and the model was investigated for stability in the external data GSE72094. After analyzing the correlation between the trait genes and significantly mutated genes, the immune infiltration between the high/low-risk groups was then examined. The Monocle package was applied to analyze the pseudo-temporal trajectory analysis of different cell clusters in macrophage clusters. Subsequently, cell clusters of data macrophages were selected as key cell clusters to explore the role of characteristic genes in different cell populations and to identify transcription factors (TFs) that affect signature genes. Finally, qPCR were employed to validate the expression levels of prognosis signature genes in LUAD. Results: 424 macrophage highly variable genes, 3920 DEGs, and 9561 DEGs were obtained from macrophage clusters, the macrophage polarization-related subtypes, and normal/LUAD tissue samples, respectively. Twenty-eight differentially expressed and highly mutated MPRGs were obtained. A prognostic risk model with 7 DE-MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) was constructed. This prognostic model still has a good prediction effect in the GSE72094 dataset. ZNF536 and DNAH9 were mutated in the low-risk group, while COL11A1 was mutated in the high-risk group, and they were highly correlated with the characteristic genes. A total of 11 immune cells were significantly different in the high/low-risk groups. Five cell types were again identified in the macrophage cluster, and then NK cells: CD56hiCD62L+ differentiated earlier and were present mainly on 2 branches. While macrophages were present on 2 branches and differentiated later. It was found that the expression levels of BCLAF1 and MAX were higher in cluster 1, which might be the TFs affecting the expression of the characteristic genes. Moreover, qPCR confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis. Conclusion: Seven MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) were identified as prognostic genes for LUAD and revealed the mechanisms of MPRGs at the single-cell level.

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Telomere maintenance is critical to ensure unlimited cancer cell proliferation, but the role of telomere-related genes in acute myeloid leukemia (AML) has not yet been thoroughly discussed. This study aims to develop a new prognostic risk model based on telomere-related genes and analyze potential mechanisms and targets. Cox regression analyses were used to build the prognostic risk model. Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) curve were used to assess the model performance. At the same time, we analyzed the relationship between the risk score and chemotherapy and immunotherapy and preliminarily explored possible mechanisms of immune resistance. The real-time polymerase chain reaction (PCR) was used to detect the prognosis gene expression levels. Finally, a prognostic signature of six telomere-related genes (TGPS6) including ALDH2, CDK18, DNMT3B, FRAT2, LGALSL, and RBL2 was constructed. The TGPS6 score was confirmed as an independent prognostic factor (HR 2.74, CI [2.13-3.53], p < 0.001) in AML and the five-year area under the ROC curve (AUC) value of the score in the training and validation set reached 0.74, 0.81 respectively. In addition, the TGPS6 perfected the European LeukemiaNet (ELN) 2017 prognosis risk stratification and performed well in both AML and cytogenetically normal AML (CN-AML) cohorts. The TGPS6 score also provided a reference for chemotherapy and immunotherapy in patients with AML.

Asunto(s)

Leucemia Mieloide Aguda , Humanos , Pronóstico , Leucemia Mieloide Aguda/diagnóstico , Leucemia Mieloide Aguda/genética , Leucemia Mieloide Aguda/tratamiento farmacológico , Factores de Riesgo , Estimación de Kaplan-Meier , Curva ROC , Aldehído Deshidrogenasa Mitocondrial