Independent Prognostic Gene Research Articles

Efferocytosis-related gene IL33 predicts prognosis and immune response and mediates proliferation and migration in vitro and in vivo of breast cancer

BackgroundBreast cancer (BRCA) has a high incidence among women, with poor prognosis and high mortality, which is increasing year by year. Efferocytosis is a process of phagocytosis of abnormal cells and is of great value in tumor research. Our study seeks to create a predictive model for BRCA using efferocytosis-related genes (ERGs) to explore the significance of efferocytosis in this disease.MethodsIn this research, Differential analysis, and univariate Cox regression were employed to identify genes linked to prognosis in BRCA patients. Then the BRCA patients were categorized into distinct groups using consensus clustering based on prognosis genes. Survival analysis, PCA, and t-SNE were performed to verify these groups. The enrichment of metabolic pathways within the detected clusters was evaluated using gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). Additionally, single-sample GSEA (ssGSEA) was used to examine changes in immune infiltration and enrichment. A risk prognostic model was constructed utilizing multivariable Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analyses, and subsequently validated its predictive accuracy by stratifying patients according to the median risk score. Ultimately, some crucial independent prognostic genes were pinpointed and their expression, roles, and immune characteristics were explored in both laboratory and live models.ResultsFindings revealed 52 differentially expressed genes (DEGs), of which 21 were significantly linked to BRCA outcomes. These 21 genes were utilized for consensus clustering to categorize BRCA patients into two subtypes. Subtype B was linked to a worse prognosis compared to Subtype A, though both subtypes were distinguishable. The enriched pathways were mainly concentrated in Subtype A and were actively expressed in this group. Following this, a prognostic risk model was constructed using five risk genes, which was proven to possess significant predictive value. A significant link was identified between the immune microenvironment and the risk-associated genes and scores. IL33 was identified as an independent prognostic gene with important research value. Its in vivo expression results aligned with the data analysis findings, showing low expression in BRCA. Furthermore, overexpression of IL33 significantly inhibited BRCA growth and motility in vitro and in vivo, while also enhancing their vulnerability to destruction by activated CD8+ T cells.ConclusionThe ERG-based risk model effectively predicts the prognosis of BRCA patients and shows a strong link with the immune microenvironment. IL33 stands out as a significant prognostic marker, crucial in the onset and advancement of BRCA. This highlights the necessity for additional studies and indicates that IL33 might be a potential target for BRCA treatment.

Aberrant Expression of JAM2 Inhibits Invasion and Migration in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer. JAM2, a member of the Junctional adhesion molecule (JAM) family, plays diverse roles in cell-cell contacts and tumor development. Although JAM2's expression and functions have been reported in various cancers, its clinical and biological significance in LUAD remains unclear. The aim of this study was to investigate the expression and function of JAM2 in LUAD, and to assess its potential as a prognostic gene and a molecular target for early diagnosis and targeted therapy. Immunohistochemistry (IHC) was performed on 37 pairs of LUAD tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted among co-expression genes in different JAM2 subgroups. Invitro experiments were also conducted to study the migratory and invasive capabilities of LUAD cells when JAM2 was overexpressed. The study confirmed that JAM2 was downregulated in LUAD, possibly due to methylation. JAM2 emerged as an independent prognostic gene for predicting the outcomes of patients with LUAD. IHC analysis revealed the significance of JAM2 with clinicopathological parameters in LUAD. GO and KEGG analyses provided insights into the biological processes and pathways associated with JAM2. Invitro experiments showed that overexpressing JAM2 significantly suppressed the migratory and invasive capabilities of LUAD cells. Additionally, JAM2 played a crucial role in LUAD inflammatory infiltration, and higher JAM2 expression predicted a better immunotherapy response. JAM2 may serve as a promising molecular target for early diagnosis and targeted therapy of LUAD. Its downregulation in LUAD, potential role as a prognostic gene, and influence on cell migration, invasion, and inflammatory infiltration make it a valuable target for further research and development of therapeutic strategies.

Identification of the shared gene signatures in retinoblastoma and osteosarcoma by machine learning

Osteosarcoma (OS) is the most prevalent secondary sarcoma associated with retinoblastoma (RB). However, the molecular mechanisms driving the interactions between these two diseases remain incompletely understood. This study aims to explore the transcriptomic commonalities and molecular pathways shared by RB and OS, and to identify biomarkers that predict OS prognosis effectively. RNA sequences and patient information for OS and RB were obtained from the University of California Santa Cruz (UCSC) Xena and Gene Expression Omnibus databases. When RB and OS were first identified, a common gene expression profile was discovered. Weighted Gene Co-expression Network Analysis (WGCNA) revealed co-expression networks associated with OS after immunotyping patients. To evaluate the genes shared by RB and OS, univariate and multivariate Cox regression analysis were then carried out. Three machine learning methods were used to pick key genes, and risk models were created and verified. Next, medications that target independent prognostic genes were found using the Cellminer database. The comparison of differential gene expression between OS and RB revealed 1216 genes, primarily linked to the activation and proliferation of immune cells. WGCNA identified 12 modules related to OS immunotyping, with the grey module showing a strong correlation with the immune-inflamed phenotype. This module intersected with differential genes from RB, producing 65 RB-associated OS Immune-inflamed Genes (ROIGs). Analysis identified 6 hub genes for model construction through univariate Cox regression and three machine learning techniques. A risk model based on these hub genes was established, demonstrating significant prognostic value for OS. Genes shared between OS and RB contribute to the progression of both cancers through multiple pathways. The ROIGs risk score model independently predicts the overall survival of OS patients. Additionally, this study highlights genes with potential as therapeutic targets or biomarkers for clinical use.

Role of anoikis-related gene RAC3 in prognosis, immune microenvironment, and contribution to malignant behavior in vitro and in vivo of bladder urothelial carcinoma.

Anoikis disrupts the normal apoptotic process in cells, leading to abnormal proliferation and migration, thereby promoting tumor formation and development. However, the role of anoikis in bladder urothelial carcinoma (BLCA) still requires further exploration. Anoikis-related genes (ARGs) were retrieved from the GeneCards and Harmonizome databases to distinguish various subtypes of BLCA and develop a predictive model for BLCA. The immune microenvironment and enrichment pathways between various subtypes were also analyzed using consensus clustering. Potential medications were screened by utilizing drug sensitivity analysis. In vitro and vivo, the character of the independent prognostic gene in BLCA was confirmed through cell studies and mouse xenograft models. One hundred thirty differentially expressed genes (DEGs) were identified, and nine of them were chosen to construct predictive models that can accurately forecast the prognosis of BLCA patients. K = 2 was correctly identified as the optimal clustering type for BLCA, showing prominent differences in survival rates between the two subgroups. The immune-related functional studies manifested that the two subtypes' immune cell expressions differed. It was verified that RAC3 is an independent prognostic gene for BLCA. RAC3 shows high expression levels in BLCA, as indicated by its consistent mRNA and protein levels across different gene expressions. The functional verification results of RAC3 in BLCA showed that silencing RAC3 can significantly inhibit BLCA cell proliferation, colony formation, and migration. RAC3 knockdown inhibited the growth and migration of BLCA in vivo. SB505124 exhibited a significant inhibitory effect on the proliferation of BLCA cells. Based on the predictive model developed in this study, BLCA patients' prognoses can be accurately predicted. SB505124 could become an important drug in the treatment of BLCA patients. RAC3 is essential in prognosis, immune microenvironment, and malignant behavior of BLCA in vitro and in vivo. It will also offer the potential for personalized treatment for BLCA patients and generate new research avenues for clinical investigators.

Signature of immune-related metabolic genes predicts the prognosis of hepatocellular carcinoma.

The majority of liver cancer cases (90%) are attributed to hepatocellular carcinoma (HCC), which exhibits significant heterogeneity and an unfavorable prognosis. Modulating the immune response and metabolic processes play a crucial role in both the prevention and treatment of HCC. However, there is still a lack of comprehensive understanding regarding the immune-related metabolic genes that can accurately reflect the prognosis of HCC. In order to address this issue, we developed a prognostic prediction model based on immune and metabolic genes. To evaluate the accuracy of our model, we performed survival analyses including Kaplan-Meier (K-M) curve and time-dependent receiver operating characteristic (ROC) curve. Furthermore, we compared the predictive performance of our risk model with existing models. Finally, we validated the accuracy of our risk model using mouse models with in situ transplanted liver cancer. By conducting lasso regression analysis, we identified four independent prognostic genes: fatty acid binding protein 6 (FABP6), phosphoribosyl pyrophosphate amidotransferase (PPAT), spermine synthase (SMS), and dihydrodiol dehydrogenase (DHDH). Based on these findings, we constructed a prognostic model. Survival analysis revealed that the high-risk group had significantly lower overall survival (OS) rates. Besides that, the ROC curve demonstrated the effective prognostic capability of our risk model for hepatocellular carcinoma (HCC) patients. Furthermore, through animal experiments, we validated the accuracy of our model by showing a correlation between high-risk scores and poor prognosis in tumor development. In conclusion, our prognostic model surpasses those solely based on immune genes or metabolic genes in terms of accuracy. We observed variations in prognosis among different risk groups, accompanied by distinct immune and metabolic characteristics. Therefore, our model provides an original evaluation index for personalized clinical treatment strategies targeting HCC patients.

Construction of a novel mitochondrial oxidative stress-related genes prognostic system and molecular subtype characterization for breast cancer

PurposeBreast cancer (BRCA) is the most common malignant tumor among women, characterized by high incidence rates and mortality rates. Oxidative stress and immunity, particularly in relation to mitochondria, have emerged as pivotal factors in breast carcinogenesis. Nonetheless, limited research has explored the specific contribution of mitochondrial oxidative stress to the prognosis of BRCA.MethodIn this study, we conducted univariate and multivariate Cox regression analyses to pinpoint independent prognostic genes associated with mitochondrial oxidative stress (MOSRGs) and their correlation with BRCA clinical outcomes. Subsequently, we developed a robust and accurate MOS scoring system for BRCA patients based on these identified independent prognostic MOSRGs.ResultOur findings were further substantiated by immune infiltration and somatic mutation analyses, providing additional evidence that the MOS scoring system holds predictive value for clinical outcomes in patients and correlates directly with three subtypes of BRCA. In vitro experiments in the MCF7 cell and breast tissue further verified the mRNA and protein expression level of independent prognostic genes, validating the consistency of the MOS prognostic signatures in BRCA.ConclusionThis research has unveiled a novel prognostic scoring system, providing valuable insights for improving patient prognosis assessment and developing individualized treatment strategies in BRCA patients.

Comprehensive utilization of in silico approach and in vitro experiment to unveil the molecular mechanisms of mono (2-ethylhexyl) phthalate-induced lung adenocarcinoma

Mono (2-ethylhexyl) phthalate (MEHP), the main bioactive metabolite of commonly used plasticizer Di (2-ethylhexyl) phthalate, has received increasing attention due to its carcinogenic toxicity. This study aims to systematically explore the molecular mechanisms underlying MEHP-induced lung adenocarcinoma (LUAD). Firstly, network toxicology was employed to construct the interaction network of MEHP-targeted LUAD-related proteins and identify core proteins. Subsequently, functional analyses were used to determine the key pathways of these proteins enriched. Next, expression and survival analyses of multiple public datasets were conducted to emphasize the importance of core genes, and an optimized prognostic model was constructed based on independent prognostic genes to explore the relationship of gene risk with immune infiltration and immunotherapy. Ultimately, molecular docking and dynamics simulation were used to predict the binding modes and affinities of MEHP with core proteins, and surface plasmon resonance experiments were utilized to further validate their direct interactions. The findings demonstrated that MEHP targets 167 LUAD-related proteins, including 28 core target proteins. These proteins form the critical networks that regulate cancer and immune-associated pathways to induce the occurrence and development of LUAD, and further coordinate patient prognosis and treatment by altering the immune microenvironment. Most importantly, their direct interactions (especially PTGS2) lay the structural foundation of MEHP regulating core proteins, greatly supporting its LUAD toxicity. In conclusion, this study introduces a novel approach for evaluating the safety of plasticizers and elucidates the molecular mechanisms behind MEHP-induced LUAD, thus offering new and effective targets and strategies for cancer prevention and treatment.

Deciphering the role of sphingolipid metabolism in the immune microenvironment and prognosis of esophageal cancer via single-cell sequencing and bulk data analysis

BackgroundEsophageal squamous cell carcinoma (ESCC) stands as a significant global health challenge, distinguished by its aggressive progression from the esophageal epithelium. Central to this malignancy is sphingolipid metabolism, a critical pathway that governs key cellular processes, including apoptosis and immune regulation, thereby influencing tumor behavior. The advent of single-cell and transcriptome sequencing technologies has catalyzed significant advancements in oncology research, offering unprecedented insights into the molecular underpinnings of cancer.MethodsWe explored sphingolipid metabolism-related genes in ESCC using scRNA-seq data from GEO and transcriptome data from TCGA. We assessed 97 genes in epithelial cells with AUCell, UCell, and singscore algorithms, followed by bulk RNA-seq and differential analysis to identify prognosis-related genes. Immune infiltration and potential immunotherapeutic strategies were also investigated, and tumor gene mutations and drug treatment strategies were analyzed.ResultOur study identified distinct gene expression patterns, highlighting ARSD, CTSA, DEGS1, and PPTQ's roles in later cellular stages. We identified seven independent prognostic genes and created a precise nomogram for prognosis.ConclusionThis study integrates single-cell and transcriptomic data to provide a reliable prognostic model associated with sphingolipid metabolism and to inform immunotherapy and pharmacotherapy for ESCC at the genetic level. The findings have significant implications for precision therapy in esophageal cancer.

Identification of a new gene signature for prognostic evaluation in cervical cancer: based on cuproptosis-associated angiogenesis and multi-omics analysis

Patients with recurrent or metastatic cervical cancer are in urgent need of novel prognosis assessment or treatment approaches. In this study, a novel prognostic gene signature was discovered by utilizing cuproptosis-related angiogenesis (CuRA) gene scores obtained through weighted gene co-expression network analysis (WGCNA) of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. To enhance its reliability, the gene signature was refined by integrating supplementary clinical variables and subjected to cross-validation. Meanwhile, the activation of the VEGF pathway was inferred from an analysis of cell-to-cell communication, based on the expression of ligands and receptors in cell transcriptomic datasets. High-CuRA patients had less infiltration of CD8 + T cells and reduced expression of most of immune checkpoint genes, which indicated greater difficulty in immunotherapy. Lower IC50 values of imatinib, pazopanib, and sorafenib in the high-CuRA group revealed the potential value of these drugs. Finally, we verified an independent prognostic gene SFT2D1 was highly expressed in cervical cancer and positively correlated with the microvascular density. Knockdown of SFT2D1 significantly inhibited ability of the proliferation, migration, and invasive in cervical cancer cells. CuRA gene signature provided valuable insights into the prediction of prognosis and immune microenvironment of cervical cancer, which could help develop new strategies for individualized precision therapy for cervical cancer patients.

Integrated bioinformatics analysis and experimental validation to understand tryptophan metabolism-related genes in hepatocellular carcinoma.

Background: Tryptophan (Trp) metabolism is closely related to tumor immunity, and its disorder can cause an immunosuppressive microenvironment, promoting the occurrence and development of hepatocellular carcinoma (HCC). The aim of this study is to explore and validate the independent prognostic genes in patients suffered from HCC. Methods: The transcriptome data of GSE87630 from GEO database were downloaded to analyze differentially expressed genes (DECs) which were intersected with the gene sets of Trp metabolism from MsigDB database. Univariate/multivariate COX regression was performed to identify the genes with independent prognostic significance. TCGA, GTEx, UALCAN, and GEPIA2 databases were applied to analyze DEGs for prognosis. RNA seq data of HCC from TCGA database were collected for Lasso regression analysis. The ssGSEA algorithm was used to perform the analysis of TCGA data. The effects of the candidate differential gene on HCC cells proliferation and migration were evaluated using EdU immunofluorescence and transwell assays. Results: Trp metabolism-related DECs for HCCs were obtained, including MAOB, CYP1A2, KYNU, CYP2E1, ALDH2, CYP2C18, TDO2, AOX1, CYP3A4 and INMT. Moreover, multivariate COX regression results showed that ALDH2 can serve as an independent prognostic molecule and its transcriptional and translational levels were significantly reduced in the tumor tissues. The low expression of ALDH2 was associated with poor prognosis. Overexpression of ALDH2 dramatically reduced the HCC cells proliferation and migration. Conclusion: ALDH2 is associated with Trp metabolism and its downregulation in HCC has a potential value on prognosis. Overexpression of ALDH2 can reduce the proliferation and migration of HCC cells.

Anoikis-related gene CDKN2A predicts prognosis and immune response and mediates proliferation and migration in thyroid carcinoma

Thyroid carcinoma (THCA) is a tumor commonly occurring in the endocrine system, and its incidence rate is increasing yearly. Anoikis is a type of cell death involved in the carcinogenesis process. This study aimed to investigate the prognosis and immune correlations of anoikis in THCA. Our study used several bioinformatics algorithms (co-expression analysis, univariate and multivariate Cox analysis) to screen anoikis-related genes (ARGs) to construct risk models. Through receiver operating characteristic (ROC) curve, nomogram, and independent prognostic analysis found that the constructed model had ideal predictive value for THCA. The consensus clustering method was used to divide ARG patterns into three subgroups, and there were significant differences in survival among the three subgroups. The CIBERSORT algorithm demonstrated strong correlations among immune infiltrating cells, prognostic genes, and risk scores. Univariate and multivariate Cox analysis showed that CDKN2A is an independent prognostic gene. Basic experiments (immunohistochemistry, qRT-PCR, etc.) showed that the expression levels of CDKN2A mRNA and protein were highly expressed in THCA, which was consistent with the results of bioinformatics analysis. In vitro, the knockdown of CDKN2A significantly inhibited the proliferation and migration of THCA cells. In summary, our study utilized eight ARGs to construct an accurate risk model. ARGs, especially CDKN2A, play a crucial role in the occurrence and development of THCA and can become potential targets for treating THCA patients.

Integrative analysis reveals a four-gene signature for predicting survival and immunotherapy response in colon cancer patients using bulk and single-cell RNA-seq data.

Colon cancer (CC) ranks as one of the leading causes of cancer-related mortality globally. Single-cell transcriptome sequencing (scRNA-seq) offers precise gene expression data for distinct cell types. This study aimed to utilize scRNA-seq and bulk transcriptome sequencing (bulk RNA-seq) data from CC samples to develop a novel prognostic model. scRNA-seq data was downloaded from the GSE161277 database. R packages including "Seurat", "Harmony", and "singleR" were employed to categorize eight major cell types within normal and tumor tissues. By comparing tumor and normal samples, differentially expressed genes (DEGs) across these major cell types were identified. Gene Ontology (GO) enrichment analyses of DEGs for each cell type were conducted using "Metascape". DEGs-based signature construction involved Cox regression and least absolute shrinkage operator (LASSO) analyses, performed on The Cancer Genome Atlas (TCGA) training cohort. Validation occurred in the GSE39582 and GSE33382 datasets. The expression pattern of prognostic genes was verified using spatial transcriptome sequencing (ST-seq) data. Ultimately, an established prognostic nomogram based on the gene signature and age was established and calibrated. Sensitivity to chemotherapeutic drugs was predicted with the "oncoPredict" R package. Using scRNA-Seq data, we examined 33,213 cells, categorizing them into eight cell types within normal and tumor samples. GO enrichment analysis revealed various cancer-related pathways across DEGs in these cell types. Among the 55 DEGs identified via univariate Cox regression, four independent prognostic genes emerged: PTPN6, CXCL13, SPINK4, and NPDC1. Expression validation through ST-seq confirmed PTPN6 and CXCL13 predominance in immune cells, while SPINK4 and NPDC1 were relatively epithelial cell-specific. Creating a four-gene prognostic signature, Kaplan-Meier survival analyses emphasized higher risk scores correlating with unfavorable prognoses, confirmed across training and validation cohorts. The risk score emerged as an independent prognostic factor, supported by a reliable nomogram. Intriguingly, drug sensitivity analysis unveiled contrasting anti-cancer drug responses in the two risk groups, suggesting significant clinical implications. We developed a novel prognostic four-gene risk model, and these genes may act as potential therapeutic targets for CC.

Identification of a coagulation-related signature correlated with immune infiltration and their prognostic implications in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a fatal form of lung cancer with a poor prognosis. Coagulation system had been confirmed closely related to tumor progression and the hypercoagulable state encouraged the immune infiltration and development of tumor cells, leading to a poor prognosis in cancer patients. However, the use of the coagulation-related genes (CRGs) for prognosis in LUAD has yet to be determined. In this study, we constructed an immune-related signature (CRRS) and identified a potential coagulation-related biomarker (P2RX1). We obtained a total of 209 CRGs based on two coagulation-related KEGG pathways, then developed the CRRS signature by using the TCGA-LUAD RNA-seq data via the procedure of LASSO-Cox regression, stepwise-Cox regression, univariate and multivariate Cox regression. Grouped by the CRRS, Kaplan-Meier survival curves and receiver operating characteristic curves were drawn for the training and validation sets, respectively. In addition, single-sample gene set enrichment analysis was exploited to explore immune infiltration level. Moreover, immunophenotypes and immunotherapy grouped by CRRS were further analyzed. We developed an immune-related signature (CRRS) composed of COL1A2, F2, PLAUR, C4BPA, and P2RX1 in LUAD. CRRS was an independent risk factor for overall survival and displayed stable and powerful performance. Additionally, CRRS possessed distinctly superior accuracy than traditional clinical variables and molecular features. Functional analysis indicated that the differentially high expressed genes in the low-risk group significantly enriched in T cell and B cell receptor signaling pathways. The low-risk group was sensitive to anti-PD-1/PD-L1 immunotherapy and displayed abundant immune infiltration and immune checkpoint gene expression. Finally, we identified an independent prognostic gene P2RX1. Low expression of P2RX1 associated with poor overall survival and decreased immune infiltration. Our study revealed a significant correlation between CRRS and immune infiltration. CRRS could serve as a promising tool to improve the clinical outcomes for individual LUAD patients.

Predictive value of DNA methylation in the efficacy of chemotherapy for gastric cancer.

Gastric cancer (GC) is one of the most common causes of cancer-related death. Drug resistance in chemotherapy often occurs in patients with GC, leading to tumor recurrence and poor survival. DNA methylation is closely related to the development of cancer. To investigate the role of DNA methylation in chemotherapy resistance in GC patients, we conducted a comprehensive analysis using DNA methylation data and survival information obtained from The Cancer Genome Atlas. Univariate Cox analysis was performed to screen for differential DNA methylation of chemotherapy response in patients who did and did not receive chemotherapy. Multivariate Cox analysis was then performed to identify the independent prognostic genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were used to explore the biological function of the signature genes. Patients receiving adjuvant chemotherapy for GC survived longer. 308 differentially methylated genes were demonstrated to be associated with prognosis. Six genes were optimally chosed for establisehing the risk model, including C6orf222, CCNL1, CREBZF, GCKR, TFCP2, and VIPR2. It was constructed based on the DNA methylation levels of these six genes: risk score = 0.47123374*C6orf222 + 9.53554803*CCNL1 + 10.40234138* CREBZF + 0.07611856* GCKR + 18.87661557*TFCP2 - 0.46396254* VIPR2. According to the risk score, patients receiving chemotherapy were divided into high- and low-risk groups, and the prognosis of the two groups was compared. The high-risk group had a shorter survival; however, this association was not present in patients without chemotherapy. The accuracy and predictive efficacy of the risk score in predicting the 1-, 3-, and 5-year survival of patients was evaluated with the receiver operating characteristic curve. In patients receiving chemotherapy, the area under the curve of the risk score for 1-, 3-, and 5-year survival was 0.841, 0.72, and 0.734, respectively. In patients who did not receive chemotherapy, the area under the curve was 0.406, 0.585, and 0.585, respectively. A nomogram model was constructed based on the risk score and clinical indicators. The model showed good consistency in the predicted probabilities and actual probabilities. Gene Ontology functional enrichment of these candidate methylated genes showed the following molecular functions: RNA binding, protein binding, mRNA binding, and nucleic acid binding; that they were mediated mainly through the following cell components: nuclear speck, nucleoplasm, nucleus, catalytic step 2 spliceosome, and the transcription factor AP-1 complex; and that they were involved in the following biological processes: mRNA processing, mRNA splicing, and RNA polymerase II promoter transcription. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment results revealed that the signaling pathways mainly enriched were transcriptional misregulation in cancer, spliceosome, and the IL-17 signaling pathway. Our work identifies a six DNA methylated expression signature as a promising biomarker of chemo-resistance in GC, which provides new insights into the development of new strategies to overcome chemo-resistance in GC.

Exploring the methylation status of CFTR and PKIA genes as potential biomarkers for lung adenocarcinoma

BackgroundOne of the most prevalent cancers in the world is lung cancer, with adenocarcinoma (LUAD) making up a significant portion of cases. According to the National Cancer Institute (NCI), there are new cases and fatality rates per 100,000 individuals as follows: New instances of lung and bronchial cancer occur annually at a rate of 50.0 per 100,000 persons. The yearly death rate for men and women is 35.0 per 100,000. DNA methylation is one of the earliest discovered and widely studied epigenetic regulatory mechanisms, and its abnormality is closely related to the occurrence and development of cancer. However, the prognostic value of DNA methylation and LUAD needs to be further explored to improve the survival prediction of LUAD patients.MethodsThe transcriptome data and clinical data of LUAD were downloaded from TCGA and GEO databases, and the Illumina Human Methylation450 array (450k array) data were downloaded from the TCGA database. Firstly, the intersection of the expressed genes of the two databases is corrected, the differential analysis is performed, and the methylation data is evaluated by the MethylMix package to obtain differentially methylated genes. Independent prognostic genes were screened out using univariate and multivariate Cox regression analysis, and a methylation prognostic model was developed using univariate Cox analysis and validated with the GSE30219 dataset in the GEO database. Survival analysis between methylation high-risk and low-risk groups was performed and a methylation-based gene prognostic model was constructed. Finally, the prediction of potential drugs associated with the LUAD gene signature using Drug Sensitivity Genomics in Cancer (GDSC).ResultsIn this study, a total of 555 samples from the TCGA database and 307 samples from GSE30219 were included, and a total of 24 differential methylation driver genes were identified. Univariate and multivariate Cox regression analyzes were used to screen out independent prognostic genes, involving 2 genes: CFTR, PKIA. Survival analysis was different between the methylation high-risk group and the low-risk group, the CFTR high methylation group and the low methylation group were poor, and the opposite was true for PKIA.ConclusionsOur study revealed that the methylation status of CFTR and PKIA can serve as potential prognostic biomarkers and therapeutic targets in lung cancer.

M7G-related genes predict prognosis and affect the immune microenvironment and drug sensitivity in osteosarcoma.

Background: Osteosarcoma (OS), a primary malignant bone tumor, confronts therapeutic challenges rooted in multidrug resistance. Comprehensive understanding of disease occurrence and progression is imperative for advancing treatment strategies. m7G modification, an emerging post-transcriptional modification implicated in various diseases, may provide new insights to explore OS pathogenesis and progression. Methods: The m7G-related molecular landscape in OS was probed using diverse bioinformatics analyses, encompassing LASSO Cox regression, immune infiltration assessment, and drug sensitivity analysis. Furthermore, the therapeutic potential of AZD2014 for OS was investigated through cell apoptosis and cycle assays. Eventually, multivariate Cox analysis and experimental validations, were conducted to investigate the independent prognostic m7G-related genes. Results: A comprehensive m7G-related risk model incorporating eight signatures was established, with corresponding risk scores correlated with immune infiltration and drug sensitivity. Drug sensitivity analysis spotlighted AZD2014 as a potential therapeutic candidate for OS. Subsequent experiments corroborated AZD2014's capability to induce G1-phase cell cycle arrest and apoptosis in OS cells. Ultimately, multivariate Cox regression analysis unveiled the independent prognostic importance of CYFIP1 and EIF4A1, differential expressions of which were validated at histological and cytological levels. Conclusion: This study furnishes a profound understanding of the contribution of m7G-related genes to the pathogenesis of OS. The discerned therapeutic potential of AZD2014, in conjunction with the identification of CYFIP1 and EIF4A1 as independent risk factors, opens novel vistas for the treatment of OS.

Role of anoikis-related gene PLK1 in kidney renal papillary cell carcinoma: a bioinformatics analysis and preliminary verification on promoting proliferation and migration.

Background: Kidney renal papillary cell carcinoma (KIRP) is a rare malignancy with a very poor prognosis. Anoikis is a specific form of apoptosis involved in carcinogenesis, but the role of anoikis in KIRP has not been explored. Methods: Anoikis-related genes (ARGs) were obtained from the GeneCards database and Harmonizome database and were used to identify different subtypes of KIRP and construct a prognostic model of KIRP. In addition, we also explored the immune microenvironment and enrichment pathways among different subtypes by consensus clustering into different subtypes. Drug sensitivity analysis was used to screen for potential drugs. Finally, we verified the mRNA and protein expression of the independent prognostic gene PLK1 in patient tissues and various cells and further verified the changes in relevant prognostic functions after constructing a PLK1 stable knockdown model using ShRNA. Results: We identified 99 differentially expressed anoikis-related genes (DEGs) associated with KIRP survival, and selected 3 genes from them to construct a prognostic model, which can well predict the prognosis of KIRP patients. Consensus clustering divided KIRP into two subtypes, and there was a significant difference in survival rates between the two subtypes. Immune profiling revealed differing immune statuses between the two subtypes, and functional analysis reveals the differential activity of different functions in different subtypes. Drug sensitivity analysis screened out 15 highly sensitive drugs in the high-risk group and 11 highly sensitive drugs in the low-risk group. Univariate and multivariate Cox regression analysis confirmed that PLK1 was an independent prognostic factor in KIRP, and its mRNA and protein expression levels were consistent with gene differential expression levels, both of which were highly expressed in KIRP. Functional verification of PLK1 in KIRP revealed significant results. Specifically, silencing PLK1 inhibited cell proliferation, clonogenicity, and migration, which indicated that PLK1 plays an important role in the proliferation and migration of KIRP. Conclusion: The prognosis model constructed by ARGs in this study can accurately predict the prognosis of KIRP patients. ARGs, especially PLK1, play an important role in the development of KIRP. This research can help doctors provide individualized treatment plans for KIRP patients and provide researchers with new research ideas.

Mining of clinical and prognosis related genes in the tumor microenvironment of endometrial cancer: A field synopsis of observational study.

Endometrial cancer (EC) is the sixth most common malignant tumor in women worldwide, and its morbidity and mortality are on the rise. The purpose of this study was to explore potential tumor microenvironment (TME)-related biomarkers associated with the clinical features and prognosis of EC. The Estimating Stromal and Immune Cells in Malignancy Using Expression Data (ESTIMATE) algorithm was used to calculate TME immune and stromal scores of EC samples and to analyze the relationship between immune/stromal scores, clinical features, and prognosis. Heat maps and Venn maps were used to screen for differentially expressed genes (DEGs). The ESTIMATE algorithm revealed immune score was significantly correlated with overall survival and tumor grade in patients with EC. A total of 1448 DEGs were screened, of which 387 were intersecting genes. Gene Ontology (GO) analysis revealed that the biological processes (BP) related to intersecting genes mainly included T cell activation and regulation of lymphocyte activation. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the intersecting genes were closely related to immune-related signaling pathways. Thirty core genes with more than 7 nodes were identified using protein-protein interaction (PPI) analysis. Six independent prognostic genes of EC were identified using Kaplan-Meier survival analysis and multivariate Cox analysis, namely CD5, BATF, CACNA2D2, LTA, CD52, and NOL4, which are all immune-infiltrating genes that are closely related to clinical features. The current study identified 6 key genes closely related to immune infiltration in the TME of EC that predict clinical outcomes, which may provide new insights into novel prognostic biomarkers and immunotherapy for patients with EC.

Bioinformatic-based genetic characterizations of neural regulation in skin cutaneous melanoma.

Recent discoveries uncovered the complex cancer-nerve interactions in several cancer types including skin cutaneous melanoma (SKCM). However, the genetic characterization of neural regulation in SKCM is unclear. Transcriptomic expression data were collected from the TCGA and GTEx portal, and the differences in cancer-nerve crosstalk-associated gene expressions between normal skin and SKCM tissues were analyzed. The cBioPortal dataset was utilized to implement the gene mutation analysis. PPI analysis was performed using the STRING database. Functional enrichment analysis was analyzed by the R package clusterProfiler. K-M plotter, univariate, multivariate, and LASSO regression were used for prognostic analysis and verification. The GEPIA dataset was performed to analyze the association of gene expression with SKCM clinical stage. ssGSEA and GSCA datasets were used for immune cell infiltration analysis. GSEA was used to elucidate the significant function and pathway differences. A total of 66 cancer-nerve crosstalk-associated genes were identified, 60 of which were up- or downregulated in SKCM and KEGG analysis suggested that they are mainly enriched in the calcium signaling pathway, Ras signaling pathway, PI3K-Akt signaling pathway, and so on. A gene prognostic model including eight genes (GRIN3A, CCR2, CHRNA4, CSF1, NTN1, ADRB1, CHRNB4, and CHRNG) was built and verified by independent cohorts GSE59455 and GSE19234. A nomogram was constructed containing clinical characteristics and the above eight genes, and the AUCs of the 1-, 3-, and 5-year ROC were 0.850, 0.811, and 0.792, respectively. Expression of CCR2, GRIN3A, and CSF1 was associated with SKCM clinical stages. There existed broad and strong correlations of the prognostic gene set with immune infiltration and immune checkpoint genes. CHRNA4 and CHRNG were independent poor prognostic genes, and multiple metabolic pathways were enriched in high CHRNA4 expression cells. Comprehensive bioinformatics analysis of cancer-nerve crosstalk-associated genes in SKCM was performed, and an effective prognostic model was constructed based on clinical characteristics and eight genes (GRIN3A, CCR2, CHRNA4, CSF1, NTN1, ADRB1, CHRNB4, and CHRNG), which were widely related to clinical stages and immunological features. Our work may be helpful for further investigation in the molecular mechanisms correlated with neural regulation in SKCM, and in searching new therapeutic targets.

A prognostic signature associated with cell senescence predicts survival outcomes and strongly associates with immunotherapy and chemotherapy response in breast cancer.

The objective of this study is to assess the predictive potency of cell senescence-related genes (CSRGs) in breast cancer (BC) and establish a risk signature. Trascriptome data of CSRGs were obtained from the TCGA and GEO databases. Consensus clustering was used to generate CSRGs-based molecular clusters for BC patients. A CSRGs-derived risk signature was built using multiple Cox regression analyses of differentially expressed genes (DEGs) between clusters. The prognosis, immune infiltration, chemotherapy and immunotherapy response between different risk groups were analyzed and compared. Two molecular clusters of BC patients were generated on the basis of 79 differentially expressed CSRGs, which showed distinct prognosis and immune infiltration. A total of 1403 DEGs between the CSRGs-derived clusters were found, and 10 of them were independent prognostic genes that used to construct a risk signature. The results demonstrated that patients with older age and advanced stage presented with a higher risk scores. In addition, the risk signature was found to be associated with outcomes, immune infiltration, chemotherapy and immunotherapy response. Patients in the low-risk group showed a favorable prognosis and higher immunotherapy response than those in the high-risk group. Finally, we developed a highly stable nomogram that incorporates risk signature, chemotherapy, radiotherapy, and stage variables, enabling accurate prediction of the overall survival (OS) of individual patients. To conclude, the signature derived from CSRGs holds great promise as a biomarker for prognostic assessment of BC and may serve as a valuable tool in guiding immunotherapy.