Extent Of Immune Cell Infiltration Research Articles

Potential diagnostic biomarkers and Mapk14 protein expression: Autophagy-related genes linking immune infiltration in acute respiratory distress syndrome

The pathogenesis of this condition is intricate, characterized by the aberrant activation of numerous cytokines and signaling pathways. This study aimed to delve into the association between the expression of the MAPK14 protein and immune cell infiltration in patients suffering from Acute Respiratory Distress Syndrome (ARDS). Additionally, it sought to assess the viability of autophagy-related genes as potential diagnostic biomarkers. To achieve this, the researchers employed various techniques such as immunohistochemistry, real-time quantitative PCR, and western blotting to measure the MAPK14 protein levels in the lung tissues of ARDS patients. These measurements were then correlated with clinical data to provide a comprehensive analysis.In this study, the researchers conducted a gene expression profile analysis to identify genes associated with autophagy. The relationship between these genes, MAPK14 expression, and immune cell infiltration was thoroughly evaluated. The findings revealed a marked increase in the expression of MAPK14 protein in the lung tissues of ARDS patients. This increased expression was found to be positively correlated with the extent of immune cell infiltration. The study's further analysis highlighted that several genes associated with autophagy exhibited expression levels that were correlated with both MAPK14 expression and the degree of immune infiltration. This suggests a complex interplay between MAPK14 protein levels, autophagy-related genes, and immune responses in the pathogenesis of ARDS. The results underscore the potential of these molecular markers in understanding the disease mechanisms and possibly aiding in the diagnosis and treatment of ARDS.

The potential of cancer stem cells for personalized risk assessment and therapeutic intervention in individuals with intrahepatic cholangiocarcinoma

BackgroundAccumulating evidence suggests that intrahepatic cholangiocarcinoma (ICC) is a stem cell-based disease, but information on the biology of cancer stem cells (CSC) in ICC is very limited.MethodsICC RNA-seq cohorts from three different public databases were integrated and the protein-coding genes were divided into different modules using "WGCNA" to screen the most relevant modules with CSC scores. Least Absolute Shrinkage and Selection Operator (LASSO) regression were introduced to construct prognostic classification models. In addition, the extent of immune cell infiltration in patients in different risk groups was assessed based on the ESTIMATE, CIBERSORT, MCP-Counter, and single sample gene set enrichment analysis (ssGSEA) algorithms. Finally, the correlation between different risk scores and common drugs was analyzed by pRRophetic package and Spearman method.ResultsIn the present study, we found that a high CSC score was associated with a poorer prognosis in patients with ICC. The yellow module obtained by WGCNA was significantly positively correlated with the CSCs score, in which 8 genes were served to build a prognostic classification model, and the obtained risk score was negatively correlated with CSCs score and prognosis. The low-risk score was more suitable for immunotherapy, and the high-risk score was more suitable for treatment with 11 antitumor drugs.ConclusionThis study revealed the regulatory role of CSC-mediated EMT, angiogenesis, and immunomodulatory biological processes in ICC, and applied a prognostic classification model to highlight the great potential of CSC for personalized risk assessment, chemotherapy, and immunotherapy intervention in ICC individuals.

Identifying subtypes and developing prognostic models based on N6-methyladenosine and immune microenvironment related genes in breast cancer

Breast cancer (BC) is the most prevalent cancer in women globally. The tumor microenvironment (TME), comprising epithelial tumor cells and stromal elements, is vital for breast tumor development. N6-methyladenosine (m6A) modification plays a key role in RNA metabolism, influencing its various aspects such as stability and translation. There is a notable link between m6A methylation and immune cells in the TME, although this relationship is complex and not fully deciphered. In this research, BC expression and clinicopathological data from TCGA were scrutinized to assess expression profiles, mutations, and CNVs of 31 m6A genes and immune microenvironment-related genes, examining their correlations, functions, and prognostic impacts. Lasso and Cox regression identified prognostic genes for constructing a nomogram. Single-cell analyses mapped the distribution and patterns of these genes in BC cell development. We investigated associations between gene-derived risk scores and factors like immune infiltration, TME, checkpoints, TMB, CSC indices, and drug response. As a complement to computational analyses, in vitro experiments were conducted to confirm these expression patterns. We included 31 m6A regulatory genes and discovered a correlation between these genes and the extent of immune cell infiltration. Subsequently, a 7-gene risk score was generated, encompassing HSPA2, TAP1, ULBP2, CXCL1, RBP1, STC2, and FLT3. It was observed that the low-risk group exhibited better overall survival (OS) in BC, with higher immune scores but lower tumor mutational burden (TMB) and cancer stem cell (CSC) indices, as well as lower IC50 values for commonly used drugs. To enhance clinical applicability, age and stage were incorporated into the risk score, and a more comprehensive nomogram was constructed to predict OS. This nomogram was validated and demonstrated good predictive performance, with area under the curve (AUC) values for 1-year, 3-year, and 5-year OS being 0.848, 0.807, and 0.759, respectively. Our findings highlight the profound impact of prognostic-related genes on BC immune response and prognostic outcomes, suggesting that modulation of the m6A-immune pathway could offer new avenues for personalized BC treatment and potentially improve clinical outcomes.

Identification of potential novel N6-methyladenosine effector-related lncRNA biomarkers for serous ovarian carcinoma: a machine learning-based exploration in the framework of 3P medicine.

Serous ovarian carcinoma (SOC) is considered the most lethal gynecological malignancy. The current lack of reliable prognostic biomarkers for SOC reduces the efficacy of predictive, preventive, and personalized medicine (PPPM/3PM) in patients with SOC, leading to unsatisfactory therapeutic outcomes. N6-methyladenosine (m6A) modification-associated long noncoding RNAs (lncRNAs) are effective predictors of SOC. In this study, an effective risk prediction model for SOC was constructed based on m6A modification-associated lncRNAs. Transcriptomic data and clinical information of patients with SOC were downloaded from The Cancer Genome Atlas. Candidate lncRNAs were identified using univariate and multivariate and least absolute shrinkage and selection operator-penalized Cox regression analyses. The molecular mechanisms of m6A effector-related lncRNAs were explored via Gene Ontology, pathway analysis, gene set enrichment analysis, and gene set variation analysis (GSVA). The extent of immune cell infiltration was assessed using various algorithms, including CIBERSORT, Microenvironment Cell Populations counter, xCell, European Prospective Investigation into Cancer and Nutrition, and GSVA. The calcPhenotype algorithm was used to predict responses to the drugs commonly used in ovarian carcinoma therapy. In vitro experiments, such as migration and invasion Transwell assays, wound healing assays, and dot blot assays, were conducted to elucidate the functional roles of candidate lncRNAs. Six m6A effector-related lncRNAs that were markedly associated with prognosis were used to establish an m6A effector-related lncRNA risk model (m6A-LRM) for SOC. Immune microenvironment analysis suggested that the high-risk group exhibited a proinflammatory state and displayed increased sensitivity to immunotherapy. A nomogram was constructed with the m6A effector-related lncRNAs to assess the prognostic value of the model. Sixteen drugs potentially targeting m6A effector-related lncRNAs were identified. Furthermore, we developed an online web application for clinicians and researchers (https://leley.shinyapps.io/OC_m6A_lnc/). Overexpression of the lncRNA RP11-508M8.1 promoted SOC cell migration and invasion. METTL3 is an upstream regulator of RP11-508M8.1. The preliminary regulatory axis METTL3/m6A/RP11-508M8.1/hsa-miR-1270/ARSD underlying SOC was identified via a combination of in vitro and bioinformatic analyses. In this study, we propose an innovative prognostic risk model and provide novel insights into the mechanism underlying the role of m6A-related lncRNAs in SOC. Incorporating the m6A-LRM into PPPM may help identify high-risk patients and personalize treatment as early as possible.

WDR4 as a potential indicator of clinical prognosis and immunotherapy in hepatocellular carcinoma.

e16275 Background: Immunotherapy has garnered increasing attention in hepatocellular carcinoma (HCC) treatment. However, there remains a pressing need for effective immunological predictive biomarkers. In our previous study, utilizing data extracted from The Cancer Genome Atlas (TCGA) database, we identified a positive correlation between WDR4 expression levels in hepatocellular carcinoma and various clinical parameters, encompassing Stage, T-stage, pathological grade, AFP level, and vascular invasion. Additionally, higher WDR4 expression was linked to poorer prognosis. Leveraging The TIMER and GEPIA databases, we further elucidated a significant association between WDR4 expression levels and the extent of immune cell infiltration, particularly macrophages, B lymphocytes, and myeloid suppressor cells, in hepatocellular carcinoma. These findings propose the potential utility of WDR4 as a biomarker for immune infiltration in hepatocellular carcinoma. As noted in the trial NCT03867084, which hypothesized that adjuvant pembrolizumab might surpass placebo efficacy, there still exists a gap in identifying the specific population benefiting from adjuvant immunotherapy. Methods: Clinical and pathological data from patients who underwent single-agent immunotherapy for HCC were additionally collected from Hunan Provincial People's Hospital between January 1, 2019, and January 31, 2024. These patients participated in the clinical trial (NCT03867084) with informed consent. The relationship between WDR4 expression levels, clinical pathological data, and patient prognosis was assessed using the Kruskal-Wallis test and Kaplan-Meier survival curve analysis. Results: WDR4 displayed significant upregulation in HCC tissues compared to para-carcinoma tissues (P < 0.001) and exhibited robust diagnostic potential. WDR4 expression showed significant associations with various immune cells, including macrophages (r = 0.278, P < 0.001). Kaplan-Meier survival analysis indicated that patients with high WDR4 expression had shorter postoperative progression-free survival in the context of immunotherapy. Data from 37 patients who underwent postoperative single-agent immunotherapy for hepatocellular carcinoma (NCT03867084) revealed a significant correlation between WDR4 expression levels and Disease-Free Survival (DFS), with notable statistical significance (Log-rank P < 0.001). Conclusions: WDR4 demonstrates elevated expression levels within HCC tissues and is associated with immune infiltration, establishing it as a prognostic biomarker in HCC. Furthermore, the positive correlation observed between WDR4 and CD68 as well as PD-L1 underscores its potential as a guiding factor in immunotherapeutic approaches for HCC.

Immunotherapy-relevance of a candidate prognostic score for Acute Myeloid Leukemia

BackgroundAcute Myeloid Leukemia (AML) exhibits a wide array of phenotypic manifestations, progression patterns, and heterogeneous responses to immunotherapies, suggesting involvement of complex immunobiological mechanisms. This investigation aimed to develop an integrated prognostic model for AML by incorporating cancer driver genes, along with clinical and phenotypic characteristics of the disease, and to assess its implications for immunotherapy responsiveness. MethodsCritical oncogenic driver genes linked to survival were identified by screening primary effector and corresponding gene pairs using data from The Cancer Genome Atlas (TCGA), through univariate Cox proportional hazard regression analysis. This was independently verified using dataset GSE37642. Primary effector genes were further refined using LASSO regression. Transcriptomic profiling was quantified using multivariate Cox regression, and the derived prognostic score was subsequently validated. Finally, a multivariate Cox regression model was developed, incorporating the transcriptomic score along with clinical parameters such as age, gender, and French-American-British (FAB) classification subtype. The ‘Accurate Prediction Model of AML Overall Survival Score’ (APMAO) was developed and subsequently validated. Investigations were conducted into functional pathway enrichment, alterations in the gene mutational landscape, and the extent of immune cell infiltration associated with varying APMAO scores. To further investigate the potential of APMAO scores as a predictive biomarker for responsiveness to cancer immunotherapy, we conducted a series of analyses. These included examining the expression profiles of genes related to immune checkpoints, the interferon-gamma signaling pathway, and m6A regulation. Additionally, we explored the relationship between these gene expression patterns and the Tumor Immune Dysfunction and Exclusion (TIDE) dysfunction scores. ResultsThrough the screening of 95 cancer genes associated with survival and 313 interacting gene pairs, seven genes (ACSL6, MAP3K1, CHIC2, HIP1, PTPN6, TFEB, and DAXX) were identified, leading to the derivation of a transcriptional score. Age and the transcriptional score were significant predictors in Cox regression analysis and were integral to the development of the final APMAO model, which exhibited an AUC greater than 0.75 and was successfully validated. Notable differences were observed in the distribution of the transcriptional score, age, cytogenetic risk categories, and French-American-British (FAB) classification between high and low APMAO groups. Samples with high APMAO scores demonstrated significantly higher mutation rates and pathway enrichments in NFKB, TNF, JAK-STAT, and NOTCH signaling. Additionally, variations in immune cell infiltration and immune checkpoint expression, activation of the interferon-γ pathway, and expression of m6A regulators were noted, including a negative correlation between CD160, m6A expression, and APMAO scores. ConclusionThe combined APMAO score integrating transcriptional and clinical parameters demonstrated robust prognostic performance in predicting AML survival outcomes. It was linked to unique phenotypic characteristics, distinctive immune and mutational profiles, and patterns of expression for markers related to immunotherapy sensitivity. These observations suggest the potential for facilitating precision immunotherapy and advocate for its exploration in upcoming clinical trials.

Identification of CNKSR1 as a biomarker for “cold” tumor microenvironment in lung adenocarcinoma: An integrative analysis based on a novel workflow

BackgroundTherapies targeting PD1/PD-L1 pathway have revolutionized the treatment of lung cancer. However, anti-PD1/PD-L1 therapies have proven beneficial for only a select group of lung adenocarcinoma (LUAD) patients and generally do not work for immuno-cold tumors characterized by a lack of immune cell infiltration. Identifying novel biomarkers is vital to broad therapeutic options for LUAD patients with no response to anti-PD1/PD-L1 immunotherapies. MethodsOur study has developed a novel strategy to identify a promising biomarker that addresses the limitations of anti-PD1/PD-L1 immunotherapy in treating immunological cold tumors. We exacted LUAD RNA-seq data from the Cancer Genome Atlas database (TCGA). Using several machine learning methods, we identified the candidate biomarker. Based on the expression level of PD-L1 and the identified biomarker, samples were categorized into four groups. We further used ESTIMATE, ssGSEA, and CIBERSORT algorithms to calculate the immune infiltration level of each group. The results were validated in three independent bulk datasets and one scRNA-seq dataset. Immunohistochemistry (IHC) assessments were performed in clinical samples to further evaluate the coexpression of CNKSR1 and PD-L1, and to compare CD8 + T cell infiltration among groups. ResultsAfter comprehensive analyses, CNKSR1 was identified as a novel promising biomarker for immuno-cold LUAD. CNKSR1 mRNA expression levels exhibited a negative correlation with both PD-L1 mRNA expression and the extent of immune cell infiltration in LUAD. Besides, in contrast to the significant association between the expression of PD-L1 and the majority of other well-established or widely studied immune checkpoint molecules, a mutually exclusive expression pattern is observed between CNKSR1 and these molecules. The aforementioned results were consistent in validation datasets. The prognostic model built based on the CNKSR1 coexpression module also showed robust predictive performance. Additionally, IHC assessments have confirmed that the coexpression of CNKSR1 and PD-L1 is rare in LUAD samples. Notably, LUADs in the high-CNKSR1 group, characterized by high CNKSR1 but low PD- L1 expression, demonstrated reduced infiltration of CD8+ T cells. ConclusionsIn summary, CNKSR1 emerges as a promising biomarker for immune-cold LUADs, and the study into CNKSR1 modulating T-cell infiltration may lead to the identification of compensatory molecules to enhance the effectiveness of current immunotherapy for LUAD.

Exploring a novel seven-gene marker and mitochondrial gene TMEM38A for predicting cervical cancer radiotherapy sensitivity using machine learning algorithms.

Radiotherapy plays a crucial role in the management of Cervical cancer (CC), as the development of resistance by cancer cells to radiotherapeutic interventions is a significant factor contributing to treatment failure in patients. However, the specific mechanisms that contribute to this resistance remain unclear. Currently, molecular targeted therapy, including mitochondrial genes, has emerged as a new approach in treating different types of cancers, gaining significant attention as an area of research in addressing the challenge of radiotherapy resistance in cancer. The present study employed a rigorous screening methodology within the TCGA database to identify a cohort of patients diagnosed with CC who had received radiotherapy treatment. The control group consisted of individuals who demonstrated disease stability or progression after undergoing radiotherapy. In contrast, the treatment group consisted of patients who experienced complete or partial remission following radiotherapy. Following this, we identified and examined the differentially expressed genes (DEGs) in the two cohorts. Subsequently, we conducted additional analyses to refine the set of excluded DEGs by employing the least absolute shrinkage and selection operator regression and random forest techniques. Additionally, a comprehensive analysis was conducted in order to evaluate the potential correlation between the expression of core genes and the extent of immune cell infiltration in patients diagnosed with CC. The mitochondrial-associated genes were obtained from the MITOCARTA 3.0. Finally, the verification of increased expression of the mitochondrial gene TMEM38A in individuals with CC exhibiting sensitivity to radiotherapy was conducted using reverse transcription quantitative polymerase chain reaction and immunohistochemistry assays. This process ultimately led to the identification of 7 crucial genes, viz., GJA3, TMEM38A, ID4, CDHR1, SLC10A4, KCNG1, and HMGCS2, which were strongly associated with radiotherapy sensitivity. The enrichment analysis has unveiled a significant association between these 7 crucial genes and prominent signaling pathways, such as the p53 signaling pathway, KRAS signaling pathway, and PI3K/AKT/MTOR pathway. By utilizing these 7 core genes, an unsupervised clustering analysis was conducted on patients with CC, resulting in the categorization of patients into three distinct molecular subtypes. In addition, a predictive model for the sensitivity of CC radiotherapy was developed using a neural network approach, utilizing the expression levels of these 7 core genes. Moreover, the CellMiner database was utilized to predict drugs that are closely linked to these 7 core genes, which could potentially act as crucial agents in overcoming radiotherapy resistance in CC. To summarize, the genes GJA3, TMEM38A, ID4, CDHR1, SLC10A4, KCNG1, and HMGCS2 were found to be closely correlated with the sensitivity of CC to radiotherapy. Notably, TMEM38A, a mitochondrial gene, exhibited the highest degree of correlation, indicating its potential as a crucial biomarker for the modulation of radiotherapy sensitivity in CC.

Based on Weighted Gene Co-Expression Network Analysis Reveals the Hub Immune Infiltration-Related Genes Associated with Ulcerative Colitis.

Immune infiltration plays a pivotal role in the pathogenesis of mucosal damage in ulcerative colitis (UC). The objective of this study was to systematically analyze and identify genetic characteristics associated with immune infiltration in UC. Gene expression data from three independent datasets obtained from the Gene Expression Omnibus (GEO) were utilized. By employing the ssGSEA and CIBERSORT algorithms, we estimated the extent of immune cell infiltration in UC samples. Subsequently, Weighted Correlation Network Analysis (WGCNA) was performed to identify gene modules exhibiting significant associations with immune infiltration, and further identification of hub genes associated with immune infiltration was accomplished using least absolute shrinkage and selection operator (LASSO) regression analysis. The relationship between the identified hub genes and clinical information was subsequently investigated. Our findings revealed significant activation of both innate and adaptive immune cells in UC. Notably, the expression levels of CD44, IL1B, LYN, and ITGA5 displayed strong correlations with immune cell infiltration within the mucosa of UC patients. Immunohistochemical analysis confirmed the significant upregulation of CD44, LYN, and ITGA5 in UC samples, and their expression levels were found to be significantly associated with common inflammatory markers, including the systemic immune inflammation indices, C-reactive protein, and erythrocyte sedimentation rate. CD44, LYN, and ITGA5 are involved in the immune infiltration pathogenesis of UC and may be potential therapeutic targets for UC.

Unraveling immunotherapeutic targets for endometriosis: a transcriptomic and single-cell analysis.

Endometriosis (EMs), a common gynecological disorder, adversely affects the quality of life of females. The pathogenesis of EMs has not been elucidated and the diagnostic methods for EMs have limitations. This study aimed to identify potential molecular biomarkers for the diagnosis and treatment ofEMs. Differential gene expression (DEG) and functional enrichment analyses were performed using the R language. WGCNA, Random Forest, SVM-REF and LASSO methods were used to identify core immune genes. The CIBERSORT algorithm was then used to analyse the differences in immune cell infiltration and to explore the correlation between immune cells and core genes. In addition, the extent of immune cell infiltration and the expression of immune core genes were investigated using single-cell RNA (scRNA) sequencing data. Finally, we performed molecular docking of three core genes with dienogest and goserelin to screen for potential drug targets. DEGs enriched in immune response, angiogenesis and estrogen processes. CXCL12, ROBO3 and SCG2 were identified as core immune genes. RT-PCR confirmed that the expression of CXCL12 and SCG2 was significantly upregulated in 12Z cells compared to hESCs cells. ROC curves showed high diagnostic value for these genes. Abnormal immune cell distribution, particularly increased macrophages, was observed in endometriosis. CXCL12, ROBO3 and SCG2 correlated with immune cell levels. Molecular docking suggested their potential as drug targets. This study investigated the correlation between EMs and the immune system and identified potential immune-related biomarkers. These findings provided valuable insights for developing clinically relevant diagnostic and therapeutic strategies for EMs.

Machine-learning algorithm-based prediction of a diagnostic model based on oxidative stress-related genes involved in immune infiltration in diabetic nephropathy patients.

Diabetic nephropathy (DN) is the most prevalent microvascular consequence of diabetes and has recently risen to the position of the world's second biggest cause of end-stage renal diseases. Growing studies suggest that oxidative stress (OS) responses are connected to the advancement of DN. This study aimed to developed a novel diagnostic model based on OS-related genes. The differentially expressed oxidative stress-related genes (DE-OSRGs) experiments required two human gene expression datasets, which were given by the GEO database (GSE30528 and GSE96804, respectively). The potential diagnostic genes were identified using the SVM-RFE assays and the LASSO regression model. CIBERSORT was used to determine the compositional patterns of the 22 different kinds of immune cell fraction seen in DN. These estimates were based on the combined cohorts. DN serum samples and normal samples were both subjected to RT-PCR in order to investigate the degree to which certain genes were expressed. In this study, we were able to locate 774 DE-OSRGs in DN. The three marker genes (DUSP1, PRDX6 and S100A8) were discovered via machine learning on two different machines. The high diagnostic value was validated by ROC tests, which focused on distinguishing DN samples from normal samples. The results of the CIBERSORT study suggested that DUSP1, PRDX6, and S100A8 may be associated to the alterations that occur in the immunological microenvironment of DN patients. Besides, the results of RT-PCR indicated that the expression of DUSP1, PRDX6, and S100A8 was much lower in DN serum samples compared normal serum samples. The diagnostic value of the proposed model was likewise verified in our cohort, with an area under the curve of 9.946. Overall, DUSP1, PRDX6, and S100A8 were identified to be the three diagnostic characteristic genes of DN. It's possible that combining these genes will be effective in diagnosing DN and determining the extent of immune cell infiltration.

MCM10 as a novel prognostic biomarker and its relevance to immune infiltration in gliomas.

Gliomas are one of the most common malignancies in the central nervous system (CNS). Members of the minichromosomal maintenance protein (MCM) family play an essential role in diagnosing and prognosis of malignant tumors. MCM10 is found in gliomas, but the prognosis and immune infiltration of gliomas has not been elucidated. To explore the biological function and immune infiltration of MCM10 in gliomas and provide a reference for the diagnosis, treatment, and prognostic evaluation. The MCM10 expression profile and the clinical information database of glioma patients were obtained from the China Glioma Genome Atlas (CGGA) and Cancer Genome Atlas (TCGA) glioma data. We analyzed the MCM10 expression levels in various cancers from The TCGA.RNA sequencing data were analyzed using the R packages to determine differentially expressed genes (DEGs) between high- and low MCM10 expressing GBM tissues from the TCGA-GBM database. The Wilcoxon rank sum test was used to compare MCM10 expression levels in glioma and normal brain tissue. To evaluate the value of MCM10 expressions in the prognosis of glioma patients by the Kaplan-Meier survival analysis, a univariate Cox analysis, multivariate Cox analysis, and a ROC curve analysis were used to analyze the correlation of MCM10 expression and the clinicopathological features of glioma patients using the TCGA database data. Subsequently, a functional enrichment analysis was performed to explore its potential signaling pathways and biological functions. Moreover, a single-sample gene set enrichment analysis was used to assess the extent of immune cell infiltration. Lastly, the authors constructed a nomogram to predict the overall survival rate (OS) of gliomas at 1, 3 and 5 years after diagnosis. MCM10 is highly expressed in 20 cancer types including gliomas, and MCM10 expression was an independent adverse prognostic factor in glioma patients. Similarly, high expression of MCM10 was associated with advanced age (60 years), increased tumor grade, tumor recurrence or development of a secondary tumor, IDH wild-type, and non-codeletion of 1p19q (p< 0.01). The OS nomogram generated a consistency index of 0.821. The results of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology (GO) functional analysis showed that the cell-cycle-related and tumor-related signaling pathways were significantly enriched in the MCM10 high expression phenotype. Moreover, signaling pathways were significantly enriched in Gene Set Enrichment Analysis (GSEA), including Rho GTPases, M phase, DNA repair, extracellular matrix organization, and nuclear receptors. Furthermore, MCM10 over expression was negatively correlated with the level of immune cell infiltration in natural killer CD56 bright cells, follicular helper T cells, plasmacytoma dendritic cells, and dendritic cells. MCM10 is an independent prognostic index of glioma patients, and the high expression of MCM10 suggests a poor prognosis; MCM10 expression is closely related to the immune cell infiltration of gliomas, and MCM10 may be related to drug resistance and development of gliomas.

M1A and m7G modification-related genes are potential biomarkers for survival prognosis and for deciphering the tumor immune microenvironment in esophageal squamous cell carcinoma

BackgroundEsophageal squamous cell carcinoma (ESCC) is the most common esophageal malignancy, and RNA methylation has been reported to be involved in the tumorigenesis of ESCC. However, no study has explored methylation modifications in m1A and m7G as prognostic markers for survival prediction in ESCC.MethodsPublic gene-expression data and clinical annotation of 254 patients obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases were analyzed to identify potential consensus clusters of m1A and m7G modification-related genes. The RNA-seq of 20 patients in Sun Yat-Sen University Cancer Center was used as the validation set. Following screening for relevant differentially expressed genes (DEGs) and enrichment pathways were elucidated. DEGs were used to construct risk models using the randomForest algorithm, and the prognostic role of the models was assessed by applying Kaplan–Meier analysis. Extent of immune cell infiltration, drug resistance, and response to cancer treatment among different clusters and risk groups were also evaluated.ResultsConsensus clustering analysis based on m1A and m7G modification patterns revealed three potential clusters. In total, 212 RNA methylation-related DEGs were identified. The methylation-associated signature consisting of 6 genes was then constructed to calculate methylation-related score (MRScore) and patients were dived into MRScore-high and MRScore-low groups. This signature has satisfied prognostic value for survival of ESCC (AUC = 0.66, 0.67, 0.64 for 2-, 3-, 4- year OS), and has satisfied performance in the validation SYSUCC cohort (AUC = 0.66 for 2- and 3-year OS). Significant correlation between m1A and m7G modification-related genes and immune cell infiltration, and drug resistance was also observed.ConclusionsTranscriptomic prognostic signatures based on m1A and m7G modification-related genes are closely associated with immune cell infiltration in ESCC patients and have important correlations with the therapeutic sensitivity of multiple chemotherapeutic agents.

Sex-dependent influence of LMAN1 on lung function and allergen-induced airway hyperresponsiveness

Abstract We recently reported LMAN1 as a regulatory receptor capable of downregulating NF-kB signaling in response to house dust mite (HDM) in vitro. In this follow up work, we have subjected WT and LMAN1 KO mice to a HDM model of allergic airway inflammation and assessed lung function and airway hyperreactivity (AHR). Given our previous in vitro findings, we had expected worsened disease in the absence of LMAN1. Interestingly, when mice were stratified by sex, we observed that while female, HDM-treated LMAN1 KO mice exhibited signs of enhanced airway narrowing and closure and enhanced AHR compared to their WT counterparts, male, HDM-treated LMAN1 KO mice showed the opposite trend. These sex-dependent effects appeared to be localized to the peripheral airways. We additionally characterized the type and extent of immune cell infiltration into the airway and highlight those features which might account for the gender-biased effects of LMAN1 on lung function in the setting of allergic asthma.

Role of Clock Genes and Circadian Rhythm in Renal Cell Carcinoma: Recent Evidence and Therapeutic Consequences.

Circadian rhythm regulates cellular differentiation and physiology and shapes the immune response. Altered expression of clock genes might lead to the onset of common malignant cancers, including Renal Cell Carcinoma (RCC). Data from Cancer Genome Atlas (TCGA) indicate that clock genes PER1-3, CRY2, CLOCK, NR1D2 and RORα are overexpressed in RCC tissues and correlate with patients' prognosis. The expression of clock genes could finely tune transcription factor activity in RCC and is associated with the extent of immune cell infiltration. The clock system interacts with hypoxia-induced factor-1α (HIF-1α) and regulates the circadian oscillation of mammalian target of rapamycin (mTOR) activity thereby conditioning the antitumor effect of mTOR inhibitors. The stimulation of natural killer (NK) cell activity exerted by the administration of interferon-α, a cornerstone of the first era of immunotherapy for RCC, relevantly varies according to circadian dosing time. Recent evidence demonstrated that time-of-day infusion directly affects the efficacy of immune checkpoint inhibitors in cancer patients. Compounds targeting the circadian clock have been identified and their role in the era of immunotherapy deserves to be further investigated. In this review, we aimed at addressing the impact of clock genes on the natural history of kidney cancer and their potential therapeutic implications.

Modification of m5C regulators in sarcoma can guide different immune infiltrations as well as immunotherapy.

Recent studies have found that 5-methylcytosine (m5C) modulators are associated with the prognosis and treatment of cancer. However, the relevance of m5C modulators in sarcoma prognosis and the tumour microenvironment is unclear. We selected 15 m5C regulators and performed unsupervised clustering to identify m5C modification patterns and differentially expressed genes associated with the m5C phenotype in The Cancer Genome Atlas (TCGA) sarcomas. The extent of immune cell infiltration in different clustering groups was explored using single-sample gene set enrichment analysis and estimation algorithms. A principal component analysis algorithm-based m5C scoring protocol was performed to assess the m5C modification patterns of individual tumors. We identified two distinct m5C modification patterns in the TCGA sarcoma cohort, which possess different clinical outcomes and biological processes. Tumour microenvironment analysis revealed two groups of immune infiltration patterns highly consistent with m5C modification patterns, classified as immune inflammatory and immune desert types. We constructed m5C scores and found that high m5C scores were closely associated with leiomyosarcoma and other subtypes, and were associated with poorer prognosis, lower PD-L1 expression, and poorer immunotherapy outcomes. The best application was validated against the m5C database. We constructed an m5C score for sarcoma based on the TCGA database and identified a poorer prognosis in the high m5c score group. The stability and good prognostic predictive power of the m5C score was verified by an external database. We found that sarcomas in the low m5C score group may have a better response to immunotherapy.

PPAR-δ as a prognostic biomarker and its association with immune infiltrates in breast cancer PPAR-δ as a prognostic biomarker and its association with immune infiltrates in breast cancer.

While peroxisome proliferator-activated receptor δ (PPAR-δ) and its associated signaling pathways have been shown to play an important regulatory role in various malignant tumors, in breast cancer, its potential influence on immune infiltration and its ability to serve as a prognostic marker remains unclear. BRCA patient samples with matched paracancerous samples were obtained from The Cancer Genome Atlas (TCGA). PPAR-δ expression, its potential effect on immune cell infiltration and its association to clinicopathological features were examined. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) and Single-Sample Gene Set Enrichment Analysis (ssGSEA) were utilized for functional and pathway enrichment and to quantify the extent of immune cell infiltration. Kaplan-Meier analysis and Cox regression analysis (nomogram) were performed to assess the association between PPAR- δ and predicted survival. To confirm these findings, an allograft tumor mouse model was generated and treated with a PPAR-δ inhibitor to examine the role of PPAR-δ expression in vivo; while immunohistochemistry (IHC) was performed to examine PPAR-δ expression in paired BRCA patient samples in vitro. Overall, the findings presented herein suggest that PPAR-δ plays a crucial role in breast cancer progression and prognosis and may serve as a survival predictive biomarker.

Identification and validation of a prognostic model based on ferroptosis-associated genes in head and neck squamous cancer.

Ferroptosis is that under the action of ferrous iron or ester oxygenase, unsaturated fatty acids highly expressed on the cell membrane are catalyzed to undergo lipid peroxidation, thereby inducing cell death. In this study, we used ferroptosis marker genes to identify 3 stable molecular subtypes (C1, C2, C3) with distinct prognostic, mutational, and immune signatures by consensus clustering; TP53, CDKN2A, etc. Have higher mutation frequencies in the three subtypes. C3 has a better prognosis, while the C1 subtype has a worse prognosis. WGCNA is used to identify molecular subtype-related gene modules.After filting, we obtained a total of 540 genes related to the module feature vector (correlation>0.7).We performed univariate COX regression analysis on these genes, and identified a total of 97 genes (p < 0.05) that had a greater impact on prognosis, including 8 ''Risk" and 89 ''Protective" genes. After using lasso regression, we identified 8 genes (ZNF566, ZNF541, TMEM150C, PPAN, PGLYRP4, ENDOU, RPL23 and MALSU1) as ferroptosis-related genes affecting prognosis. The ferroptosis prognosis-related risk score (FPRS) was calculated for each sample in TCGA-HNSC dataset. The results showed that FPRS was negatively correlated with prognosis.The activated pathways in the PFRS-high group mainly include immune-related pathways and invasion-related pathways. We assessed the extent of immune cell infiltration in patients in our TCGA-HNSC cohort by using the expression levels of gene markers in immune cells. The FPRS-high group had a higher level of immune cell infiltration. We found that the expression of immune checkpoints was significantly up-regulated in the FPRS-low group and the FPRS-high group had a higher probability of immune escape and a lower probability of benefiting from immunotherapy. In this work, we constructed a scoring Ferroptosis-related prognostic model that can well reflect risk and positive factors for prognosis in patients with head and neck squamous cell carcinoma. It can be used to guide individualized adjuvant therapy and chemotherapy for patients with head and neck cancer. Therefore, it has a good survival prediction ability and provides an important reference for clinical treatment.

Role of androgen receptor signaling pathway-related lncRNAs in the prognosis and immune infiltration of breast cancer

Androgen receptor (AR) is strong association with breast cancer (BRCA). We aimed to investigate the effect of the androgen receptor signaling pathway-related long non-coding RNAs (ARSP-related lncRNAs) on the process of subtype classification and the tumor microenvironment (TME) of breast cancer (BRCA). Our study screen ARSP-related lncRNAs for the construction of a risk model. The single-sample gene set enrichment analysis (ssGSEA) method was used to detect the differences between the immune responses generated by the patients belonging to the low- and high-risk groups. The relationship between the ARSP-related lncRNAs and TME was explored following the process of cluster analysis. The univariate Cox analysis and the Lasso regression analysis method was used to screen nine of these lncRNAs to develop a risk model. It was observed that risk score could function as an independent prognostic factor, affecting the prognoses of patients suffering from BRCA. The validity of the model was assessed by analyzing the generated calibration curves and a nomogram. Additionally, the effect of the risk score on the extent of immune cell infiltration realized in TME was explored. M2 macrophages correlated positively, whereas NK cells, CD4+ T cells, and naive B cells correlated negatively with the risk score. Results obtained using the cluster analysis indicated that immune scores correlated with clustered subtypes. Finally, the risk score and cluster subtypes were analyzed to study the sensitivity of the patients toward different drugs to identify the appropriate therapeutic agents. The prognoses of patients suffering from BRCA can be accurately predicted by ARSP-related lncRNAs.

A novel prognostic model based on six methylation-driven genes predicts overall survival for patients with clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a lethal urological malignancy. DNA methylation is involved in the regulation of ccRCC occurrence and progression. This study aimed to establish a prognostic model based on DNA methylation to predict the overall survival (OS) of patients with ccRCC. To create this model, we used the transcriptome and DNA methylation data of patients with ccRCC from The Cancer Genome Atlas (TCGA) database. We then used the MethylMix R package to identify methylation-driven genes, and LASSO regression and multivariate Cox regression analyses established the prognostic risk model, from which we derived risk scores. We incorporated these risk scores and clinical parameters to develop a prognostic nomogram to predict 3-, 5-, and 7-year overall survival, and its predictive power was validated using the ArrayExpress cohort. These analyses identified six methylation-driven genes (SAA1, FUT6, SPATA18, SHROOM3, AJAP1, and NPEPL1) that produced risk scores, which were sorted into high- and low-risk patient groups. These two groups differed in nomogram-predicted prognosis, the extent of immune cell infiltration, tumor mutational burden, and expected response to additional therapies. In conclusion, we established a nomogram based on six DNA methylation-driven genes with excellent accuracy for prognostic prediction in ccRCC patients. This nomogram model might provide novel insights into the epigenetic mechanism and individualized treatment of ccRCC.