Immune Cell Infiltration Levels Research Articles

Ubiquitin-Specific Protease 52 as a Prognostic Biomarker Correlates with Tumor Microenvironment and Therapy Response in Colorectal Cancer.

As the primary members of the deubiquitinase family, ubiquitin-specific proteases can regulate the efficacy of immunotherapy and mediate immune evasion. However, further research is needed to explore the influence of USP52 on the prognosis of colorectal cancer (CRC), the tumor immune microenvironment, and therapeutic response. The differential expression of USP52 between CRC and normal tissues was analyzed using multiple public databases. The relationship between USP52 with the prognosis and clinicopathological characteristics of CRC patients was evaluated, and a nomogram was constructed to predict patient survival based on USP52 expression. Subsequently, gene set variation analysis (GSVA) was used to explore the potential biological functions of USP52 in CRC. The impact of USP52 on the tumor microenvironment (TME) was estimated. Moreover, the effect of USP52 on the response to immunotherapy and chemotherapeutic drugs in CRC was investigated. Finally, the correlation between tumor mutation burden (TMB)/microsatellite instability (MSI) status and USP52 was explored. The expression of USP52 was markedly upregulated in CRC, correlating with a poor prognosis in patients. GSVA uncovered a strong association between high USP52 and immune suppression. Furthermore, high USP52 was found to be correlated with a non-inflamed TME, resulting in reduced immune cell infiltration levels. Additionally, it was observed that patients with high USP52 exhibited low sensitivity to both immunotherapy and chemotherapeutic drugs. Lastly, high USP52 was negatively associated with high TMB and MSI. This study revealed the significance of USP52 in TME, efficacy of therapy, and clinical prognosis in CRC, offering novel insights for the therapeutic advancements in CRC.

Integrative analysis reveals the potential prognostic roles and immunological values of unc-5 netrin receptor A (UNC5A) in glioma

BackgroundUNC5A had been reported to play crucial roles in multiple cancers. However, little was known about the associations among UNC5A and glioma. Therefore, we first combined scRNA-seq, proteomics, as well as bulk RNA-seq in order to investigate UNC5A’s functions in gliomas.MethodsOnline databases provided scRNA-seq, proteomics, as well as bulk RNA-seq data on UNC5A in gliomas. The following procedures were conducted in order: QRT-PCR, Norman chart, gene set enrichment analysis (GSEA), and univariate/multifactor Cox regression analyses. We further explored the associations among UNC5A and tumor immunity.ResultsBy comparing gliomas with normal tissues, the TCGA dataset showed a significantly reduced expression of UNC5A, which was also confirmed by GSE50161, GSE4290, and QRT-PCR findings (p < 0.05). In both the TCGA and CGGA datasets, gliomas patients with low-UNC5A expression would have poorer overall survival (OS) prognoses (p < 0.05). ScRNA-seq analysis by the CancerSEA online website presented that UNC5A had a low expression in various glioma clusters and significantly associated with six functional states. Moreover, UNC5A might be a reliable independent biomarker of OS in gliomas patients (p < 0.05). Based on the results of GSEA, UNC5A might be connected to three significant pathways in gliomas. We also successfully created a Norman chart to assess the OS prognoses of these patients. Additionally, in aspects of tumor immunity, the infiltration levels of immune cells in LGG, the immune cell pathways, tumor immune microenvironment, as well as immune checkpoints in both LGG and GBM were revealed to be significantly influenced by UNC5A (p < 0.05).ConclusionsUNC5A was found to have prognostic and immunological significance in gliomas, offering patients with gliomas new treatment options.

Development and Validation of an Immune Prognostic Index Related to Infiltration of CD4+ and CD8+ T Cells in Colorectal Cancer.

Colorectal cancer (CRC) is a highly prevalent cancer worldwide, but treatment outcomes can vary significantly among patients with similar clinical or historical stages. This study aimed to investigate the differences in immune cell abundance associated with malignant progression in CRC patients. We utilized data from patients with CRC obtained from The Cancer Genome Atlas as our training set. To assess immune cell infiltration levels, an immune cell risk score (ICRS) was calculated. Furthermore, we performed network analysis to identify effective T cell-related genes (ETRGs) and subsequently constructed an effective T cell prognostic index (ETPI). The performance of the ETPI was evaluated through external validation using four Gene Expression Omnibus datasets. Additionally, a nomogram analysis and drug sensitivity analysis were conducted to explore the clinical utility of the ETRGs. We also examined the expression of ETRGs in clinical samples. Based on the ICRS, we identified activated CD4+ and CD8+ T cells as protective factors in terms of prognosis. Six ETRGs were identified to develop the ETPI, which exhibited remarkable prognostic performance. In the external validation of immunotherapy, the low ETPI group demonstrated a significantly lower recurrence rate. To optimize therapeutic strategies, we developed a nomogram. Notably, patients with different ETPI values exhibited varying responses to tumor pathway inhibitors. Finally, we observed higher protein expression of certain ETRGs in normal tissues compared to tumors. Our findings suggest that the ETPI may contribute to the precise selection of patients based on tumor microenvironment and key genomic landscape interactions, thereby optimizing drug benefits and informing clinical strategies in future.

Construction of prognostic markers for pancreatic adenocarcinoma based on mitochondrial fusion-related genes.

Early detection of pancreatic adenocarcinoma (PAAD) remains a pressing clinical problem. Information on the clinical prognostic value of mitochondrial fusion-related genes in PAAD remains limited. In this study, we investigated mitochondrial fusion-related genes of PAAD to establish an optimal signature plate for the early diagnosis and prognosis of PAAD. The cancer genome atlas database was used to integrate the Fragments Per Kilobase Million data and related clinical data for patients with PAAD. Least absolute shrinkage and selection operator regression, cox regression, operating characteristic curves, and cBioPortal database was used to evaluate model performance, assess the prognostic ability and sensitivity. The levels of immune infiltration were compared by CIBERSORT, QUANTISEQ, and EPIC. Chemotherapy sensitivity between the different risk groups was compared by the Genomics of Drug Sensitivity in Cancer database and the "pRRophetic" R package. At last, a total of 4 genes were enrolled in multivariate Cox regression analysis. The risk-predictive signature was constructed as: (0.5438 × BAK1) + (-1.0259 × MIGA2) + (1.1140 × PARL) + (-0.4300 × PLD6). The area under curve of these 4 genes was 0.89. Cox regression analyses indicates the signature was an independent prognostic indicator (P < .001, hazard ratio [HR] = 1.870, 95% CI = 1.568-2.232). Different levels of immune cell infiltration in the 2 risk groups were observed using the 3 algorithms, with tumor mutation load (P = .0063), tumor microenvironment score (P = .01), and Tumor Immune Dysfunction and Exclusion score (P = .0012). The chemotherapeutic sensitivity analysis also revealed that the half-maximal inhibitory concentration of 5-fluorouracil (P = .0127), cisplatin (P = .0099), docetaxel (P < .0001), gemcitabine (P = .0047), and pacilataxel (P < .0001) were lower in the high-risk groups, indicating that the high-risk group patients had a greater sensitivity to chemotherapy. In conclude, we established a gene signature plate comprised of 4 mitochondrial fusion related genes to facilitate early diagnosis and prognostic prediction of PAAD.

Diagnostic value of selenoprotein changes in renal tissues for acute rejection of kidney transplantation as revealed by transcriptomics

BackgroundThere seems to be a close link between the changing levels of selenoproteins, which are important for maintaining redox homeostasis in the body, and acute rejection of kidney transplants. The aim of this study was to explore the diagnostic value of selenoprotein change characteristics in renal tissues for acute rejection of kidney transplantation. MethodsWe first explored the potential biological functions of 25 selenoproteins in the human body by enrichment analysis and used the HPA database to clarify the expression levels of selenoproteins in kidney tissues; We then constructed a diagnostic model using “Logistic regression analysis” and “Nomogram model”; Calibration curves and ROC curves were used to evaluate the diagnostic models, and clinical decision curves (DCA) were used to assess the diagnostic value of selenoprotein changes to the clinic; Single-gene GSEA enrichment analysis to further explore the potential regulatory mechanisms of selenoproteins; The Cibersort algorithm explores the level of immune cell infiltration and uses correlation analysis to clarify the correlation between selenoproteins and immune cells; We further assessed the diagnostic value of selenoproteins in kidney transplantation ABMR and TCMR, respectively. Finally, we validated the expression level of selenoproteins in kidney tissues by constructing a rat model of acute rejection of kidney transplantation using transcriptome sequencing. ResultsOur enrichment analysis revealed that selenoproteins are mainly closely associated with biological functions such as oxidative stress, inflammation, and immune regulation (P<0.05); The HPA database suggests that a total of 23 selenoproteins can be expressed in kidney tissue. We constructed a diagnostic model using these 23 selenoproteins, and both calibration curves and ROC curves proved that their change levels have good diagnostic value for acute rejection of kidney transplantation, and DCA curves proved the role of selenoproteins in clinical decision-making; Single-gene GSEA enrichment analysis revealed that selenoproteins are closely associated with immune regulation-related pathways (P<0.05); The Cibersort algorithm identified 10 immune cell infiltration levels that were significantly altered during acute rejection of kidney transplantation (P<0.05), while correlation analyses indicated that selenoproteins correlate with multiple immune cell infiltrations; In ABMR and TCMR, we again verified the diagnostic value of selenoprotein changes in acute rejection of kidney transplantation. Finally, we found significant differences in the expression levels of nine selenoproteins in a rat model of acute rejection of kidney transplantation (P<0.05). ConclusionChanges in selenoproteins in renal tissues have good diagnostic value for acute rejection of kidneyl transplantation, and selenoproteins may be able to be a potential target for alleviating acute rejection of kidney transplantation.

Identifying the Pattern Characteristics of Anoikis-Related Genes in Keloid.

Objective: Anoikis is a kind of programmed cell death that is triggered when cells lose contact with each other or with the matrix. However, the potential value of anoikis-related genes (ARGs) in keloid (KD) has not been investigated. Approach: We downloaded three keloid fibroblast (KF) RNA sequencing (RNA-seq) datasets from the Gene Expression Omnibus (GEO) and obtained 338 ARGs from a search of the GeneCards database and PubMed articles. Weighted correlation network analysis was used to construct the coexpression network and obtain the KF-related ARGs. The LASSO-Cox method was used to screen the hub ARGs and construct the best prediction model. Then, GEO single-cell sequencing datasets were used to verify the expression of hub genes. We used whole RNA-seq for gene-level validation and the correlation between KD immune infiltration and anoikis. Results: Our study comprehensively analyzed the role of ARGs in KD for the first time. The least absolute shrinkage and selection operator (LASSO) regression analysis identified six hub ARGs (HIF1A, SEMA7A, SESN1, CASP3, LAMA3, and SIK2). A large number of miRNAs participate in the regulation of hub ARGs. In addition, correlation analysis revealed that ARGs were significantly correlated with the infiltration levels of multiple immune cells in patients with KD. Innovation: We explored the expression characteristics of ARGs in KD, which is extremely important for determining the molecular pathways and mechanisms underlying KD. Conclusions: This study provides a useful reference for revealing the characteristics of ARGs in the pathogenesis of KD. The identified hub genes may provide potential therapeutic targets for patients. This study provides new ideas for individualized therapy and immunotherapy.

The deubiquitinating enzymes-related signature predicts the prognosis and immunotherapy response in breast cancer.

Breast cancer is a prevalent disease that has a dismal prognosis for patients and a bad outlook for treatments. Ubiquitination is a reversible biological process that regulates protein production and degradation, as well as plays a vital role in protein transport, localization, and biological activity. We obtained the breast cancer patient sample data and used a machine learning technique to create a novel index called Deubiquitinating enzyme related index (DUBRI) by gathering genes associated to deubiquitinating enzymes. Based on DUBRI, we systematically analyze patients' prognosis, clinical characteristics, tumor immune microenvironment, chemotherapy response and immunotherapy response. Finally, the function of OTUB2 was explored in breast cancer cells. DUBRI, which consists of five deubiquitinating enzyme genes (OTUB2, USP41, MINDY2, YOD1, and PSMD7), is a reliable predictor of survival in breast cancer patients. We found that the high DUBRI group presented higher levels of immune cell infiltration. We performed molecular docking prediction of core target proteins in deubiquitinating enzymes. In vitro experiments verified that knockdown of OTUB2 could inhibit the proliferation and migration of breast cancer. The DUBRI discovered in this research may effectively evaluate the outlook of breast cancer patients and identify groups of patients who would gain advantages from immunotherapy, offering vital knowledge for the future targeted treatment of breast cancer patients.

The Novel-B-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Esophageal Carcinoma

BackgroundThe current understanding of the prognostic significance of B cells and their role in the tumor microenvironment (TME) in esophageal carcinoma (ESCA) is limited.MethodsWe conducted a screening for B-cell-related genes through the analysis of single-cell transcriptome data. Subsequently, we developed a B-cell-related gene signature (BRGrisk) using LASSO regression analysis. Patients from The Cancer Genome Atlas cohort were divided into a training cohort and a test cohort. Patients were categorized into high- and low-risk groups based on their median BRGrisk scores. The overall survival was assessed using the Kaplan-Meier method, and a nomogram based on BRGrisk was constructed. Immune infiltration profiles between the risk groups were also compared.ResultsThe BRGrisk prognostic model indicated significantly worse outcomes for patients with high BRGrisk scores (p < 0.001). The BRGrisk-based nomogram exhibited good prognostic performance. Analysis of immune infiltration revealed that patients in the high-BRGrisk group had notably higher levels of immune cell infiltration and were more likely to be in an immunoresponsive state. Enrichment analysis showed a strong correlation between the prognostic gene signature and cancer-related pathways. IC50 results indicated that patients in the low-BRGrisk group were more responsive to common drugs compared to those in the high-BRGrisk group.ConclusionsThis study presents a novel BRGrisk that can be used to stratify the prognosis of ESCA patients and may offer guidance for personalized treatment strategies aimed at improving prognosis.

TCBIR/CD320: a potential therapeutic target upregulated in endothelial cells and associated with immune cell infiltration in liver hepatocellular carcinoma

CD320, which is a transmembrane protein responsible for facilitating the absorption of vitamin B12, plays a key role in this process. However, the relationships between CD320 and immune cell infiltration levels remain unclear, with limited studies investigating the diagnostic and prognostic significance of CD320 in hepatocellular carcinoma. We used various databases, including the TIMER, GEPIA, UALCAN and TCGA databases to investigate the expression levels of CD320 in hepatocellular carcinoma. Subsequently, we analyzed the prognosis of hepatocellular carcinoma patients with different expression levels of CD320. Furthermore, we also performed Western blot, immunohistochemistry, and immunofluorescence analyses to validate the results of the database analysis. Finally, the functions of CD320 in hepatocellular carcinoma were also confirmed via relevant cell experiments and angiogenesis assays. We found that CD320 expression was significantly upregulated in tumor vascular endothelial cells. Moreover, the knockdown of CD320 led to a reduction in angiogenesis in endothelial cells. Increased expression of CD320 was also correlated with a poor prognosis in patients with hepatocellular carcinoma, which suggested that CD320 may be a potential prognostic marker. Finally, TIMER analysis demonstrated that the infiltration of six immune cell types was significantly associated with high expression levels of CD320 in hepatocellular carcinoma. Herein, we demonstrated that CD320 may play an important role in angiogenesis in hepatocellular carcinoma. These findings suggested that CD320 may be a potential clinical prognostic marker and immunotherapy target for hepatocellular carcinoma.

The liquid-liquid phase separation signature predicts the prognosis and immunotherapy response in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common and fatal malignancy characterized by poor patient prognosis and treatment outcome. The process of liquid-liquid phase separation in tumour cells alters the dysfunction of biomolecular condensation in tumour cells, which affects tumour progression and treatment. We downloaded the data of HCC samples from TCGA database and GEO database, and used a machine learning method to build a new liquid-liquid phase separation index (LLPSI) by liquid-liquid phase separation related genes. The LLPSI-related column line Figure was constructed to provide a quantitative tool for clinical practice. HCC patients were divided into high and low LLPSI groups based on LLPSI, and clinical features, tumour immune microenvironment, chemotherapeutic response, and immunotherapeutic response were systematically analysed. LLPSI, which consists of five liquid-liquid phase separation-associated genes (MAPT, WDR62, PLK1, CDCA8 and TOP2A), is a reliable predictor of survival in patients with HCC and has been validated in multiple external datasets. We found that the high LLPSI group showed higher levels of immune cell infiltration and better response to immunotherapy compared to the low LLPSI group, and LLPSI can also be used for prognostic prediction in various cancers other than HCC. Invitro experiments verified that knockdown of MAPT could inhibit the proliferation and migration of HCC. The LLPSI identified in this study can accurately assess the prognosis of patients with HCC and identify patient populations that will benefit from immunotherapy, providing valuable insights into the clinical management of HCC.

Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi-omics consensus machine learning approach.

Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, there is a dearth of precise, individualized treatment plans. We integrated mRNA, lncRNA, microRNA, methylation and mutation data from the TCGA database for LUAD. Utilizing ten clustering algorithms, we identified stable multi-omics consensus clusters (MOCs). These data were then amalgamated with ten machine learning approaches to develop a robust model capable of reliably identifying patient prognosis and predicting immunotherapy outcomes. Through ten clustering algorithms, two prognostically relevant MOCs were identified, with MOC2 showing more favourable outcomes. We subsequently constructed a MOCs-associated machine learning model (MOCM) based on eight MOCs-specific hub genes. Patients characterized by a lower MOCM score exhibited better overall survival and responses to immunotherapy. These findings were consistent across multiple datasets, and compared to many previously published LUAD biomarkers, our MOCM score demonstrated superior predictive performance. Notably, the low MOCM group was more inclined towards 'hot' tumours, characterized by higher levels of immune cell infiltration. Intriguingly, a significant positive correlation between GJB3 and the MOCM score (R = 0.77, p < 0.01) was discovered. Further experiments confirmed that GJB3 significantly enhances LUAD proliferation, invasion and migration, indicating its potential as a key target for LUAD treatment. Our developed MOCM score accurately predicts the prognosis of LUAD patients and identifies potential beneficiaries of immunotherapy, offering broad clinical applicability.

Development and validation of a glioma prognostic model based on telomere-related genes and immune infiltration analysis.

Gliomas are the most prevalent primary brain tumors, and patients typically exhibit poor prognoses. Increasing evidence suggests that telomere maintenance mechanisms play a crucial role in glioma development. However, the prognostic value of telomere-related genes in glioma remains uncertain. This study aimed to construct a prognostic model of telomere-related genes and further elucidate the potential association between the two. We acquired RNA-seq data for low-grade glioma (LGG) and glioblastoma (GBM), along with corresponding clinical information from The Cancer Genome Atlas (TCGA) database, and normal brain tissue data from the Genotype-Tissue Expression (GTEX) database for differential analysis. Telomere-related genes were obtained from TelNet. Initially, we conducted a differential analysis on TCGA and GTEX data to identify differentially expressed telomere-related genes, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on these genes. Subsequently, univariate Cox analysis and log-rank tests were employed to obtain prognosis-related genes. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis were sequentially utilized to construct prognostic models. The model's robustness was demonstrated using receiver operating characteristic (ROC) curve analysis, and multivariate Cox regression of risk scores for clinical characteristics and prognostic models were calculated to assess independent prognostic factors. The aforementioned results were validated using the Chinese Glioma Genome Atlas (CGGA) dataset. Finally, the CIBERSORT algorithm analyzed differences in immune cell infiltration levels between high- and low-risk groups, and candidate genes were validated in the Human Protein Atlas (HPA) database. Differential analysis yielded 496 differentially expressed telomere-related genes. GO and KEGG pathway analyses indicated that these genes were primarily involved in telomere-related biological processes and pathways. Subsequently, a prognostic model comprising ten telomere-related genes was constructed through univariate Cox regression analysis, log-rank test, LASSO regression analysis, and multivariate Cox regression analysis. Patients were stratified into high-risk and low-risk groups based on risk scores. Kaplan-Meier (K-M) survival analysis revealed worse outcomes in the high-risk group compared to the low-risk group, and establishing that this prognostic model was a significant independent prognostic factor for glioma patients. Lastly, immune infiltration analysis was conducted, uncovering notable differences in the proportion of multiple immune cell infiltrations between high- and low-risk groups, and eight candidate genes were verified in the HPA database. This study successfully constructed a prognostic model of telomere-related genes, which can more accurately predict glioma patient prognosis, offer potential targets and a theoretical basis for glioma treatment, and serve as a reference for immunotherapy through immune infiltration analysis.

The roles of lncRNA AP001469.3 in clinical implications, immune landscape and carcinogenesis of colorectal cancer.

Previously, long non-coding RNA (lncRNA) gene AP001469.3 was reported to participate in the construction of an immune-related lncRNA signature, which showed promising clinical predictive value in colorectal cancer (CRC) patients. However, the clinical and immunological significance and biological function of AP001469.3 in CRC remain unclear. In this study, we aim to explore the roles of AP001469.3 in CRC progression, thereby opening an avenue for CRC treatment. Our study collected data from The Cancer Genome Atlas (TCGA) database and investigated the role of AP001469.3 in CRC through bioinformatics analysis. Cell-type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) methods evaluated the immune infiltration. The biological functions of AP001469.3 in CRC were validated by in vitro experiments. Gene set enrichment analysis (GSEA) was used to estimate the enrichment of functional pathways and gene signatures. In this work, high expression of AP001469.3 was found in CRC and was positively associated with tumor-node-metastasis (TNM) stage in CRC. AP001469.3 expression had a strong relationship with microsatellite instability (MSI) in colon adenocarcinoma (COAD). Additionally, AP001469.3 expression was associated with StromalScore, ImmuneScore, ESTIMATEScore, immune cell infiltration (ICI) levels and immune checkpoint (ICP) genes expression in CRC. Subsequent results showed that immunotherapy could be more effective in CRC patients with low-AP001469.3 expression using the immunophenoscore (IPS). We confirmed that the transcript of AP001469.3 gene ENST00000430259 was highly expressed in CRC tissues and cell lines. In vitro experiments indicated that ENST00000430259 knockdown reduced the proliferation, migration and invasion of CRC cells. Finally, our GSEA results showed that the majority of the differentially enriched signaling pathways between the high- and low-AP001469.3 expression groups were immune-related. Taken together, our study demonstrates that lncRNA gene AP001469.3 is associated with immunological characteristics in CRC and promotes malignant progression of CRC. Moreover, AP001469.3 can be potentially used as an immunotherapeutic indicator and a therapeutic target for CRC patients.

Integrative pan-cancer analysis reveals the prognostic and immunotherapeutic value of ALKBH7 in HNSC.

The AlkB homolog 7 (ALKBH7) is a nonheme iron (II) α-ketoglutarate-dependent dioxygenase superfamily member, which may affect the progression of several types of human cancer. However, the biological effect, especially the immune-related effect, of ALKBH7 in HNSC remains unclear. Herein, several databases were employed at first to assess the different expression of ALKBH7 as well as their relationship to the prognosis, RNA modification, DNA methylation modulation, immune microenvironment and chemotherapeutic responses of various types of cancers. We found that ALKBH7 was expressed differentially in pan-cancer, and correlated with a satisfied prognosis especially in HNSC. The expression of ALKBH7 was also associated with the level of immune cell infiltration, TMB, MSI, HRD, MMR deficiency, and DNA methyltransferases in a wide variety of cancers, which might be potentially related to the responses against chemotherapeutic agents. Next, the role of ALKBH7 in HNSC was further investigated. Western blot and immunohistochemical analysis in HNSC patients from the NMU cohort showed the reduced ALKBH7 expression level in tumor tissues. In vitro experiments of cell migration, invasion, and proliferation showed a potential protective effect of ALKBH7 in HNSC. Collectively, ALKBH7 might play a protective role in the development and progression of multiple cancers by affecting the metabolism and immune cell infiltration, especially HNSC, which could be a potential biomarker to predict prognosis and chemotherapeutic response.

Breast Cancer Treatment Strategies Targeting the Tumor Microenvironment: How to Convert "Cold" Tumors to "Hot" Tumors.

Breast cancer characterized as "cold tumors" exhibit low levels of immune cell infiltration, which limits the efficacy of conventional immunotherapy. Recent studies have focused on strategies using nanotechnology combined with tumor microenvironment modulation to transform "cold tumors" into "hot tumors". This approach involves the use of functionalized nanoparticles that target and modify the tumor microenvironment to promote the infiltration and activation of antitumor immune cells. By delivering immune activators or blocking immunosuppressive signals, these nanoparticles activate otherwise dormant immune responses, enhancing tumor immunogenicity and the therapeutic response. These strategies not only promise to increase the response rate of breast cancer patients to existing immunotherapies but also may pave new therapeutic avenues, providing a new direction for the immunotherapy of breast cancer.

Identification of lncRNA-mRNA network linking ferroptosis and immune infiltration to colon adenocarcinoma suppression

BackgroundColon adenocarcinoma (COAD) is one of the most common malignant tumors. The interplay involving ferroptosis between tumor and immune cells plays a crucial in cancer progression. However, the biological basis of this interplay in COAD development remains elusive. MethodsTranscriptome data of COAD samples were obtained from The Cancer Genome Atlas and National Center for Biotechnology Information databases. Using single-sample gene set enrichment analysis, we calculated the ferroptosis score (FS) and immune cell infiltration levels for each sample, leveraging the expression levels of genes related to ferroptosis and various immune cell types. Samples with FSs greater than the 75th percentile were classified into the high-FS subgroup, while those below the 25th percentile were categorized as the low-FS subgroup. Moreover, tumor tissue samples and adjacent normal tissue samples were collected from twenty colon patients. Using real-time quantitative polymerase chain reaction, we validated the expression of certain genes in these samples. ResultsThe COAD samples with high FSs experienced favorable survival probability and heightened sensitivity to anticancer drugs, with FSs negatively associated with the pathological stages. Moreover, the up-regulated genes in high-FS subgroup exhibited enrichment in immune-related pathways, suggesting a correlation between immunity and ferroptosis. Importantly, we discovered a key lncRNA-mRNA co-expression network linking tumor cell ferroptosis and immune infiltration (e.g., neutrophil) in the progression and classification of COAD. Further analysis identified several ferroptosis-related lncRNAs (e.g., RP11-399O19.9) within this network, indicating their potential roles in COAD progression and deserving in-depth study. ConclusionsOur findings provide novel insights into the underlying biological basis, particularly involving lncRNAs, at gene expression level associated with ferroptosis in COAD and cancer therapy. Nevertheless, further analysis and validation are required to expand the findings.

Construction of a circadian rhythm-relevant gene signature for hepatocellular carcinoma prognosis, immunotherapy and chemosensitivity prediction

AimsThis study explored the molecular and biologic mechanisms underlying the association between circadian rhythm disorders (CRD) and increased risk for hepatocellular carcinoma (HCC). BackgroundCRD are linked to increased risk for HCC, but the molecular and biologic mechanisms underlying this association are limited.ObjectiveThe study constructed and validated a CRD related gene model as an independent prognostic factor for HCC, providing insight into the molecular mechanisms linking CRD to increased HCC risk and identifying potential indicators for the efficacy of immunotherapy and anticancer drugs. This helps provide important clues for personalized treatment strategies for HCC patients. MethodsGene sets correlated with circadian rhythm were obtained from the Molecular Signatures Database (MSigDB) to intersect with differentially expressed genes (DEGs) between tumor samples and control samples in The Cancer Genome Atlas (TCGA) and HCCDB18 from Hepatocellular Carcinoma Cell DataBase (HCCDB). The CRD related gene model was developed by univariate Cox and stepwise multivariate analysis. Immune checkpoint blockade (ICB) therapy and anticancer drugs were analyzed using the tumor immune dysfunction and exclusion (TIDE) and pRRophetic, respectively. Seurat determined the cell type of HCC by analyzing single-cell data, and malignant cells were identified using Copykat. To detect the mRNA levels of genes in the CRD related gene model, quantitative real-time polymerase chain reaction (qRT-PCR) was carried out. ResultsThe activity of circadian rhythm in HCC tissue was significantly lower than that in control tissue. Subsequently, EZH2, IMPDH2, TYMS and SERPINE1 were selected to construct the CRD related gene model, which was an independent factor for HCC prognosis. Notably, low-risk patients had lower levels of immune cell infiltration and lower TIDE scores compared to high-risk patients with HCC, indicating that patients with a low risk may derive more benefit from immunotherapy. IMPDH2, TYMS and SERPINE1 expressed significantly higher in malignant cells than in benign epithelial cells. ConclusionsThis study presents a CRD related gene model to reveal the molecular perspective of the dependent mechanism of the association between CRD and cancer, which provides a potential indicator for understanding the preclinical efficacy of ICB and anticancer drugs.

Exploring the Roles of Key Mediators IKBKE and HSPA1A in Alzheimer's Disease and Hepatocellular Carcinoma through Bioinformatics Analysis.

Recent studies have hinted at a potential link between Alzheimer's Disease (AD) and cancer. Thus, our study focused on finding genes common to AD and Liver Hepatocellular Carcinoma (LIHC), assessing their promise as diagnostic indicators and guiding future treatment approaches for both conditions. Our research utilized a broad methodology, including differential gene expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), gene enrichment analysis, Receiver Operating Characteristic (ROC) curves, and Kaplan-Meier plots, supplemented with immunohistochemistry data from the Human Protein Atlas (HPA) and machine learning techniques, to identify critical genes and significant pathways shared between AD and LIHC. Through differential gene expression analysis, WGCNA, and machine learning methods, we identified nine key genes associated with AD, which served as entry points for LIHC analysis. Subsequent analyses revealed IKBKE and HSPA1A as shared pivotal genes in patients with AD and LIHC, suggesting these genes as potential targets for intervention in both conditions. Our study indicates that IKBKE and HSPA1A could influence the onset and progression of AD and LIHC by modulating the infiltration levels of immune cells. This lays a foundation for future research into targeted therapies based on their shared mechanisms.

Multi-omic validation of the cuproptosis-sphingolipid metabolism network: modulating the immune landscape in osteosarcoma.

The current understanding of the mechanisms by which metal ion metabolism promotes the progression and drug resistance of osteosarcoma remains incomplete. This study aims to elucidate the key roles and mechanisms of genes involved in cuproptosis-related sphingolipid metabolism (cuproptosis-SPGs) in regulating the immune landscape, tumor metastasis, and drug resistance in osteosarcoma cells. This study employed multi-omics approaches to assess the impact of cuproptosis-SPGs on the prognosis of osteosarcoma patients. Lasso regression analysis was utilized to construct a prognostic model, while multivariate regression analysis was applied to identify key core genes and generate risk coefficients for these genes, thereby calculating a risk score for each osteosarcoma patient. Patients were then stratified into high-risk and low-risk groups based on their risk scores. The ESTIMATE and CIBERSORT algorithms were used to analyze the level of immune cell infiltration within these risk groups to construct the immune landscape. Single-cell analysis was conducted to provide a more precise depiction of the expression patterns of cuproptosis-SPGs among immune cell subtypes. Finally, experiments on osteosarcoma cells were performed to validate the role of the cuproptosis-sphingolipid signaling network in regulating cell migration and apoptosis. In this study, seven cuproptosis-SPGs were identified and used to construct a prognostic model for osteosarcoma patients. In addition to predicting survival, the model also demonstrated reliability in forecasting the response to chemotherapy drugs. The results showed that a high cuproptosis-sphingolipid metabolism score was closely associated with reduced CD8 T cell infiltration and indicated poor prognosis in osteosarcoma patients. Cellular functional assays revealed that cuproptosis-SPGs regulated the LC3B/ERK signaling pathway, thereby triggering cell death and impairing migration capabilities in osteosarcoma cells. The impact of cuproptosis-related sphingolipid metabolism on the survival and migration of osteosarcoma cells, as well as on CD8 T cell infiltration, highlights the potential of targeting copper ion metabolism as a promising strategy for osteosarcoma patients.

Developing a prognostic model for skin melanoma based on the persistent tumor mutation burden and determining IL17REL as a therapeutic target

BackgroundOne popular and well-established marker for the immune checkpoint blockade (ICB) response is tumor mutation burden (TMB). Persistent TMB (pTMB), a subset of TMB, provides a better indicator to predict patient ICB therapy outcomes, as shown by some studies. Immune checkpoint drugs have significantly changed how melanoma is treated in recent years.MethodsIn this study, we integrated the TCGA-SKCM database and data of pTMB of TCGA from the paper that first mentioned pTMB and analyzed mutational and Immune characteristics associated with pTMB level in SKCM. Next, the predictive DEGs were identified the subgroups of pTMB by Cox regression and LASSO analyses to construct a pTMB-related signature. Finally, the expression and Biological functions of signature genes was detected, and further validated in vitro assay.ResultsIn the current research, we explored the mutational and immunological features related to the level of TMB in cutaneous melanoma (CM). The high-pTMB subgroup exhibited an increasing incidence of gene changes and higher levels of immune cell infiltration. Subsequently, we established a pTMB-related signature based on the predictive DEGs and found the biological features and immune-associated variables between two distinct risk groups. Lastly, the results of the clinical sample validation demonstrated that the expression of IL17REL was down-regulated in the collected samples of individuals with CM. The in vitro assay results indicated that IL17REL effectively suppressed the proliferation, clonality, and migration of CM cells.ConclusionIn conclusion, we have developed a prediction model associated with TMB and subsequently validated the potential influence of IL17REL on Overall Survival (OS) in patients diagnosed with melanoma.