Immune Cell Infiltration Levels Research Articles

MFG-E8 promotes M2 polarization of macrophages and is associated with poor prognosis in patients with gastric cancer

BackgroundMilk Fat Globule-Epidermal Growth Factor 8 (MFG-E8) has been reported to play an oncogenic role in a variety of tumors. However, its involvement in gastric cancer (GC) development has not been described. MethodsThe cancer genome atlas (TCGA) and the gene expression omnibus database (GEO) databases were used to analyze the expression of MFG-E8 in GC. These findings were further validated using immunohistochemistry (IHC) and western blotting assay (WB). Kaplan-Meier method, univariate logistic regression, and Christopher Cox regression were used to study the relationship between MFG-E8 and clinical pathology. In addition, the potential signaling pathways involved in MFG-E8 and its potential correlation with levels of immune cell infiltration were investigated. Finally, the biological function of MFG-E8 in GC cells was revealed. ResultsMFG-E8 was highly expressed in GC patients and cells, and the high level of MFG-E8 was associated with poor overall survival (OS). KEGG analysis indicated that MFG-E8 may play an important role in the cAMP signaling pathway. The expression of MFG-E8 was positively correlated with the infiltration of M2 macrophages. The patients with high MFG-E8 were easy to develop chemotherapy resistance. Furthermore, the knockdown of MFG-E8 significantly inhibited the proliferation and invasion of GC cells. ConclusionMFG-E8 in GC may serve as a prognostic marker and a potential immunotherapy target for GC.

Upregulation of MED7 was associated with progression in hepatocellular carcinoma.

MED subunits have been reported to be associated with various types of tumors, however, the potential role of MED7 in hepatocellular carcinoma (HCC) was still unclear. The aim of the study was to explore the role of MED7 in HCC. In this study, MED7 mRNA expression levels between HCC and adjacent normal tissues were first analyzed by several public datasets. Then we utilized a tissue microarray (TMA) to investigate the clinical role of MED7 in HCC by immunohistochemistry (IHC). Meanwhile, the potential mechanisms of MED7 based on gene-gene correlation analyses were also explored. High mRNA level of MED7 correlated with advanced stage and worse grade of differentiation. IHC results showed that MED7 protein level was upregulated in HCC and associated with Edmondson grade and Microvascular invasion in 330 cases of HCC. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis revealed that MED7 co-expressed genes participate primarily in ribonucleoprotein complex biogenesis, protein targeting, mRNA processing and nucleoside triphosphate metabolic process et cetera. Further analysis also revealed that MED7 mRNA level has significant correlation with immune cells infiltration levels. MED7 was upregulated in HCC and correlated with progression of HCC. Meanwhile, MED7 may promote HCC through participating in multiple gene networks to influence tumorigenesis as well as immune response in HCC microenvironment.

Establishment of a lysosome-related prognostic signature in breast cancer to predict immune infiltration and therapy response.

Lysosomes are instrumental in intracellular degradation and recycling, with their functional alterations holding significance in tumor growth. Nevertheless, the precise role of lysosome-related genes (LRGs) in breast cancer (BC) remains elucidated. This study aimed to establish a prognostic model for BC based on LRGs. Employing The Cancer Genome Atlas (TCGA) BC cohort as a training dataset, this study identified differentially expressed lysosome-related genes (DLRGs) through intersecting LRGs with differential expression genes (DEGs) between tumor and normal samples. A prognostic model of BC was subsequently developed using Cox regression analysis and validated within two Gene Expression Omnibus (GEO) external validation sets. Further analyses explored functional pathways, the immune microenvironment, immunotherapeutic responses, and sensitivity to chemotherapeutic drugs in different risk groups. Additionally, the mRNA and protein expression levels of genes within the risk model were examined by utilizing the Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) databases. Clinical tissue specimens obtained from patients were gathered to validate the expression of the model genes via Real-Time Polymerase Chain Reaction (RT-PCR). We developed a risk model of BC based on five specific genes (ATP6AP1, SLC7A5, EPDR1, SDC1, and PIGR). The model was validated for overall survival (OS) in two GEO validation sets (p=0.00034 for GSE20685 and p=0.0095 for GSE58812). In addition, the nomogram incorporating clinical factors showed better predictive performance. Compared to the low-risk group, the high-risk group had a higher level of certain immune cell infiltration, including regulatory T cells (Tregs) and type 2 T helper cells (Th2). The high-risk patients appeared to respond less well to general immunotherapy and chemotherapeutic drugs, according to the Tumor Immune Dysfunction and Exclusion (TIDE), Immunophenotype Score (IPS), and drug sensitivity scores. The RT-PCR results validated the expression trends of some prognostic-related genes in agreement with the previous differential expression analysis. Our innovative lysosome-associated signature can predict the prognosis for BC patients, offering insights for guiding subsequent immunotherapeutic and chemotherapeutic interventions. Furthermore, it has the potential to provide a scientific foundation for identifying prospective therapeutic targets.

Identification and validation of eight lysosomes-related genes signatures and correlation with immune cell infiltration in lung adenocarcinoma

Lung cancer is the leading cause of cancer-related death. Lysosomes are key degradative compartments that maintain protein homeostasis. In current study, we aimed to construct a lysosomes-related genes signature to predict the overall survival (OS) of patients with Lung Adenocarcinoma (LUAD). Differentially expressed lysosomes-related genes (DELYs) were analyzed using The Cancer Genome Atlas (TCGA-LUAD cohort) database. The prognostic risk signature was identified by Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox proportional hazards regression and multivariate Cox analysis. The predictive performance of the signature was assessed by Kaplan–Meier curves and Time-dependent receiver operating characteristic (ROC) curves. Gene set variant analysis (GSVA) was performed to explore the potential molecular biological function and signaling pathways. ESTIMATE and single sample gene set enrichment analysis (ssGSEA) were applied to estimate the difference of tumor microenvironment (TME) between the different risk subtypes. An eight prognostic genes (ACAP3, ATP8B3, BTK, CAV2, CDK5R1, GRIA1, PCSK9, and PLA2G3) signature was identified and divided patients into high-risk and low-risk groups. The prognostic signature was an independent prognostic factor for OS (HR > 1, p < 0.001). The molecular function analysis suggested that the signature was significantly correlated with cancer-associated pathways, including angiogenesis, epithelial mesenchymal transition, mTOR signaling, myc-targets. The low-risk patients had higher immune cell infiltration levels than high-risk group. We also evaluated the response to chemotherapeutic, targeted therapy and immunotherapy in high- and low-risk patients with LUAD. Furthermore, we validated the expression of the eight gene expression in LUAD tissues and cell lines by qRT-PCR. LYSscore signature provide a new modality for the accurate diagnosis and targeted treatment of LUAD and will help expand researchers’ understanding of new prognostic models.

A novel sphingolipid metabolism-related long noncoding RNA signature predicts the prognosis, immune landscape and therapeutic response in pancreatic adenocarcinoma

Sphingolipid metabolism affects prognosis and resistance to immunotherapy in patients with cancer and is an emerging target in cancer therapy with promising diagnostic and prognostic value. Long noncoding ribonucleic acids (lncRNAs) broadly regulate tumour-associated metabolic reprogramming. However, the potential of sphingolipid metabolism-related lncRNAs in pancreatic adenocarcinoma (PAAD) is poorly understood. In this study, coexpression algorithms were employed to identify sphingolipid metabolism-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) algorithm was used to develop a sphingolipid metabolism-related lncRNA signature (SMLs). The prognostic predictive stability of the SMLs was validated using Kaplan–Meier. Univariate and multivariate Cox, receiver operating characteristic (ROC) and clinical stratification analyses were used to comprehensively assess the SMLs. Gene set variation analysis (GSVE), gene ontology (GO) and tumor mutation burden (TMB) analysis explored the potential mechanisms. Additionally, single sample gene set enrichment analysis (ssGSEA), ESTIMATE, immune checkpoints and drug sensitivity analysis were used to investigate the potential predictive function of the SMLs. Finally, an SMLs-based consensus clustering algorithm was utilized to differentiate patients and determine the suitable population for immunotherapy. The results showed that the SMLs consists of seven sphingolipid metabolism-related lncRNAs, which can well determine the clinical outcome of individuals with PAAD, with high stability and general applicability. In addition, the SMLs-based consensus clustering algorithm divided the TCGA-PAAD cohort into two clusters, with Cluster 1 showing better survival than Cluster 2. Additionally, Cluster 1 had a higher level of immune cell infiltration than Cluster 2, which combined with the higher levels of immune checkpoints in Cluster 1 suggests that Cluster 1 is more consistent with an immune ‘hot tumor’ profile and may respond better to immune checkpoint inhibitors (ICIs). This study offers new insights regarding the potential role of sphingolipid metabolism-related lncRNAs as biomarkers in PAAD. The constructed SMLs and the SMLs-based clustering are valuable tools for predicting clinical outcomes in PAAD and provide a basis for clinical selection of individualized treatments.

Identifying CD1c as a potential biomarker by the comprehensive exploration of tumor mutational burden and immune infiltration in diffuse large B cell lymphoma.

Tumor mutational burden (TMB) is a valuable prognostic biomarker. This study explored the predictive value of TMB and the potential association between TMB and immune infiltration in diffuse large B-cell lymphoma (DLBCL). We downloaded the gene expression profile, somatic mutation, and clinical data of DLBCL patients from The Cancer Genome Atlas (TCGA) database. We classified the samples into high-and low-TMB groups to identify differentially expressed genes (DEGs). Functional enrichment analyses were performed to determine the biological functions of the DEGs. We utilized the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm to estimate the abundance of 22 immune cells, and the significant difference was determined by the Wilcoxon rank-sum test between the high- and low-TMB group. Hub gene had been screened as the prognostic TMB-related immune biomarker by the combination of the Immunology Database and Analysis Portal (ImmPort) database and the univariate Cox analysis from the Gene Expression Omnibus (GEO) database including six DLBCL datasets. Various database applications such as Tumor Immune Estimation Resource (TIMER), CellMiner, konckTF, and Genotype-Tissue Expression (GTEx) verified the functions of the target gene. Wet assay confirmed the target gene expression at RNA and protein levels in DLBCL tissue and cell samples. Single nucleotide polymorphism (SNP) occurred more frequently than insertion and deletion, and C > T was the most common single nucleotide variant (SNV) in DLBCL. Survival analysis showed that the high-TMB group conferred poor survival outcomes. A total of 62 DEGs were obtained, and 13 TMB-related immune genes were identified. Univariate Cox analysis results illustrated that CD1c mutation was associated with lower TMB and manifested a satisfactory clinical prognosis by analysis of large samples from the GEO database. In addition, infiltration levels of immune cells in the high-TMB group were lower. Using the TIMER database, we systematically analyzed that the expression of CD1c was positively correlated with B cells, neutrophils, and dendritic cells and negatively correlated with CD8+ T cells, CD4+ T cells, and macrophages. Drug sensitivity showed a significant positive correlation between CD1c expression level and clinical drug sensitivity from the CellMiner database. CREB1, AHR, and TOX were used to comprehensively explore the regulation of CD1c-related transcription factors and signaling pathways by the KnockTF database. We searched the GETx database to compare the mRNA expression levels of CD1c between DLBCL and normal tissues, and the results suggested a significant difference between them. Moreover, wet experiments were conducted to verify the high expression of CD1c in DLBCL at the RNA and protein levels. Higher TMB correlated with poor survival outcomes and inhibited the immune infiltrates in DLBCL. Our results suggest that CD1c is a TMB-related prognostic biomarker.

Leveraging diverse cell-death related signature predicts the prognosis and immunotherapy response in renal clear cell carcinoma.

Modulation of programmed cell death in tumor cells alters the tumor microenvironment and the influx of tumor-infiltrating lymphocytes, and the combination of its inducers and immune checkpoint inhibitors plays a synergistic role in enhancing antitumor effects. We downloaded the data of clear cell renal cell carcinoma samples from The Cancer Genome Atlas and used a machine learning approach to build a new programmed cell death index (PCDI) through 13 programmed cell death-related genes. Based on PCDI, clinical features, tumor immune microenvironment, chemotherapy response and immunotherapy response were systematically analyzed. PCDI consists of eight programmed cell death-related genes (TBX3, BID, TCIRG1, IDUA, KDR, PYCARD, IFNG and LRRK2). PCDI is a reliable predictor of survival in clear cell renal cell carcinoma patients and has been validated in multiple external datasets. We found that the high PCDI group showed higher levels of immune cell infiltration and better response to immunotherapy compared to the low PCDI group, and PCDI can also be used for prognostic prediction in a variety of cancers other than clear cell renal cell carcinoma. In vitro experiments demonstrated that knockdown of IDUA inhibited the proliferation and migration of clear cell renal cell carcinoma. The PCDI identified in this study provides valuable insights into the clinical management of clear cell renal cell carcinoma by accurately evaluating the prognosis of patients with clear cell renal carcinoma and identifying the patient population that would benefit from immunotherapy.

Identification and analysis of diverse cell death patterns in diabetic kidney disease using microarray-based transcriptome profiling and single-nucleus RNA sequencing

BackgroundDiabetic kidney disease (DKD) is the most lethal complication of diabetes. Diverse programmed cell death (PCD) has emerged as a crucial disease phenotype that has the potential to serve as an indicator of renal function decline and can be used as a target for researching drugs for DKD. MethodsMicroarray-based transcriptome profiling and single-nucleus transcriptome sequencing (snRNA-seq) related to DKD were retrieved from the Gene Expression Omnibus (GEO) database. 13 PCD-related genes (including alkaliptosis, apoptosis, autophagy-dependent cell death, cuproptosis, disulfidptosis, entotic cell death, ferroptosis, lysosome-dependent cell death, necroptosis, netotic cell death, oxeiptosis, parthanatos, and pyroptosis) were obtained from various public databases and reviews. The gene set variation analysis (GSVA) analysis was used to explore the pathway activity of these 13 PCDs in DKD, and the pathway activity of these PCDs in different renal cells was studied based on DKD-related snRNA-seq data. To identify the core PCDs that play a significant role in DKD, we analyzed the relationships between different types of PCD and immune infiltration, fibrosis-related gene expression levels, glomerular filtration rate (GFR), and diagnostic efficiency in DKD. Using the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm, we screened for core death genes among the core PCDs and constructed a cell death-related signature (CDS) risk score based on the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, we validated the predictive performance of the CDS risk score in an independent validation set. ResultsWe identified 4 core PCD pathways, namely entotic cell death, apoptosis, necroptosis, and pyroptosis in DKD, and further applied the WGCNA algorithm to screen 4 core death genes (CASP1, CYBB, PLA2G4A, and CTSS) and constructed a CDS risk score based on these genes. The CDS risk score demonstrated high diagnostic efficiency for DKD patients, and those with higher scores had higher levels of immune cell infiltration and poorer GFR. ConclusionOur study sheds light on the fact that multiple PCDs contribute to the progression of DKD, highlighting potential therapeutic targets for treating this disease.

A pan-cancer analysis indicates long noncoding RNA HAND2-AS1 as a potential prognostic, immunomodulatory and therapeutic biomarker in various cancers including colorectal adenocarcinoma

The HAND2-AS1 (HAND2 Antisense RNA 1) Long noncoding RNA (lncRNA) has emerged as a participant in the initiation of various cancer types, underscoring its pivotal involvement in both oncological processes and immune responses. To gain deeper insights into the functional nuances of HAND2-AS1 and identify novel avenues for cancer immunotherapy, a comprehensive evaluation of this gene was undertaken. Here, based on the co-expression network analysis and construction of interacting lncRNA–mRNA genes, we introduce the HAND2-AS1 lncRNA, emphasizing its key roles in tumorigenesis and immune regulation. Our study spans across 33 distinct cancer types, revealing the HAND2-AS1’s aberrant expression patterns, methylation variations, mutational signatures, and immune engagement. Across a majority of tumors, HAND2-AS1 exhibited a propensity for down-regulation, remarkably an association with poor survival outcomes. The outcomes of functional enrichment analyses strongly suggest HAND2-AS1’s engagement in tumor progression and its association with various immune pathways across diverse tumor classifications. Additionally, a positive correlation emerged between HAND2-AS1 expression and the infiltration levels of key immune cells, encompassing not only immunosuppressive entities such as tumor-associated macrophages, cancer-associated fibroblasts, and Tregs, but also immune effector cells like NK cells and CD8+ T cells, spanning a pan-cancer context. Furthermore, the differential expression of HAND2-AS1 appears to have downstream consequences on various pathways, thus implicating it as a potential regulator in diverse cancer types. Finally, we have employed CRC tumor and normal samples to carry out clinical validation of HAND2-AS1. Our study unveils HAND2-AS1’s potential as a pan-cancer tumor suppressor, and its essential role in the tumorigenesis and immune surveillance. The increased HAND2-AS1 expression emerges as a promising candidate for prognostic evaluation, therapeutic strategy, and a focal point for immunotherapeutic interventions.

A novel prognostic and therapeutic target biomarker based on complement-related gene signature in gastric cancer.

Gastric cancer (GC) is one of the most prevalent cancer types that reduce human life expectancy. The current tumor-node-metastasis (TNM) staging system is inadequate in identifying higher or lower risk of GC patients because of tumor heterogeneity. Research shows that complement plays a dual role in the tumor development and progression of GC. We downloaded GC data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A complement-related risk signature was constructed through bioinformatics analysis. Subsequently, the predictive ability of this signature was validated with GSE84437 dataset, and a nomogram integrating risk score and common clinical factors was established. Besides, we evaluated the association of risk score with the immune and stromal cell infiltration in TCGA. Furthermore, immunotherapy response prediction and drug susceptibility analysis were conducted to access the ability of the risk signature in predicting the therapeutic effect. A complement-related gene (CRG) signature, based on six genes (SPLG, C9, ITIH1, ZFPM2, CD36, and SERPINE1), was established. In both the training and validation sets, the overall survival of GC patients in the high-risk group was lower than that of the low-risk group, and the nomogram to predict the 1-, 2-, and 3-year survival rates of GC patients was developed. In addition, CIBERSORT algorithm showed the high-risk patients had higher levels of immune cell infiltration than low-risk patients, and the ESTIMATE results implied that the high-risk group had more stromal component in tumor microenvironment. Besides, compared to the low-risk group, there were higher expressions of most immune checkpoint genes and HLA genes in the high-risk group, and the high-risk patients showed higher sensitivity to the chemotherapy and targeted drugs (axitinib, dasatinib, pazopanib, saracatinib, sunitinib and temsirolimus). The novel CRG signature may act as a reliable, efficient tool for prognostic prediction and treatment guidance in future clinical practice.

RNA Sequencing and Bioinformatics Analysis to Reveal Potential Biomarkers in Patients with Combined Allergic Rhinitis and Asthma Syndrome.

Combined allergic rhinitis and asthma syndrome (CARAS) is a concurrent clinical or subclinical allergic symptom of diseases of the upper and lower respiratory tract. This study is the first to explore the expression profiles of mRNA, lncRNA, and circRNA in CARAS using RNA sequencing, which may provide insight into the mechanisms underlying CARAS. Whole blood samples from nine participants (three CARAS patients, three AR patients, and three normal control participants) were subjected to perform RNA sequencing, followed by identification of differentially expressed lncRNAs (DElncRNAs), circRNAs (DEcircRNAs) and mRNAs (DEmRNAs). Then, lncRNA/circRNA-mRNA regulatory pairs were constructed, followed by functional analysis, immune infiltration analysis, drug prediction, and expression validation with RT-qPCR and ELISA. The results showed that 61 DEmRNAs, 23 DElncRNAs and 3 DEcircRNAs may be related to the occurrence and development of CARAS. KRT8 may be implicated in the development of AR into CARAS. Three immunity-related mRNAs (IDO1, CYSLTR2, and TEC) and two hypoxia-related mRNAs (TKTL1 and VLDLR) were associated with the occurrence and development of CARAS. TEC may be considered a drug target for Dasatinib in treating CARAS. Several lncRNA/circRNA-mRNA regulatory pairs were identified in CARAS, including LINC00452/MIR4280HG/hsa_circ_0007272/hsa_circ_0070934-CLC, HEATR6-DT/LINC00639/LINC01783/hsa_circ_0008903-TEC, RP11-71L14.3-IDO1/SMPD3, RP11-178F10.2-IDO1/HRH4, and hsa_circ_0008903-CYSLTR2, which may indicate potential regulatory effects of lncRNAs/circRNAs in CARAS. Dysregulated levels of immune cell infiltration may be closely related to CARAS. The regulating effect of lncRNA/circRNA-immunity/hypoxia-related mRNA regulatory pairs may be involved in the occurrence and development of CARAS.

Identification of P3H1 as a Predictive Prognostic Biomarker for Bladder Urothelial Carcinoma Based on the Cancer Genome Atlas Database.

The extracellular matrix in the tumor microenvironment are closely related to the development of tumors. This study's primary aim is to study the association between prolyl 3-hydroxylase 1 (P3H1) which mainly expresses collagen in extracellular matrix and the progression and prognosis of bladder cancer (BC). The clinical and transcriptome data were acquired from the cancer genome atlas database. BLCAsubtyping is used to evaluate tissue subtypes of BC. The COX proportional hazards can be used to evaluate the survival process's influencing factors. Immunohistochemistry was used to identify differences in the expression of P3H1 in cancer and paired adjacent tissues. GSEA was used to investigate the underlying biological processes. Finally, ssGSEA, TIMER and pRRophetic were used to study the relationship between P3H1 and immune cell infiltration and drug sensitivity. The expression of P3H1 was substantially higher in highly invasive BC samples than in low invasive BC. P3H1 was an independent predictor of overall survival (HR = 1.12, p = 0.03). P3H1 expression was significantly higher in tumor tissues than adjacent normal tissues in clinical tissue samples, and was significantly higher in highly stage cancer than low stage cancer samples. Samples with high P3H1 expression had a higher level of immune cell infiltration and immune function, as well as a significant correlation with macrophage and dendritic cell infiltration and TGF-beta, Th1 cells, and macrophage regulation (cor >0.3, p <0.05). P3H1 high expression samples were substantially more sensitive to docetaxel, cisplatin, vinblastine, camptothecin, paclitaxel, and other medicines than P3H1 low expression samples. P3H1 is a possible oncogene and an independent predictor of poor prognosis in BC; it also has enhanced sensitivity to docetaxel, cisplatin, vinblastine, camptothecin, paclitaxel, and other medications.

Boiling Histotripsy Using Dual-Frequency Protocol on Murine Breast Tumor Model and Promotes Immune Activation.

Histotripsy is an ultrasound-guided, noninvasive, nonthermal ablation therapy that can mechanically lyse target tissues. There have been no reports of enhanced histotripsy for large-volume triple-negative breast cancer (TNBC). This study aims to verify the ability of a novel approach of dual-frequency mode combined with two-stage millisecond-length ultrasound pulses (DF-TS) to accelerate the treatment of murine subcutaneous 4T1 tumors and determine immune changes after treatment. A custom-designed 1.1-/2.2-MHz two-element confocal-annular array was used to treat approximately 6-mm tumors under ultrasound guidance and real-time monitoring. Two-stage millisecond-length ultrasound pulses were used to generate approximate cuboid ablation volumes (diagonal 5-6 mm) within each tumor, with a dose of 100 pulses/point. Immune effects were characterized by changes of pro-inflammatory cytokine levels and infiltration levels of immune cells. In all targeted treatment areas, bubble cloud activity was visualized by ultrasound monitoring. The novel protocol resulted in elliptical and controllable sized lesions, reducing the number of scanning points, and was generally well tolerated. After treatment, tumor growth experienced a seven-day stagnation period, the survival period of mice was prolonged, and the levels of pro-inflammatory cytokines and immune cell infiltration increased. This study demonstrates that DF-TS boiling histotripsy (BH) has a noninvasive, efficient, and precise ablation ability for TNBC and potentially enhances immune responses.

Genetic variants in hypoxia-inducible factor pathway are associated with colorectal cancer risk and immune infiltration.

Hypoxia-inducible factor (HIF) pathway genes influence tumorigenesis and immune status. However, the associations between genetic variants in hypoxia-related genes and colorectal cancer risk and the immune status of hypoxia-associated genes in colorectal cancer have not been systematically characterized. The associations between genetic variants and colorectal cancer risk were evaluated in Chinese, Japanese and European populations using logistic regression analysis. The relationships between target genes and tumour immune infiltration were predicted by Tumour Immune Estimation Resource (TIMER). We found that rs34533650 in EPAS1 was associated with colorectal cancer risk (OR = 1.43, 95% CI = 1.20-1.70, P(FDR) = 8.35 × 10-4 ), and this finding was validated in two independent populations (Japanese: OR = 1.07, 95% CI = 1.01-1.15, p = 3.38 × 10-2 ; European: OR = 1.11, 95% CI = 1.03-1.19, p = 6.04 × 10-3 ). EPAS1-associated genes were enriched in immune-related pathways. In addition, we found that EPAS1 copy number variation (CNV) was associated with the degree of infiltration of immune cells and observed correlations between EPAS1 expression and immune cell infiltration levels in colorectal cancer. These results highlight that genetic variants of hypoxia-related genes play roles in colorectal cancer risk and provide new insight that EPAS1 might be a promising predictor of colorectal cancer susceptibility and immune status.

The mitochondrial energy metabolism pathway-related signature predicts prognosis and indicates immune microenvironment infiltration in osteosarcoma.

Abnormalities in the mitochondrial energy metabolism pathways are closely related to the occurrence and development of many cancers. Furthermore, abnormal genes in mitochondrial energy metabolism pathways may be novel targets and biomarkers for the diagnosis and treatment of osteosarcoma. In this study, we aimed to establish a mitochondrial energy metabolism-related gene signature for osteosarcoma prognosis. We first obtained differentially expressed genes based on the metastatic status of 84 patients with osteosarcoma from the TARGET database. After Venn analysis of differentially expressed genes and mitochondrial energy metabolism pathway-related genes (MMRGs), 2 key genes were obtained using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis. Next, we used these 2 genes to establish a prognostic signature. Subsequent analyses elucidated the correlation between these 2 key genes with clinical features and 28 types of immune cells. Pathway changes in osteosarcoma pathogenesis under different metastatic states were clarified using gene set enrichment analysis (GSEA) of differentially expressed genes. A gene signature composed of 2 key prognosis-related genes (KCNJ5 and PFKFB2) was identified. A risk score was calculated based on the gene signature, which divided osteosarcoma patients into low- or high-risk groups that showed good and poor prognosis, respectively. High expression of these 2 key genes is associated with low-risk group in patients with osteosarcoma. We constructed an accurate nomogram to help clinicians assess the survival time of patients with osteosarcoma. The results of immune cell infiltration level showed that the high-risk group had lower levels of immune cell infiltration. GSEA revealed changes in immune regulation and hypoxia stress pathways in osteosarcoma under different metastatic states. Our study identified an excellent gene signature that could be helpful in improving the prognosis of patients with osteosarcoma.

Construction and validation of a transient receptor potential-related long noncoding RNA signature for prognosis prediction in breast cancer patients.

Breast cancer (BC) is the most commonly diagnosed malignancy in women around the world. Accumulating evidence suggests that transient receptor potential (TRP) channels play a significant role in tumor progression and immune cell infiltration. Hence, we conducted the study to investigate the correlation between TRP-associated lncRNAs and the prognosis of breast carcinoma. In the current study, 33 TRP-associated genes were selected from a review published by Amrita Samanta et al, and the TRP-related lncRNAs were identified by Pearson analysis. Based on the sum of the expression levels of 12 lncRNAs provided by the Cancer Genome Atlas (TCGA), a TRP-associated lncRNA signature was established by using Cox regression analysis. According to the median value of the risk score in the training set, BC patients were separated into high- and low-risk groups. Subsequently, functional enrichment analysis was conducted on the differential expression genes (DEGs) between different risk groups. The Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression (ESTIMATE) Score was calculated by ESTIMATE, and the immune cell infiltration was evaluated by ssGSEA. Finally, the immune checkpoint gene expression levels, microsatellite instability (MSI), and immunophenoscore (IPS) were further assessed. The high-risk groups exhibited lower survival rates, while the low-risk groups showed higher survival rates. As a result, the DEGs between different risk groups were highly enriched in immune cell activation and immunoregulation. Besides, the ESTIMATE scores of patients in low-risk groups were higher than those in high-risk groups. The infiltration levels of several immune cells were remarkably elevated in low-risk groups, and various immune signatures were activated with a decreased risk score. Eventually, the TRP-associated lncRNA signature was confirmed with a highly potential ability to evaluate the immunotherapy response in breast carcinoma patients. The outcomes of the current study indicated that the 12-TRP-associated-lncRNA risk model was an independent prognostic risk factor for BC patients. This risk model could be closely related to the tumor immune microenvironment in BC. Our findings will provide new insights for future immunotherapy for BC treatment.

Selection of M7G-related lncRNAs in kidney renal clear cell carcinoma and their putative diagnostic and prognostic role

BackgroundKidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. This study aims to develop new biomarkers for KIRC and explore the impact of biomarkers on the immunotherapeutic efficacy for KIRC, providing a theoretical basis for the treatment of KIRC patients.MethodsTranscriptome data for KIRC was obtained from the The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Weighted gene co-expression network analysis identified KIRC-related modules of long noncoding RNAs (lncRNAs). Intersection analysis was performed differentially expressed lncRNAs between KIRC and normal control samples, and lncRNAs associated with N(7)-methylguanosine (m7G), resulting in differentially expressed m7G-associated lncRNAs in KIRC patients (DE-m7G-lncRNAs). Machine Learning was employed to select biomarkers for KIRC. The prognostic value of biomarkers and clinical features was evaluated using Kaplan-Meier (K-M) survival analysis, univariate and multivariate Cox regression analysis. A nomogram was constructed based on biomarkers and clinical features, and its efficacy was evaluated using calibration curves and decision curves. Functional enrichment analysis was performed to investigate the functional enrichment of biomarkers. Correlation analysis was conducted to explore the relationship between biomarkers and immune cell infiltration levels and common immune checkpoint in KIRC samples.ResultsBy intersecting 575 KIRC-related module lncRNAs, 1773 differentially expressed lncRNAs, and 62 m7G-related lncRNAs, we identified 42 DE-m7G-lncRNAs. Using XGBoost and Boruta algorithms, 8 biomarkers for KIRC were selected. Kaplan-Meier survival analysis showed significant survival differences in KIRC patients with high and low expression of the PTCSC3 and RP11-321G12.1. Univariate and multivariate Cox regression analyses showed that AP000696.2, PTCSC3 and clinical characteristics were independent prognostic factors for patients with KIRC. A nomogram based on these prognostic factors accurately predicted the prognosis of KIRC patients. The biomarkers showed associations with clinical features of KIRC patients, mainly localized in the cytoplasm and related to cytokine-mediated immune response. Furthermore, immune feature analysis demonstrated a significant decrease in immune cell infiltration levels in KIRC samples compared to normal samples, with a negative correlation observed between the biomarkers and most differentially infiltrating immune cells and common immune checkpoints.ConclusionIn summary, this study discovered eight prognostic biomarkers associated with KIRC patients. These biomarkers showed significant correlations with clinical features, immune cell infiltration, and immune checkpoint expression in KIRC patients, laying a theoretical foundation for the diagnosis and treatment of KIRC.

Identification and validation of autophagy-related genes in primary open-angle glaucoma

BackgroundAs the most common type of glaucoma, the etiology of primary open-angle glaucoma (POAG) has not been unified. Autophagy may affect the occurrence and development of POAG, while the specific mechanism and target need to be further explored.MethodsThe GSE27276 dataset from the Gene Expression Omnibus (GEO) database and the autophagy gene set from the GeneCards database were selected to screen differentially expressed autophagy-related genes (DEARGs) of POAG. Hub DEARGs were selected by constructing protein-protein interaction (PPI) networks and utilizing GSE138125 dataset. Subsequently, immune cell infiltration analysis, genome-wide association study (GWAS) analysis, gene set enrichment analysis (GSEA) and other analyses were performed on the hub genes. Eventually, animal experiments were performed to verify the mRNA levels of the hub genes by quantitative real time polymerase chain reaction (qRT-PCR).ResultsA total of 67 DEARGs and 2 hub DEARGs, HSPA8 and RPL15, were selected. The hub genes were closely related to the level of immune cell infiltration. GWAS analysis confirmed that the causative regions of the 2 hub genes in glaucoma were on chromosome 11 and chromosome 3, respectively. GSEA illustrated that pathways enriched for highly expressed HSPA8 and RPL15 contained immunity, autophagy, gene expression and energy metabolism-related pathways. qRT-PCR confirmed that the expression of Hspa8 and Rpl15 in the rat POAG model was consistent with the results of bioinformatics analysis.ConclusionsThis study indicated that HSPA8 and RPL15 may affect the progression of POAG by regulating autophagy and provided new ideas for the pathogenesis and treatment of POAG.

A novel hypoxia-associated gene signature for prognosis prediction in head and neck squamous cell carcinoma

BackgroundHead and neck squamous cell carcinoma (HNSCC) is the most common malignant tumor of head and neck, which seriously threatens human life and health. However, the mechanism of hypoxia-associated genes (HAGs) in HNSCC remains unelucidated. This study aims to establish a hypoxia-associated gene signature and the nomogram for predicting the prognosis of patients with HNSCC.MethodsPrevious literature reports provided a list of HAGs. The TCGA database provided genetic and clinical information on HNSCC patients. First, a hypoxia-associated gene risk model was constructed for predicting overall survival (OS) in HNSCC patients and externally validated in four GEO datasets (GSE27020, GSE41613, GSE42743, and GSE117973). Then, immune status and metabolic pathways were analyzed. A nomogram was constructed and assessed the predictive value. Finally, experimental validation of the core genes was performed by qRT-PCR.ResultsA HNSCC prognostic model was constructed based on 8 HAGs. This risk model was validated in four external datasets and exhibited high predictive value in various clinical subgroups. Significant differences in immune cell infiltration levels and metabolic pathways were found between high and low risk subgroups. The nomogram was highly accurate for predicting OS in HNSCC patients.ConclusionsThe 8 hypoxia-associated gene signature can serve as novel independent prognostic indicators in HNSCC patients. The nomogram combining the risk score and clinical stage enhanced predictive performance in predicting OS compared to the risk model and clinical characteristics alone.

Grid2 interacting protein is a potential biomarker related to immune infiltration in colorectal cancer

BackgroundColorectal cancer (CRC) is one of the three deadliest malignant tumors in the world, posing a severe hazard to human health. Nonetheless, the 5-year survival rate for advanced CRC remains unsatisfactory. Grid2 interacting protein (GRID2IP) is a Purkinje fiber postsynaptic scaffold protein implicated in a number of signal transduction pathways in the nervous system. Previous studies have shown that Grid2 is closely related to the occurrence and prognosis of gastric cancer and many other diseases. Therefore, we aim to identify the relationship between GRID2IP and the occurrence and prognosis of CRC.MethodsTranscriptome data were retrieved from The Cancer Genome Atlas (TCGA) database to analyze the differential expression of GRID2IP in a variety of malignant tumors and then validate it by quantitative real time polymerase chain reaction(Q-PCR) and Western Blot in HT29 and SW480 cells. "DESeq2" package was used to analyze the differentially expressed genes (DEGs) between the high- and low-GRID2IP subgroups. In relation to DEGs, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. In addition, gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were employed to examine DEGs-associated signaling pathways and GRID2IP-associated immune cell infiltration levels. Besides, overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) were compared between the two subgroups using a Kaplan–Meier analysis. In addition, a prognostic model for GRID2IP and clinical characteristics was developed using the univariate Cox regression method. The "pRRophetic" package was applied to predict the drug sensitivity of different subgroups. Moreover, we also performed single-cell analysis of GRID2IP using the TISCH database.ResultsGRID2IP is upregulated in CRC patients. The rise of GRID2IP inhibits the invasion of tumor-associated immune cells resulting in a lower immune score. In addition, high GRID2IP expression was associated with poor prognosis in different clinical subgroups. Analysis of single cells revealed that GRID2IP was predominantly expressed in immune cells, myofibroblasts, and cancerous cells. In terms of chemotherapy drug sensitivity, the subgroup with high GRID2IP expression was less sensitive to gemcitabine.ConclusionsOur results suggest that rising GRID2IP promotes tumor-associated immune cell infiltration and suggests adverse outcomes in CRC patients, which may be a useful biomarker for determining the prognosis of CRC and a potential target molecule for CRC therapy.