PRF1 is a prognostic marker and correlated with immune infiltration in head and neck squamous cell carcinoma

Signature identification based on immunogenic cell death-related lncRNAs to predict the prognosis and immune activity of patients with endometrial carcinoma

Background

Endometrial carcinoma (EC) is one of the most prevalent gynecologic malignancies and requires further classification for treatment and prognosis. Long non-coding RNAs (lncRNAs) and immunogenic cell death (ICD) play a critical role in tumor progression. Nevertheless, the role of lncRNAs in ICD in EC remains unclear. This study aimed to explore the role of ICD related-lncRNAs in EC via bioinformatics and establish a prognostic risk model based on the ICD-related lncRNAs. We also explored immune infiltration and immune cell function across prognostic groups and made treatment recommendations.

Methods

A total of 552 EC samples and clinical data of 548 EC patients were extracted from The Cancer Genome Atlas (TCGA) database and University of California Santa Cruz (UCSC) Xena, respectively. A prognostic-related feature and risk model was developed using the least absolute shrinkage and selection operator (LASSO). Subtypes were classified with consensus cluster analysis and validated with t-Distributed Stochastic Neighbor Embedding (tSNE). Kaplan-Meier analysis was conducted to assess differences in survival. Infiltration by immune cells was estimated by single sample gene set enrichment analysis (ssGSEA), Tumor IMmune Estimation Resource (TIMER) algorithm. Quantitative polymerase chain reaction (qPCR) was used to detect lncRNAs expression in clinical samples and cell lines. A series of studies was conducted in vitro and in vivo to examine the effects of knockdown or overexpression of lncRNAs on ICD.

Results

In total, 16 ICD-related lncRNAs with prognostic values were identified. Using SCARNA9, FAM198B-AS1, FKBP14-AS1, FBXO30-DT, LINC01943, and AL161431.1 as risk model, their predictive accuracy and discrimination were assessed. We divided EC patients into high-risk and low-risk groups. The analysis showed that the risk model was an independent prognostic factor. The prognosis of the high- and low-risk groups was different, and the overall survival (OS) of the high-risk group was lower. The low-risk group had higher immune cell infiltration and immune scores. Consensus clustering analysis divided the samples into four subtypes, of which cluster 4 had higher immune cell infiltration and immune scores.

Conclusions

A prognostic signature composed of six ICD related-lncRNAs in EC was established, and a risk model based on this signature can be used to predict the prognosis of patients with EC.

Cellular immunity analysis by a modular acoustofluidic platform: CIAMAP

The study of molecular mechanisms at the single-cell level holds immense potential for enhancing immunotherapy and understanding neuroinflammation and neurodegenerative diseases by identifying previously concealed pathways within a diverse range of paired cells. However, existing single-cell pairing platforms have limitations in low pairing efficiency, complex manual operation procedures, and single-use functionality. Here, we report a multiparametric cellular immunity analysis by a modular acoustofluidic platform: CIAMAP. This platform enables users to efficiently sort and collect effector-target (i.e., NK92-K562) cell pairs and monitor the real-time dynamics of immunological response formation. Furthermore, we conducted transcriptional and protein expression analyses to evaluate the pathways that mediate effector cytotoxicity toward target cells, as well as the synergistic effect of doxorubicin on the cellular immune response. Our CIAMAP can provide promising building blocks for high-throughput quantitative single-cell level coculture to understand intercellular communication while also empowering immunotherapy by precision analysis of immunological synapses.

A novel defined risk signature of cuproptosis-related long non-coding RNA for predicting prognosis, immune infiltration, and immunotherapy response in lung adenocarcinoma

Background: Cuproptosis is a newly discovered non-apoptotic form of cell death that may be related to the development of tumors. Nonetheless, the potential role of cuproptosis-related lncRNAs in tumor immunity formation and patient-tailored treatment optimization of lung adenocarcinoma (LUAD) is still unclear. Methods: RNA sequencing and survival data of LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) database for model training. The patients with LUAD in GSE29013, GSE30219, GSE31210, GSE37745, and GSE50081 were used for validation. The proofed cuproptosis-related genes were extracted from the previous studies. The Pearson correlation was applied to select cuproptosis-related lncRNAs. We chose differentially expressed cuproptosis-related lncRNAs in the tumor and normal tissues and allowed them to go to a Cox regression and a LASSO regression for a lncRNA signature that predicts the LUAD prognosis. Kaplan-Meier estimator, Cox model, ROC, tAUC, PCA, nomogram predictor, decision curve analysis, and real-time PCR were further deployed to confirm the model's accuracy. We examined this model's link to other regulated cell death forms. Applying TMB, immune-related signatures, and TIDE demonstrated the immunotherapeutic capabilities of signatures. We evaluated the relationship of our signature to anticancer drug sensitivity. GSEA, immune infiltration analysis, and function experiments further investigated the functional mechanisms of the signature and the role of immune cells in the prognostic power of the signature. Results: An eight-lncRNA signature (TSPOAP1-AS1, AC107464.3, AC006449.7, LINC00324, COLCA1, HAGLR, MIR4435-2HG, and NKILA) was built and demonstrated owning prognostic power by applied to the validation cohort. Each signature gene was confirmed differentially expressed in the real world by real-time PCR. The eight-lncRNA signature correlated with 2321/3681 (63.05%) apoptosis-related genes, 11/20 (55.00%) necroptosis-related genes, 34/50 (68.00%) pyroptosis-related genes, and 222/380 (58.42%) ferroptosis-related genes. Immunotherapy analysis suggested that our signature may have utility in predicting immunotherapy efficacy in patients with LUAD. Mast cells were identified as key players that support the predicting capacity of the eight-lncRNA signature through the immune infiltrating analysis. Conclusion: In this study, an eight-lncRNA signature linked to cuproptosis was identified, which may improve LUAD management strategies. This signature may possess the ability to predict the effect of LUAD immunotherapy. In addition, infiltrating mast cells may affect the signature's prognostic power.

Pyroptosis patterns influence the clinical outcome and immune microenvironment characterization in HPV-positive head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous tumor with diverse molecular pathological profiles. Recent studies have suggested the vital role of pyroptosis in tumor microenvironment. However, the expression patterns of pyroptosis in HPV-positive HNSCC are still unclear.

METHODS

Unsupervised clustering analysis was used to identify the pyroptosis patterns based on the RNA-sequencing data of 27 pyroptosis-related genes (PRGs) in HPV-positive HNSCC samples. Random forest classifier and artificial neural network were performed to screen the signature genes associated with pyroptosis, which were verified in two independent external cohorts and qRT-PCR experiment. Principal component analysis was used to develop a scoring system, namely Pyroscore.

RESULTS

The expression variations of 27 PRGs in HPV-positive HNSCC patients were analyzed from genomic and transcriptional domains. Two pyroptosis-related subtypes with distinct clinical outcomes, enrichment pathways and immune characteristics were identified. Next, six signature genes (GZMB, LAG3, NKG7, PRF1, GZMA and GZMH) associated with pyroptosis were selected for prognostic prediction. Further, a Pyroscore system was constructed to determine the level of pyroptosis in each patient. A low Pyroscore was featured by better survival time, increased immune cell infiltration, higher expression of immune checkpoint molecules and T cell-inflamed genes, as well as elevated mutational burden. The Pyroscore was also related to the sensitivity of chemotherapeutic agents.

CONCLUSIONS

The pyroptosis-related signature genes and Pyroscore system may be reliable predictors of prognosis and serve as mediators of immune microenvironment in patients with HPV-positive HNSCC.

Comprehensive analysis of immune-related genes associated with the microenvironment of patients with unexplained infertility

Background

Disturbances in immunological responses and modulation lead to implantation and pregnancy failure and might be involved in the pathogenesis of infertility. This project aimed to screen and identify immune-related genes as potential biomarkers for treatment.

Methods

Gene expression profiles were obtained from Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were screened using GEO 2R to explore potential biomarkers. Protein-protein interaction (PPI) network analysis and functional enrichment analysis were applied to explore possible mechanisms. The deconvolution algorithm [referred to as Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT)] was employed to assess tissue-infiltrating immune cells. Western blot analysis and immunohistochemistry (IHC) were conducted for determination of protein levels.

Results

In this research, we identified 24 candidate immune-related DEGs via combined DEGs and functional analysis. We also found that the ratio of M0 macrophages and resting mast cells was higher in infertile group (P<0.05), whereas the amounts of activated natural killer (NK) cells was significantly lower compared with the control group (P<0.05). Furthermore, we evaluated the relationship between immune cells and candidate genes and found that 17 genes were related to M0 macrophages, resting mast cells, or activated NK cells. The genes CD40, PRF1, and EDN3 were chosen based on validation from independent datasets. Finally, our clinical samples confirmed the expression of the 3 genes.

Conclusions

The study recognized 3 genes that are signatures and could be potential biomarkers for unexplained infertility. These genes might guide the immunotherapy of these patients and become new treatment targets.

Classification related to immunogenic cell death predicts prognosis, immune microenvironment characteristics, and response to immunotherapy in lower-grade gliomas

Background

Immunogenic cell death (ICD) is a form of cell death that elicits immune responses against the antigens found in dead or dying tumor cells. Growing evidence implies that ICD plays a significant role in triggering antitumor immunity. The prognosis for glioma remains poor despite many biomarkers being reported, and identifying ICD-related biomarkers is imminent for better-personalized management in patients with lower-grade glioma (LGG).

Materials and methods

We identified ICD-related differentially expressed genes (DEGs) by comparing gene expression profiles obtained across Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) cohorts. On the foundation of ICD-related DEGs, two ICD-related clusters were identified through consensus clustering. Then, survival analysis, functional enrichment analysis, somatic mutation analysis, and immune characteristics analysis were performed in the two ICD-related subtypes. Additionally, we developed and validated a risk assessment signature for LGG patients. Finally, we selected one gene (EIF2AK3) from the above risk model for experimental validation.

Results

32 ICD-related DEGs were screened, dividing the LGG samples from the TCGA database into two distinct subtypes. The ICD-high subgroup showed worse overall survival (OS), greater immune infiltration, more active immune response process, and higher expression levels of HLA genes than the ICD-low subgroup. Additionally, nine ICD-related DEGs were identified to build the prognostic signature, which was highly correlated with the tumor-immune microenvironment and could unambiguously be taken as an independent prognostic factor and further verified in an external dataset. The experimental results indicated that EIF2AK3 expression was higher in tumors than paracancerous tissues, and high-expression EIF2AK3 was enriched in WHO III and IV gliomas by qPCR and IHC, and Knockdown of EIF2AK3 suppressed cell viability and mobility in glioma cells.

Conclusion

We established novel ICD-related subtypes and risk signature for LGG, which may be beneficial to improving clinical outcome prediction and guiding individualized immunotherapy.

A more novel and powerful prognostic gene signature of lung adenocarcinoma determined from the immune cell infiltration landscape

Background

Lung adenocarcinoma (LUAD) is the leading histological subtype of lung cancer worldwide, causing high mortality each year. The tumor immune cell infiltration (ICI) is closely associated with clinical outcome with LUAD patients. The present study was designed to construct a gene signature based on the ICI of LUAD to predict prognosis.

Methods

Downloaded the raw data of three cohorts of the TCGA-LUAD, GSE72094, and GSE68465 and treat them as training cohort, validation cohort one, and validation cohort two for this research. Unsupervised clustering detailed grouped LUAD cases of the training cohort based on the ICI profile. The univariate Cox regression and Kaplan-Meier was adopted to identify potential prognostic genes from the differentially expressed genes recognized from the ICI clusters. A risk score-based prognostic signature was subsequently developed using LASSO-penalized Cox regression analysis. The Kaplan-Meier analysis, Cox analysis, ROC, IAUC, and IBS were constructed to assess the ability to predict the prognosis and effects of clinical variables in another two independent validation cohorts. More innovatively, we searched similar papers in the most recent year and made comprehensive comparisons with ours. GSEA was used to discover the related signaling pathway. The immune relevant signature correlation identification and immune infiltrating analysis were used to evaluate the potential role of the signature for immunotherapy and recognize the critical immune cell that can influence the signature's prognosis capability.

Results

A signature composed of thirteen gene including ABCC2, CCR2, CERS4, CMAHP, DENND1C, ECT2, FKBP4, GJB3, GNG7, KRT6A, PCDH7, PLK1, and VEGFC, was identified as significantly associated with the prognosis in LUAD patients. The thirteen-gene signature exhibited independence in evaluating the prognosis of LUAD patients in our training and validation cohorts. Compared to our predecessors, our model has an advantage in predictive power. Nine well know immunotherapy targets, including TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, and PDCD1 were recognized correlating with our signature. The mast cells were found to play vital parts in backing on the thirteen-gene signature's outcome predictive capacity.

Conclusions

Collectively, the current study indicated a robust thirteen-gene signature that can accurately predict LUAD prognosis, which is superior to our predecessors in predictive ability. The immune relevant signatures, TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, PDCD1, and mast cells infiltrating were found closely correlate with the thirteen-gene signature's power.

Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis

Methods

Gene expression profiles of GSE13904, GSE26378, GSE26440, GSE65682, and GSE69528 were obtained from the National Center for Biotechnology Information (NCBI). The differentially expressed genes (DEGs) were searched using limma software package. Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs and screen hub genes.

Results

A total of 108 DEGs were identified in the study, of which 67 were upregulated and 41 were downregulated. 15 superlative diagnostic biomarkers (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) for sepsis were identified by bioinformatics analysis.

Conclusion

15 hub genes (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) have been elucidated in this study, and these biomarkers may be helpful in the diagnosis and therapy of patients with sepsis.

Pyroptosis-related gene expression patterns and corresponding tumor microenvironment infiltration characterization in ovarian cancer

Pyroptosis, a form of inflammatory programmed cell death, is accompanied by inflammation and participate in the body's immune response. The expression of pyroptosis-related genes (PRGs) is associated with tumor prognosis in ovarian cancer (OC), but it is still unknown whether pyroptosis can affect tumor immune microenvironment (TME) of OC. Based on 30 PRGs, we comprehensively assessed the pyroptosis patterns by using PRGscore and correlated them with TME features in 474 OC patients. Finally, we identified three pyroptosis modification patterns and TME immune characteristics of these patterns were in response to three immune phenotypes (immune-desert, immune-inflamed, and immune-excluded phenotypes). PRGscore can predict patient survival, staging, grading, and immunotherapy efficacy. Low PRGscore was associated with better survival advantage and increased mutation burden. Low PRGscore patients showed significantly better therapeutic effects and clinical results in chemotherapy and immunotherapy. Besides, the capability of PRGscore in predicting prognosis and immunotherapy sensitivity was further verified in other three tumor cohorts. In conclusion, the comprehensive assessment of OC pyroptosis modifications can help enhancing our understanding of TME immune infiltration and provide better personalized treatment tactics for OC patients.

Novel immune subtypes identification of HER2-positive breast cancer based on immunogenomic landscape

HER2 positive BC is heterogeneous. But few studies discussed the classification of HER2-positive BC based on immune-related signatures. Using three publicly BC genomics datasets, we classified HER2 positive BC based on 33 immune-related signatures and used unsupervised machine learning methods to predict and perform the classification. We grouped three HER2-positive BC subtypes that we called Immune-High (IM-H), Immune-Medium (IM-M), and Immune-Low (IM-L), and manifested this categorization was predictable, duplicable and reliable by analyzing another dataset. Compared to other subtypes, IM-H had a higher immune cell infiltration level and stronger anti-tumor immune activities, as well as better clinical survival outcome. Besides these signatures, there were some cancer-related pathways which were hyperactivated in IM-H, including cytokine-cytokine receptor interactions, antigen processing and presentation pathways, natural killer cell-mediated cytotoxicity, Th1 and Th2 cell differentiation, chemokine signaling pathway, Th17 cell differentiation, B and T cell receptor signaling, NF-kappa B signaling, PD-L1 expression and PD-1 checkpoint pathway in cancer, TNF signaling, IL-17 signaling, NOD-like receptor signaling and Toll-like receptor signaling. By contrast, IM-L showed depressed immune-related signatures and enhanced activation of lycosylphosphatidylinositol-anchor biosynthesis and mismatch repair. Moreover, we discovered a gene co-expression network focused on eight transcription factor genes (EOMES, TBX21, GFI1, IRF4, POU2AF1, CIITA, FOXP3 and TOX) and one tumor suppress gene (PRF1), which were closely related with tumor immune. We identified three HER2-positive BC subtypes based on immune-related signatures, which had potential clinical implications and promoted the optimal stratification of HER2-positive BC responsive to immunotherapy.

A pancancer analysis of the carcinogenic role of receptor-interacting serine/threonine protein kinase-2 (RIPK2) in human tumours

Background

To explore the expression and carcinogenic mechanism of RIPK2 in human tumours, and to provide the theoretical basis for the further study of RIPK2.

Methods

We used the TCGA, CPTAC, HPA databases to analyse the expression, mutation, and prognosis of RIPK2 in human tumours. Through the Cbioportal, Ualcan, TIMER2.0, and STRING websites, We understand the genetic variation, immune infiltration and enrichment analysis of RIPK2 related genes.

Results

RIPK2 was highly expressed in most tumours (such as BRCA, COAD and LUSC, etc.), and the high expression of RIPK2 was correlated with tumour stage and prognosis. In addition, Amplification was the main type of RIPK2 in tumour mutation state, and the amplification rate was about 8.5%. In addition, RIPK2 was positively associated with tumour-infiltrating immune cells (such as CD8+ T, Tregs, and cancer-associated fibroblasts). According to the KEGG analysis, RIPK2 may play a role in tumour mainly through NOD-like signaling pathway and NF-kappaB signaling pathway. GO enrichment analysis showed that the RIPK2 is mainly related to I-kappaB kinase/NF-kappaB signaling, Ribonucleoprotein granule and Ubiquitin-like protein ligase binding.

Conclusion

RIPK2 plays an important role in the occurrence, development and prognosis of malignant tumours. Our pancancer study provided a relatively comprehensive description of the carcinogenic effects of RIPK2 in different tumours, and provided useful information for further study of RIPK2.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01239-3.

Identification of Novel Molecular Therapeutic Targets and Their Potential Prognostic Biomarkers Based on Cytolytic Activity in Skin Cutaneous Melanoma

Skin cutaneous melanoma (SKCM) attracts attention worldwide for its extremely high malignancy. A novel term cytolytic activity (CYT) has been introduced as a potential immunotherapy biomarker associated with counter-regulatory immune responses and enhanced prognosis in tumors. In this study, we extracted all datasets of SKCM patients, namely, RNA sequencing data and clinical information from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database, conducted differential expression analysis to yield 864 differentially expressed genes (DEGs) characteristic of CYT and used non-negative matrix factorization (NMF) method to classify molecular subtypes of SKCM patients. Among all genes, 14 hub genes closely related to prognosis for SKCM were finally screen out. Based on these genes, we constructed a 14-gene prognostic risk model and its robustness and strong predictive performance were further validated. Subsequently, the underlying mechanisms in tumor pathogenesis and prognosis have been defined from a number of perspectives, namely, tumor mutation burden (TMB), copy number variation (CNV), tumor microenvironment (TME), infiltrating immune cells, gene set enrichment analysis (GSEA) and immune checkpoint inhibitors (ICIs). Furthermore, combined with GTEx database and HPA database, the expression of genes in the model was verified at the transcriptional level and protein level, and the relative importance of genes in the model was described by random forest algorithm. In addition, the model was used to predict the difference in sensitivity of SKCM patients to chemotherapy and immunotherapy. Finally, a nomogram was constructed to better aid clinical diagnosis.

Gene Expression Subtyping Reveals Immune alterations:TCGA Database for Prognosis in Ovarian Serous Cystadenocarcinoma

Serous ovarian cancer is the most common and primary death type in ovarian cancer. In recent studies, tumor microenvironment and tumor immune infiltration significantly affect the prognosis of ovarian cancer. This study analyzed the four gene expression types of ovarian cancer in TCGA database to extract differentially expressed genes and verify the prognostic significance. Meanwhile, functional enrichment and protein interaction network analysis exposed that these genes were related to immune response and immune infiltration. Subsequently, we proved these prognostic genes in an independent data set from the GEO database. Finally, multivariate cox regression analysis revealed the prognostic significance of TAP1 and CXCL13. The genetic alteration and interaction network of these two genes were shown. Then, we established a nomogram model related to the two genes and clinical risk factors. This model performed well in Calibration plot and Decision Curve Analysis. In conclusion, we have obtained a list of genes related to the immune microenvironment with a better prognosis for serous ovarian cancer, and based on this, we have tried to establish a clinical prognosis model.