Immune Score Research Articles

Subtyping colorectal cancer based on septic shock-associated genes: prognosis and immune characteristics

BackgroundSepsis and colorectal cancer (CRC) are leading causes of death. Given their mutual dependence for susceptibility, we used bioinformatics to explore potential connections between septic shock (SS) and CRC.MethodsWe identified 452 co-expressed genes between SS-related differential expression genes (SS-DEGs) and CRC patient-expressed genes (TCGA-CRC genes). CRC samples were categorized into two cluster subgroups through hierarchical clustering. We then compared the prognosis and immune landscapes of the two cluster subgroups through survival analysis, immune microenvironment analysis, and immune therapy response evaluation.ResultsClustering analysis of the 452 CRC patient-expressed SS-DEGs identified two subtypes: SS-like CRC (SL-CRC) and non-SS-like CRC (NSL-CRC). There were no significant differences in overall survival between the CRC subtypes. However, the subtypes displayed significant differences in immune score, stromal score, and ESTIMATE score. Based on immune therapy databases, there were also significant differences in responses to anti-CTLA-4 and anti-PD-1 immune checkpoint inhibitors between the subtypes.ConclusionOur study reveals significant differences in the immune microenvironment and immune therapy responses between SL-CRC and NSL-CRC subtypes. These findings provide a foundation for identifying new therapeutic targets and developing personalized treatment strategies for specific CRC subtypes.

Prognostic model development using novel genetic signature associated with adenosine metabolism and immune status for patients with hepatocellular carcinoma.

The high mortality rate of hepatocellular carcinoma (HCC) is partly due to advanced diagnosis, emphasizing the need for effective predictive tools in HCC treatment. The aim of this study is to propose a novel prognostic model for HCC based on adenosine metabolizing genes and explore the potential relationship between them. Regression analysis was performed to identify differentially expressed genes associated with adenosine metabolism in HCC patients using RNA sequencing data obtained from a public database. Adenosine metabolism-related risk score (AMrisk) was derived using the least absolute shrinkage and selection operator (LASSO) Cox regression and verified using another database. Changes in adenosine metabolism in HCC were analyzed using functional enrichment analysis and multiple immune scores. The gene expression levels in patient samples were validated using quantitative reverse transcription polymerase chain reaction. Thirty adenosine metabolism-related differentially expressed genes were identified in HCC, and six genes (ADA, P2RY4, P2RY6, RPIA, SLC6A3, and VEGFA) were used to calculate the AMrisk score; the higher the risk scores, the lower the overall survival. Moreover, immune infiltration activation and immune checkpoints were considerably higher in the high-risk group. Additional in vitro experiments validated the enhanced expression of these six genes in HCC. The established predictive model demonstrated that adenosine metabolism-related genes was significantly associated with prognosis in HCC patients.

Tumor Microenvironment in Clear Cell Renal Cell Carcinoma: A Comprehensive Analysis

Background: M6A RNA methylation and the tumor microenvironment (TME) have been reported to play important roles in the progression and prognosis of clear cell renal cell carcinoma (ccRCC). However, whether m6A RNA methylation regulators affect the TME in ccRCC remains unknown. Thus, we aimed to evaluate comprehensively the effect of m6A RNA methylation regulators on the TME in ccRCC. Methods: Transcriptome data of ccRCC were obtained from TCGA database. Consensus clustering analysis was conducted based on the expression of m6A RNA methylation regulators. Survival differences were evaluated by Kaplan–Meier analysis between the clusters. The DESeq2 package was used to analyze the differentially expressed genes (DEGs) between the clusters. GO and KEGG pathway analyses were performed by the ClusterProfiler R package. The CIBERSORT algorithm was used to evaluate immune infiltration. Results: The expression of 15 m6A regulators significantly differed between ccRCC and normal kidney tissues. Based on the expression of these 15 m6A regulators, two clusters were identified by consensus clustering, in which cluster 1 had better overall survival (OS). Overall, 4,429 DEGs were identified between the two clusters and were enriched in immune-related biological processes. Cluster 1 had lower immune and ESTIMATE scores, higher expression of HLA and lower expression of immune checkpoint molecules. Moreover, immune infiltration and expressions of Th1/IFNγ gene signature were also significantly different between the two clusters. Conclusion: Our study revealed m6A regulators were important participants in the development of ccRCC, with a close relationship with the TME.

A novel hypoxia- and lactate metabolism-related prognostic signature to characterize the immune landscape and predict immunotherapy response in osteosarcoma

BackgroundImmunotherapy has shown considerable promise in cancer treatment, yet only a minority of osteosarcoma patients derive benefits from this approach. Hypoxia and lactate metabolism are two predominant characteristics of the tumor microenvironment. These features are crucial for molding the immune landscape and thus have the potential to act as predictive indicators for immunotherapy response.MethodsPrognostic modeled genes were identified through univariate and multivariate Cox regression as well as LASSO regression analyses. The tumor microenvironment was evaluated using ESTIMATE, CIBERSORT, and ImmuCellAI analyses. Tide prediction and expression of immune checkpoints, MHC molecules, chemokines, interleukins, interferons, receptors, and other cytokines were utilized to estimate immunotherapy efficacy. Single-cell analysis was performed to demonstrate the expression of modeled genes among various immune cell types. Experimental validation was carried out to verify the expression and functions of SFXN4 and SQOR.ResultsA potent signature was constructed with 8 genes related to hypoxia and lactate metabolism, including MAFF, COL5A2, FAM162A, SQOR, UQCRB, SFXN4, PFKFB2 and COX6A2. A nomogram incorporating risk scores and other clinical features demonstrated excellent predictive capacity. Osteosarcoma patients with high-risk scores exhibited poor prognosis and more “cold” tumor characteristics. According to the ESTIMATE algorithm, these patients displayed lower immune, stromal, and ESTIMATE scores, partially attributed to inadequate infiltration of key immunocytes. The Ciborsort analysis similarly indicated that high-risk individuals had diminished infiltration of critical anti-tumor immune cells such as Cytotoxic T cells, CD4+ T cells, and NK cells. The low expression levels of certain immune checkpoints, MHC molecules, chemokines, interleukins, interferons, receptors, and other cytokines in high-risk cases suggested their unsatisfactory responses to immune treatment. Tide prediction further demonstrated that fewer individuals classified as high risk may exhibit sensitivity to immune checkpoint inhibitor therapy. Notably, SFXN4 was found to be highly expressed in osteosarcoma tissues and cells; it promoted the growth, migration, and invasion of osteosarcoma cells, while SQOR had the opposite effect.ConclusionOur research has developed a robust hypoxia- and lactate metabolism-related gene signature, providing a solid theoretical foundation for prognosis prediction, classification of “cold” and “hot” tumors, accessing immunotherapy response, and directing personalized treatment for osteosarcoma.

Multi-omics Data Integration Analysis Identified Therapeutic Targets and Potential Reuse Drugs for Osteoporosis.

To facilitate drug discovery and development for the treatment of osteoporosis. With global aging, osteoporosis has become a common problem threatening the health of the elderly. It is of important clinical value to explore new targets for drug intervention and develop promising drugs for the treatment of osteoporosis. To understand the major molecules that mediate the communication between the cell populations of bone marrow-derived mesenchymal stem cells (BM-MSCs) in osteoporosis and osteoarthritis patients and identify potential reusable drugs for the treatment of osteoporosis. Single-cell RNA sequencing (scRNA-seq) data of BM-MSCs in GSE147287 dataset were classified using the Seurat package. CellChat was devoted to analyzing the ligand-receptor pairs (LR pairs) contributing to the communication between BM-MSCs subsets. The LR pairs that were differentially expressed between osteoporosis samples and control samples and significantly correlated with immune score were screened in the GSE35959 dataset, and the differentially expressed gene in both GSE35959 and GSE13850 data sets were identified as targets from a single ligand or receptor. The therapeutic drugs for osteoporosis were screened by network proximity method, and the top-ranked drugs were selected for molecular docking and molecular dynamics simulation with the target targets. Twelve subsets of BM-MSCs were identified, of which CD45-BM-MSCS_4, CD45-BM- MSCS_5, and CD45+ BM-MSCs_5 subsets showed significantly different distributions between osteoporosis samples and osteoarthritis samples. Six LR pairs were identified in the bidirectional communication between these three BM-MSCs subsets and other BM-MSCs subsets. Among them, MIF-CD74 and ITGB2-ICAM2 were significantly correlated with the immune score. CD74 was identified as the target, and a total of 48 drugs targeting CD47 protein were identified. Among them, DB01940 had the lowest free energy binding score with CD74 protein and the binding state was very stable. This study provided a new network-based framework for drug reuse and identified initial insights into therapeutic agents targeting CD74 in osteoporosis, which may be meaningful for promoting the development of osteoporosis treatment.

Comprehensive Pan-cancer analysis of CD73: explore its association with prognosis and tumor immune microenvironment

BackgroundCD73 is adenosine generation molecule, which is involved in the immune regulation. However, the roles of CD73 in the tumor microenvironment remain unknown. MethodsCD73 expression levels in pan-cancers were analyzed, based on The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Human Protein ALTAS (HPA) databases. Kaplan-Meier (KM) plotter was used to analyze the prognostic values of CD73. Immune scores in pan-cancers was evaluated by ESTIMATE. TIMER2.0 and UCSCXena were used to explore the correlation between CD73 and immune infiltration/immune checkpoints/tumor mutation burden (TMB)/microsatellite instability (MSI). CD73 expression correlated genes in tumor tissues were screened by using LinkedOmics tool. ResultsFirstly, CD73 was significant increased in following cancer types: esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), brain lower grade glioma (LGG), lung adenocarcinoma (LUAD), pancreatic adenocarcinoma (PAAD) and stomach adenocarcinoma (STAD). Secondly, high CD73 expression was associated with poor overall survival in patients with breast invasive carcinoma (BRCA), CESC, HNSC, liver hepatocellular carcinoma (LIHC), LUAD, lung squamous cell carcinoma (LUSC), PAAD and STAD. However, in KIRC and UCEC, patients high CD73 expression showed a favorable prognosis. Thirdly, the immune scores of the high CD73 expression in bladder urothelial carcinoma (BLCA), BRCA, kidney chromophobe (KICH), LUAD, LUSC, ovarian serous cystadenocarcinoma (OV), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), and skin cutaneous melanoma (SKCM) were significant higher than that of patients with low CD73 expression. Furthermore, we observed a positive correlation between CD73 and the multiple immune cells infiltration, including CD4+ memory T cells, CD8+ T cells, Treg cells, myeloid DC and macrophage, particularly in BRCA and LUAD. There was no strong correlation between CD73 and TMB/MSI. In LUAD, CD73 expression correlated genes were mainly enrichment in positive regulation of cell proliferation, cell adhesion, positive regulation of kinase activity, cellular response to LPS. However, in UCEC, CD73 correlated genes were mainly associated with fatty acid omega-oxidation, protein localization to endoplasmic reticulum exit site, endoplasmic reticulum to Golgi vesicle-mediated transport. ConclusionCD73 could be used to predict the prognosis of patients with several cancers. The potential functional mechanism of CD73 involved in cancer progress may not only dependent on its immunomodulatory effects.

Identification of PANoptosis-based signature for predicting the prognosis and immunotherapy response in AML

BackgroundIn recent years, the incidence of acute myeloid leukemia (AML) has increased rapidly with a suboptimal prognosis. In AML, cell death is independent of tumorigenesis, tumor invasion, and drug resistance. PANoptosis is a newly discovered form of cell death that combines pyroptosis, apoptosis, and necroptosis. However, no studies have explored the role of PANoptosis-based signatures in AML. MethodsWe screened for PANoptosis-related genes and established a PANoptosis-risk signature using the least absolute shrinkage and selection operator (LASSO) and Cox regression analysis. We combined TCGA, bulk RNA sequencing, and single-cell sequencing to investigate the correlation between candidate genes and the AML tumor microenvironment. ResultsThe PANoptosis risk signature effectively predicted prognosis with good sensitivity and specificity. The risk score emerged as an independent prognostic factor. Functional enrichment analysis of PANoptosis-related differentially expressed genes suggested that the risk score may be related to cell immunity. Patients with high-risk scores exhibited increased immune cell infiltration, implying a hot tumor immune microenvironment. The risk score was positively correlated with the immune scores and expression levels of immune checkpoints. Therefore, we identified three model factors, BIRC3, PELI1, and PRKACG, as predictors for immunotherapy efficacy. Single-cell sequencing analysis demonstrated that PELI1 and BIRC3 may participate in the regulation of the AML immune microenvironment. Finally, we performed a drug sensitivity analysis to target BIRC3 and PELI1 using molecular docking and molecular dynamics simulations. ConclusionOur study established and verified a PANoptosis risk signature to predict the survival and immunological treatment response in AML.

Discovery of five diagnostic biomarkers associated with immune cell infiltration in cases of acute myocardial infarction.

Acute myocardial infarction (AMI) remains one of the leading causes of mortality and morbidity worldwide. GSE61144 and GSE66360 were the sources of microarray gene expression profiles for acute myocardial infarction patients and were acquired from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). After merging the datasets, genes that were differentially expressed were chosen. A total of 234 genes were found to have different expression levels. Of these, 206 genes were upregulated, and 28 genes were downregulated. Five coexpression modules were identified by WGCNA, with the yellow module showing a high correlation with AMI (r=0.65, P=2.0e-15). Ninety-two hub genes were selected in the yellow module by setting a threshold of module membership (MM) greater than 0.8 and gene significance (GS) higher than 0.4. By overlapping these genes with the differentially expressed genes, 81 hub genes were obtained. Five key genes (C5AR1, CXCL1, CXCL2, FPR1, and P2RY13) were identified through PPI analysis. AMI patients exhibited elevated levels of immune cell infiltration, and immune scores in AMI samples were significantly positively correlated with all five key genes. Moreover, the expression levels of these five genes were higher in AMI patients. These five genes possessed area under the curve (AUC) values exceeding 0.8 for diagnosing AMI, thereby demonstrating their efficacy as diagnostic markers. C5AR1, CXCL1, CXCL2, FPR1, and P2RY13 have the potential to be useful biomarkers in diagnosing AMI and are linked to immune cell infiltration in AMI, opening up new avenues for future research into the pathogenesis of AMI.

Deciphering Immune-related Gene Signatures in Diabetic Retinopathy: Insights from In silico Analysis and In vitro Experiment.

Diabetes retinopathy (DR) is one of the most common microvascular consequences of diabetes, and the economic burden is increasing. Our aim is to decipher the relevant mechanisms of immune-related gene features in DR and explore biomarkers targeting DR. Provide a basis for the treatment and prevention of DR. The immune infiltration enrichment score of DR patients was evaluated from the single- cell RNA sequencing dataset, and the samples were divided into low immune subgroups and high immune subgroups based on this result. Through weighted gene correlation network analysis, differentially expressed genes (DEGs) between two subgroups were identified and crossed with genes with the strongest immune association, resulting in significant key genes. Then divide the DR individuals into two immune related differentially expressed gene (IDEG) clusters, A and B. Submit cross DEGs between two clusters through Gene Set Enrichment Analysis (GSEA) to further explore their functions. A protein-protein interaction (PPI) network of IDEG was established to further identify central genes associated with DR. Use the discovered central genes to predict the regulatory network involved in the pathogenesis of DR. Then, the role of the identified hub gene in the pathogenesis of DR was further studied through in vitro experiments. We found that the immune scores of DR and control groups were different, and 27 IDEGs were found in the DR subgroup. Compared with cluster A, the proportion of cytotoxic lymphocytes, B lineage, monocyte lineage, and fibroblasts in DR patients in cluster B is significantly enriched. GSEA indicates that these genes are associated with T cell activation, regulation of immune response processes, lymphocyte-mediated immunity, TNF signaling pathway, and other signaling pathways. The PPI network subsequently identified 10 hub genes in DR, including SIGLEC10, RGS10, PENK, FGD2, LILRA6, CIITA, EGR2, SIGLEC7, LILRB1, and CD300LB. The upstream regulatory network and lncRNA miRNA mRNA ceRNA network of these hub genes were ultimately constructed. The discovery and identification of these genes will provide biomarkers for targeted prediction and treatment of DR. By integrating bioinformatics analysis and in vitro experiments, we have identified a set of central genes, indicating that these genes can serve as potential biomarkers for DR, which may be promising targets for future DR immunotherapy interventions.

Diagnostic and Prognostic Potential of FBXO8 Expression in Kidney Renal Clear Cell Carcinoma and Its Regulation of Renal Adenocarcinoma Cells

BackgroundThe F-box protein 8 Gene (FBXO8) has been shown to suppress invasion and metastasis in various cancer types. Recurrence and drug resistance pose significant challenges in renal cell carcinoma (RCC). Identifying novel biomarkers is crucial for addressing these issues. MethodsData on RNA sequencing and patient survival for KIRC was obtained from The Cancer Genome Atlas (TCGA), UALCAN, and Gene Expression Omnibus (GEO) databases. We confirmed FBXO8 gene expression and its impact on survival. Clinical characteristics were classified, and FBXO8 expression differences among various categories were observed. We conducted biofunctional predictions and analyzed the tumor microenvironment (TME), immune cell infiltration, and immune checkpoints in relation to FBXO8 expression. FBXO8 was overexpressed using a plasmid, and we assessed Kidney renal clear cell carcinoma (KIRC) cell proliferation, migration, and apoptosis through CCK8, wound healing tests, and western blot analysis. ResultsOur findings revealed decreased FBXO8 expression in KIRC, with patients exhibiting low FBXO8 expression experiencing shorter survival times. The low expression group showed elevated TME immune and estimate scores. Biofunctional analyses indicated that FBXO8 expression was notably linked to drug metabolism cytochrome P450, nutrition disease, receptor-ligand activity, and neuroactive ligand-receptor interaction. Furthermore, we discovered significant correlations between FBXO8 expression and immune cell infiltration, as well as checkpoints such as CD274. Overexpression (OE) of FBXO8 led to a marked reduction in cell proliferation and migration, along with increased apoptosis, as evidenced by apoptosis-related protein expression. ConclusionThis study demonstrates that FBXO8 serves as a biomarker for KIRC and plays a role in regulating cell proliferation, migration, and apoptosis.

The oncogenic functions of SPARCL1 in bladder cancer.

Secreted protein, acidic and rich in cysteine-like 1 (SPARCL1) belongs to the SPARC family of matricellular proteins. However, underlying functions of SPARCL1 in bladder cancer (BCa) remain understudied. We performed an integrated search for the expression patterns of SPARCL1 in relation to various clinicopathological features of BCa. We then carried out Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and gene set enrichment analysis (GSEA). Furthermore, we investigated the correlations between SPARCL1 and immunological features, such as tumour mutation burden (TMB), immune activation processes, immune checkpoint expression, tumour immune dysfunction and exclusion (TIDE) scores, and chemotherapeutic sensitivity in BCa. Our analysis revealed that SPARCL1 was downregulated across multiple cancers. In BCa, elevated SPARCL1 was linked with advanced histopathologic stage, higher T and N stage, and poorer prognosis in the clinical cohort. Invitro experiments demonstrated that increased SPARCL1 expression inhibited cell proliferation, migration, and invasion. Additionally, highly expressed SPARCL1 was linked to elevated immune, stromal and ESTIMATE scores, as well as an increase in naive B cells, M2 macrophages, and resting mast cells. We observed a moderate correlation between SPARCL1 expression and CD163, VSIG4 and MS4A4A, which are markers of M2 macrophages. Furthermore, SPARCL1 expression was positively related to TMB, immune activation processes, TIDE scores, immune checkpoint expression, and chemotherapeutic sensitivity in BCa. Our study highlights the potential involvement of SPARCL1 in macrophage recruitment and polarization and suggests its utility as a biomarker for prognosis in BCa.

Systemic Immune-Inflammation-Based Biomarker and Fragility Fractures in People Living With HIV: A 10-Year Follow-Up Cohort Study in China.

Fragility fractures are a significant concern among people living with HIV(PLWH) due to the combined effects of chronic inflammation, immune dysregulation, and antiretroviral therapy. Traditional biomarkers have limited predictive value for fragility fractures in this population. This study aims to evaluate the systemic immune inflammation-based scores as novel biomarkers for predicting fragility fractures in PLWH in China. We conducted a cohort study of PLWH in the orthopedic department of Beijing Ditan Hospital from January 2011 to September 2023. We monitored fragility fractures and collected data on demographics, clinical characteristics, and laboratory parameters. Multivariate Cox and logistic regression models were used to assess the predictive value of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) for fragility fractures. Restricted cubic splines (RCS) were employed to explore potential nonlinear relationships, and subgroup analyses were conducted to examine the stability of these associations. During a median follow-up of 5.5 years, our study included 1148 PLWH patients, and 204 patients (17.8%) experienced fragility fractures. After adjusting for all covariates, SII and SIRI were identified as independent risk factors for fragility fractures in PLWH, whereas NLR, PLR, and MLR were not. Patients with higher levels of SII and SIRI had a significantly increased risk of fragility fractures compared to those with lower levels (HR: 1.96, 95% CI: 1.24-3.10, p = 0.004; HR: 1.83, 95% CI: 1.16-2.88, p = 0.009). RCS analysis indicated a stable linear relationship between SIRI and fragility fractures. Furthermore, KM curves demonstrated that patients with higher SII and SIRI scores had a higher likelihood of experiencing fragility fractures. Our research shows that SII and SIRI are promising biomarkers for predicting fragility fractures in PLWH. Clinicians should consider incorporating SIRI into clinical practice to improve fracture risk stratification and guide preventive strategies for this vulnerable population.

Integrated bulk and single-cell profiling characterize sphingolipid metabolism in pancreatic cancer

BackgroundAbnormal sphingolipid metabolism (SM) is closely linked to the incidence of cancers. However, the role of SM in pancreatic cancer (PC) remains unclear. This study aims to explore the significance of SM in the prognosis, immune microenvironment, and treatment of PC.MethodsSingle-cell and bulk transcriptome data of PC were acquired via TCGA and GEO databases. SM-related genes (SMRGs) were obtained via MSigDB database. Consensus clustering was utilized to construct SM-related molecular subtypes. LASSO and Cox regression were utilized to build SM-related prognostic signature. ESTIMATE and CIBERSORT algorithms were employed to assess the tumour immune microenvironment. OncoPredict package was used to predict drug sensitivity. CCK-8, scratch, and transwell experiments were performed to analyze the function of ANKRD22 in PC cell line PANC-1 and BxPC-3.ResultsA total of 153 SMRGs were acquired, of which 48 were linked to PC patients’ prognosis. Two SM-related subtypes (SMRGcluster A and B) were identified in PC. SMRGcluster A had a poorer outcome and more active SM process compared to SMRGcluster B. Immune analysis revealed that SMRGcluster B had higher immune and stromal scores and CD8 + T cell abundance, while SMRGcluster A had a higher tumour purity score and M0 macrophages and activated dendritic cell abundance. PC with SMRGcluster B was more susceptible to gemcitabine, paclitaxel, and oxaliplatin. Then SM-related prognostic model (including ANLN, ANKRD22, and DKK1) was built, which had a very good predictive performance. Single-cell analysis revealed that in PC microenvironment, macrophages, epithelial cells, and endothelial cells had relatively higher SM activity. ANKRD22, DKK1, and ANLN have relatively higher expression levels in epithelial cells. Cell subpopulations with high expression of ANKRD22, DKK1, and ANLN had more active SM activity. In vitro experiments showed that ANKRD22 knockdown can inhibit the proliferation, migration, and invasion of PC cells.ConclusionThis study revealed the important significance of SM in PC and identified SM-associated molecular subtypes and prognostic model, which provided novel perspectives on the stratification, prognostic prediction, and precision treatment of PC patients.

Gene expression of prostate-specific membrane antigen (FOLH1) in clear cell renal cell carcinoma predicts angiogenesis and response to tyrosine kinase inhibitors

PurposeCombination systemic therapies (CSTs) of immuno-oncologic (IO) and VEGF-inhibiting agents (VEGFi) have become the standard of care for management of metastatic clear cell renal cell carcinoma (m-ccRCC). However, treatment outcomes vary between patients, with no established biomarkers to determine optimal CST regimens (IO/IO or IO/VEGFi). Prostate Specific Membrane Antigen (PSMA), encoded by the FOLH1 gene, is a marker of tumor neovasculature in ccRCC, the downstream target of VEGFi. We evaluated the relation between FOLH1 expression and angiogenesis, as well as clinical outcomes, in 5 m-ccRCC ST trials. Materials and Methodsusing Spearman's rank correlation (SPRC) test, we assessed the correlation between FOLH1 expression and gene expression signature (GES) scores corresponding to angiogenic and immunologic features of the tumor microenvironment (TME) of m-ccRCC in our trial cohorts. Using Cox proportional hazard regression (Cox-PHR), we assessed the association between FOLH1 expression level, summarized by within-study quantiles (qFOLH1), and progression-free and overall survival (PFS, OS). ResultsIncreased FOLH1 expression was significantly associated with higher TME angiogenesis GES scores (SPRC +0.5, P < 0.001), but did not consistently correlate with immune feature GES scores. Meta-analysis of PFS in the sunitinib TKI arm of trial cohorts showed an overall positive association with qFOLH1 (HR = 0.89; 95% CI = 0.85-0.94, P < 0.0001). qFOLH1 was not significantly associated with OS in the sunitinib arms of the two trials with OS data (COMPARZ, HR 0.87, 95% CI 0.71–1.07, P = 0.17; and Checkmate-214, HR 0.89, 95% CI 0.67–1.17, P = 0.70). ConclusionsPSMA-encoding FOLH1 gene expression correlates with neoangiogenesis and predicts PFS in m-ccRCC patients treated with sunitinib TKI, suggesting that PSMA PET could be explored as a noninvasive biomarker for guiding CST choice (IO/IO or IO/VEGFi) as well as prediction of treatment response to VEGFi in m-ccRCC patients.

RAC2 as a Tumor-Suppressive Biomarker Associated with T Cell Infiltration in Breast Cancer.

Background: RAC2 is critical in regulating the homeostasis of hematopoietic stem cells. Nonetheless, its role in breast cancer (BC) remains unclear, necessitating further investigation. Methods: The expression of RAC2 in the BC and healthy tissues was acquired from The Cancer Genome Atlas. Its validity was further assessed using datasets from the gene expression omnibus database. The Tumor Immune Single-cell Hub database was used to collect and analyze the single-cell RNA sequencing datasets of BC. The diagnostic relevance of RAC2 was evaluated using receiver operating characteristic curves. Further assessment was carried out via enrichment analyses; Gene Set Analysis, immune scoring, single-cell sequencing, and immunohistochemical analysis were conducted to confirm the relationship between RAC2 expression and immune infiltration. Results: RAC2 expression was notably heightened in BC (p < 0.001). It was observed that a better prognosis was linked to heightened expression of RAC2 (p < 0.01), with the diagnostic efficacy of the marker noted to be good (area under the curve = 0.858). We found a lower percentage of protumor immune cells and a greater proportion of antitumor immune cells in the high RAC2. Our analysis revealed alterations in gene expression and an enriched network of immune pathways influenced by RAC2. Notably, cytotoxic genes, chemokines, chemokine receptors, immunostimulators, and immunosuppressive molecules positively correlated with RAC2 expression. RAC2 expression reliably predicted how patients would respond to two different therapeutic approaches in BC. Conclusions: The RAC2 was found to be a key biomarker in BC in the current study, demonstrating considerable potential as a prognostic and diagnostic marker. These results highlight the RAC2 potential to improve precision medicine strategies and treatment outcomes for patients with BC.

Discovery of PANoptosis-related signatures correlates with immune cell infiltration in psoriasis.

Psoriasis is an inflammatory skin disease that relapses frequently. Keratinocyte apoptosis dysregulation plays a crucial role in the pathological mechanisms of psoriasis. PANoptosis is a process with intermolecular interaction among pyroptosis, apoptosis, and necroptosis. The mechanism of PANoptosis in the occurrence and development of psoriasis is still unclear. Here we present a novel approach by identifying PANoptosis-related signatures (PANoptosis-sig) from skin tissue of psoriasis patients and healthy controls on transcriptional and protein levels. Five PANoptosis-sig (TYMP, S100A8, S100A9, NAMPT, LCN2) were identified. Enrichment analysis showed they were mainly enriched in response to leukocyte aggregation, leukocyte migration, chronic inflammatory response and IL-17 signaling pathway. Single cell transcriptome analysis showed TYMP and NAMPT were expressed in almost all cell populations, while LCN2, S100A8 and S100A9 were significantly highly expressed in keratinocyte. We then constructed predictive and diagnostic models with the PANoptosis-sig and evaluated their performance. Finally, unsupervised consensus clustering analysis was conducted to ascertain psoriasis molecular subtypes by the PANoptosis-sig. The psoriasis cohort was divided into two distinct subtypes. Immune landscape showed that the stromal score of cluster 1 was significantly higher than cluster 2, while the immune and estimate scores of cluster 2 were expressively higher than cluster 1. Cluster 1 exhibited high expression of Plasma cells, Tregs and Mast cells resting, while cluster 2 showed high expression of T cells, Macrophages M1, Dendritic cells activated, and Neutrophils in immune infiltration analysis. And cluster 2 was more sensitive to immune checkpoints. In conclusion, our findings revealed potential biomarkers and therapeutic targets for the prevention, diagnosis, and treatment of psoriasis, enhancing our understanding of the molecular mechanisms underlying PANoptosis.

Transcriptomics profiling of the non-small cell lung cancer microenvironment across disease stages reveals dual immune cell-type behaviors

BackgroundLung cancer is the leading cause of cancer death worldwide, with poor survival despite recent therapeutic advances. A better understanding of the complexity of the tumor microenvironment is needed to improve patients’ outcome.MethodsWe applied a computational immunology approach (involving immune cell proportion estimation by deconvolution, transcription factor activity inference, pathways and immune scores estimations) in order to characterize bulk transcriptomics of 62 primary lung adenocarcinoma (LUAD) samples from patients across disease stages. Focusing specifically on early stage samples, we validated our findings using an independent LUAD cohort with 70 bulk RNAseq and 15 scRNAseq datasets and on TCGA datasets.ResultsThrough our methodology and feature integration pipeline, we identified groups of immune cells related to disease stage as well as potential immune response or evasion and survival. More specifically, we reported a duality in the behavior of immune cells, notably natural killer (NK) cells, which was shown to be associated with survival and could be relevant for immune response or evasion. These distinct NK cell populations were further characterized using scRNAseq data, showing potential differences in their cytotoxic activity.ConclusionThe dual profile of several immune cells, most notably T-cell populations, have been discussed in the context of diseases such as cancer. Here, we report the duality of NK cells which should be taken into account in conjunction with other immune cell populations and behaviors in predicting prognosis, immune response or evasion.

The signature of SARS-CoV-2-related genes predicts the immune therapeutic response and prognosis in breast cancer

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an exceptionally contagious single-stranded RNA virus with a strong positive contagion. The COVID-19 pandemic refers to the swift worldwide dissemination of SARS-CoV-2 infection, which began in late 2019. The COVID-19 epidemic has disrupted many cancer treatments. A few reports indicate that the prevalence of SARS-CoV-2 has disrupted the treatment of breast cancer patients (BCs). However, the role of SARS-CoV-2 in the occurrence and prognosis of BC has not been elucidated. Here, we applied bioinformatics to construct a prognostic signature of SARS-CoV-2-related genes (SCRGs). Specifically, weighted gene co-expression network analysis (WGCNA) was utilized to extract co-expressed genes of differentially expressed genes (DEGs) in breast cancer and SCRGs. Then, we utilized the least absolute shrinkage and selection operator (LASSO) algorithm and univariate regression analysis to screen out three hub genes (DCTPP1, CLIP4 and ANO6) and constructed a risk score model. We further analyzed tumor immune invasion, HLA-related genes, immune checkpoint inhibitors (ICIs), and sensitivity to anticancer drugs in different SARS-CoV-2 related risk subgroups. In addition, we have developed a nomination map to expand clinical applicability. The results of our study indicate that BCs with a high-risk score are linked to negative outcomes, lower immune scores, and reduced responsiveness to anticancer medications. This suggests that the SARS-CoV-2 related signature could be used to guide prognosis assessment and treatment decisions for BCs.

TYK2 Protein Expression and Its Potential as a Tissue-Based Biomarker for the Diagnosis of Colorectal Cancer.

The aim of this study was to examine the expression of TYK2 in colorectal cancer (CRC) and to determine the potential diagnostic and prognostic significance of this kinase. Digital image analysis was performed to assess immunohistochemical TYK2 reactivity. There were significant differences for all positive pixels between CRC and normal colonic mucosa, with higher TYK2 expression levels observed in surgical margins than in adenocarcinomas (p = 0.0004). Paired t tests showed elevated immunoreactivity for overall TYK2 expression in matched pairs of CRC with adjacent surgical margins (p < 0.0001). Higher percentages of weak (p < 0.0001) and strong pixels (p = 0.0260) were detected in normal colonic mucosa than in cancer tissues. To distinguish cancer from normal intestinal mucosa, the following cutoffs for the TYK2 immune score were found: 29.5% for all cases and 31% for matched pairs. Tumor budding (Bd) was negatively correlated with the percentage of strong pixels for TYK2 (ρ = -0.270, p = 0.0096). The percentage of strong pixels was significantly elevated for the T parameter (p = 0.0428). There was a positive correlation between the number of involved lymph nodes and weak pixels (ρ = 0.239, p = 0.0242). Immunofluorescence staining showed significantly higher signal intensities in colonic mucosa than in CRC. The protein level of TYK2 was significantly higher in controls than in cancer tissues. TEM imaging showed lower levels of TYK2 in cancer than in ulcerative colitis. TYK2 protein expression may bring diagnostic value in patients diagnosed with CRC.

Identification and Experimental Verification of PDK4 as a Potential Biomarker for Diagnosis and Treatment in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder marked by sustained joint inflammation, with an etiology that remains elusive. Achieving an early and precise diagnosis poses significant challenges. This study aims to elucidate the molecular pathways involved in RA pathogenesis by screening genes associated with its occurrence, analyzing the related molecular activities, and ultimately developing more effective molecular-level treatments for RA. Microarray expression profiling datasets GSE1919, GSE10500, GSE15573, GSE77298, GSE206848, and GSE236924 were sourced from the Gene Expression Omnibus (GEO) database. Samples were divided into experimental (RA) and control (normal) groups. Differentially expressed genes (DEGs) were identified using R software packages such as limma, glmnet, e1071 as well as randomForest. Cross-validation of DEGs was conducted using lasso regression and the random forest (RF) algorithm in R software to pinpoint intersecting genes that met the criteria. Among these, one gene was selected as the target for correlation analysis to identify DEGs related to the target gene. Enrichment analysis utilized the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases and Gene Ontology (GO) data. Gene Set Enrichment Analysis (GSEA) was performed to compare the expression levels of the target gene (PDK4) across various biological pathways and functions in groups with high and low expression. The relationship between target gene expression levels and cellular immune function was assessed using the immune function score technique. The discrepancy in immune cell distribution between the control and experimental groups, as well as their correlation with target gene expression levels, was elucidated using CIBERSORT. The relationships between mRNA, lncRNA, and miRNA were depicted in the ceRNA regulation network. The expression levels of the target gene were validated using Western blot and qRT-PCR. In this study, six intersecting genes meeting the criteria were identified through cross-validation, and PDK4 was chosen as the target gene for further investigation. Functional analysis using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) revealed that PDK4-associated DEGs are primarily enriched in the PPAR signaling pathway, thereby regulating synovial cell proliferation and migration, ultimately influencing the onset and progression of rheumatoid arthritis (RA). Immune infiltration analysis suggested that eosinophil quantity may influence the progression of RA. Experimental results from PCR and Western blot confirmed the downregulation of PDK4 in the RA group. The significant downregulation of PDK4 expression in patients diagnosed with rheumatoid arthritis (RA) was confirmed. PDK4 may function as a novel regulatory factor in the onset and progression of RA, with potential applications as a diagnostic biomarker for the condition.