Immune-related Biomarkers Research Articles

RAD51AP1 as an Immune-Related Prognostic Biomarker and Therapeutic Response Predictor in Hepatocellular Carcinoma.

RAD51 associated protein 1 (RAD51AP1) is shown to regulate cell proliferation and cancer progression. However, the immune-infiltrating correlation and the therapeutics guidance of RAD51AP1 in hepatocellular carcinoma (HCC) still need further investigation. In this study, comprehensive bioinformatic analysis of RAD51AP1 on differential expression, clinicopathologic correlation, prognostic value, and function enrichment were performed in The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO; GSE14520 and GSE76427), and International Cancer Genome Consortium (ICGC) datasets. Besides, the Guangxi cohort containing 50 pairs HCC and adjacent non-cancerous samples from First Affiliated Hospital of Guangxi Medical University was served as validation cohort. Moreover, we explored the predictive value of RAD51AP1 to therapeutics response and its underlying correlation with HCC immunoinfiltration. RAD51AP1 was significantly overexpressed in HCC tissues and had a high diagnostic value of HCC. The shorter survival time and poorer clinical features were showed when RAD51AP1 upregulated, and then a nomogram featuring RAD51AP1 expression and other clinicopathologic factors was established to predict prognosis. In CIBERSORT analysis, higher T cells follicular helper but lower T cells CD4+ memory resting infiltration levels were exhibited when RAD51AP1 upregulated. The ssGSEA analysis demonstrated that high-RAD51AP1 expression subgroup had higher macrophages, Th2 and Treg cells infiltration levels, but lower type II IFN response function. Furthermore, high-RAD51AP1 expression subgroup exhibited the upregulated expression levels of immune-related checkpoint genes, but lower IPS and TIDE scores which suggested a possibly better immunotherapy response. The drug sensitivity analysis showed the high-expression subgroup may be more susceptible to Bexarotene, Doxorubicin, Gemcitabine and Tipifarnib. Taken together, RAD51AP1 is a potential diagnostic and prognostic biomarker. It may be related to the immunosuppressive microenvironment and could be an underlying HCC treatment strategy. However, the conclusions still require further validation studies.

Comprehensive analysis for the immune related biomarkers of platinum-based chemotherapy in ovarian cancer

BackgroundOvarian cancer (OC) is one of the most lethal gynecological malignancies. This study aimed to identify biomarkers that were sensitive to platinum-based chemotherapeutic agents and can be used in immunotherapy and explore the importance of their mechanisms of action. MethodsRNA-seq profiles and clinicopathological data for OC samples were obtained from The Cancer Genome Atlas (TCGA) and cBioPortal platform, respectively. Platinum-sensitive and platinum-resistant OC samples in the TCGA cohort were selected based on the clinical information. RNA-seq data for 70 OC samples withSingle-sample gene set enrichment analysis (ssGSEA) and unsupervised clustering were used to classify OC patients from the TCGA cohort into clusters with different proportions of infiltrating immune cells. ESTIMATE analysis was used to assess the immune landscape among clusters. Differential expression, univariate Cox regression, and LASSO regression analyses were performed to construct prognostic model. Spearman correlation analysis was conducted to investigate the correlations among immune checkpoint inhibitors (ICIs) and risk score, half-maximal drug inhibitory concentration (IC50) and risk score. ResultsUsing ssGSEA and unsupervised clustering, OC samples were divided into two clusters with different immune cell infiltration. Then, 1715 differentially expressed immune-related genes (DEIRGs) were identified between two clusters, 984 differentially expressed platinum-sensitive related genes (DEPSRGs) between 149 platinum-sensitive and 63 platinum-resistant OC samples were identified, and 5384 differentially expressed genes (DEGs) between 380 OC and 194 normal samples were detected from the TCGA cohort. Six biomarkers (GMPPB, SRPK1, STC1, PRSS16, HPDL, and SPTSSB) were detected to establish a prognostic model. The OC patients in the TCGA cohort were classified into high- and low-risk groups. The receive operating characteristic (ROC) curve was plotted and demonstrated that the prognostic model performed well with the area under ROC curve (AUC) greater than 0.6. The expressions of 5 ICIs, including CD200, TNFRSF18, CD160, CD200R1, and CD274 (PD-L1), were significantly different between two risk groups, and the risk score was significant negative associated with CTLA4, TNFRSF4, TNFRSF18, and CD274. Moreover, there were significant differences in IC50 of 10 chemo drugs between two risk groups, patients in the high-risk group could be more resistant to po0tinib, dasatinib, and neratinib. ConclusionIn summary, this study constructed a novel prognostic model based on six prognostic biomarkers, including GMPPB, SRPK1, STC1, PRSS16, HPDL, and SPTSSB, which can be utilized for predicting the prognosis of OC patients. These biomarkers were the potential therapeutic targets.

Identification and verification of novel immune-related ferroptosis signature with excellent prognostic predictive and clinical guidance value in hepatocellular carcinoma.

Background: Immunity and ferroptosis often play a synergistic role in the progression and treatment of hepatocellular carcinoma (HCC). However, few studies have focused on identifying immune-related ferroptosis gene biomarkers. Methods: We performed weighted gene co-expression network analysis (WGCNA) and random forest to identify prognostic differentially expressed immune-related genes (PR-DE-IRGs) highly related to HCC and characteristic prognostic differentially expressed ferroptosis-related genes (PR-DE-FRGs) respectively to run co-expression analysis for prognostic differentially expressed immune-related ferroptosis characteristic genes (PR-DE-IRFeCGs). Lasso regression finally identified 3 PR-DE-IRFeCGs for us to construct a prognostic predictive model. Differential expression and prognostic analysis based on shared data from multiple sources and experimental means were performed to further verify the 3 modeled genes' biological value in HCC. We ran various performance testing methods to test the model's performance and compare it with other similar signatures. Finally, we integrated composite factors to construct a comprehensive quantitative nomogram for accurate prognostic prediction and evaluated its performance. Results: 17 PR-DE-IRFeCGs were identified based on co-expression analysis between the screened 17 PR-DE-FRGs and 34 PR-DE-IRGs. Multi-source sequencing data, QRT-PCR, immunohistochemical staining and testing methods fully confirmed the upregulation and significant prognostic influence of the three PR-DE-IRFeCGs in HCC. The model performed well in the performance tests of multiple methods based on the 5 cohorts. Furthermore, our model outperformed other related models in various performance tests. The immunotherapy and chemotherapy guiding value of our signature and the comprehensive nomogram's excellent performance have also stood the test. Conclusion: We identified a novel PR-DE-IRFeCGs signature with excellent prognostic prediction and clinical guidance value in HCC.

Lacking Immunotherapy Biomarkers for Biliary Tract Cancer: A Comprehensive Systematic Literature Review and Meta-Analysis.

Immunotherapy has recently been incorporated into the spectrum of biliary tract cancer (BTC) treatment. The identification of predictive response biomarkers is essential in order to identify those patients who may benefit most from this novel treatment option. Here, we propose a systematic literature review and a meta-analysis of PD-1, PD-L1, and other immune-related biomarker expression levels in patients with BTC. Prisma guidelines were followed for this systematic review and meta-analysis. Eligible studies were searched on PubMed. Studies published between 2017 and 2022, reporting data on PD-1/PD-L1 expression and other immune-related biomarkers in patients with BTC, were considered eligible. A total of 61 eligible studies were identified. Despite the great heterogeneity between 39 studies reporting data on PD-L1 expression, we found a mean PD-L1 expression percentage (by choosing the lowest cut-off per study) of 25.6% (95% CI 21.0 to 30.3) in BTCs. The mean expression percentages of PD-L1 were 27.3%, 21.3%, and 27.4% in intrahepatic cholangiocarcinomas (iCCAs-15 studies), perihilar-distal CCAs (p/dCCAs-7 studies), and gallbladder cancer (GBC-5 studies), respectively. Furthermore, 4.6% (95% CI 2.38 to 6.97) and 2.5% (95% CI 1.75 to 3.34) of BTCs could be classified as TMB-H and MSI/MMRd tumors, respectively. From our analysis, PD-L1 expression was found to occur approximately in 26% of BTC patients, with minimal differences based on anatomical location. TMB-H and MSI molecular phenotypes occurred less frequently. We still lack a reliable biomarker, especially in patients with mismatch-proficient tumors, and we must need to make an effort to conceive new prospective biomarker discovery studies.

Identification of immune-related biomarkers co-occurring in acute ischemic stroke and acute myocardial infarction.

Acute ischemic stroke (AIS) and acute myocardial infarction (AMI) share several features on multiple levels. These two events may occur in conjunction or in rapid succession, and the occurrence of one event may increase the risk of the other. Owing to their similar pathophysiologies, we aimed to identify immune-related biomarkers common to AIS and AMI as potential therapeutic targets. We identified differentially expressed genes (DEGs) between the AIS and control groups, as well as AMI and control groups using microarray data (GSE16561 and GSE123342). A weighted gene co-expression network analysis (WGCNA) approach was used to identify hub genes associated with AIS and/or AMI progression. The intersection of the four gene sets identified key genes, which were subjected to functional enrichment and protein-protein interaction (PPI) network analyses. We confirmed the expression levels of hub genes using two sets of gene expression profiles (GSE58294 and GSE66360), and the ability of the genes to distinguish patients with AIS and/or AMI from control patients was assessed by calculating the receiver operating characteristic values. Finally, the investigation of transcription factor (TF)-, miRNA-, and drug-gene interactions led to the discovery of therapeutic candidates. We identified 477 and 440 DEGs between the AIS and control groups and between the AMI and control groups, respectively. Using WGCNA, 2,776 and 2,811 genes in the key modules were identified for AIS and AMI, respectively. Sixty key genes were obtained from the intersection of the four gene sets, which were used to identify the 10 hub genes with the highest connection scores through PPI network analysis. Functional enrichment analysis revealed that the key genes were primarily involved in immunity-related processes. Finally, the upregulation of five hub genes was confirmed using two other datasets, and immune infiltration analysis revealed their correlation with certain immune cells. Regulatory network analyses indicated that GATA2 and hsa-mir-27a-3p might be important regulators of these genes. Using comprehensive bioinformatics analyses, we identified five immune-related biomarkers that significantly contributed to the pathophysiological mechanisms of both AIS and AMI. These biomarkers can be used to monitor and prevent AIS after AMI, or vice versa.

OLR1 Is a Pan-Cancer Prognostic and Immunotherapeutic Predictor Associated with EMT and Cuproptosis in HNSCC.

Metabolism plays a critical role in cancer. OLR1 has been implicated in cardiovascular and metabolic disorders, while its association with tumorigenesis and tumor immunity remains poorly defined in the literature. We conducted comprehensive pan-cancer analyses based on the TCGA database to examine OLR1 expression and its prognostic implications. Correlations between OLR1 expression level and tumor immunity and immunotherapy were investigated by immune infiltration, enrichment, and TIDE analysis methods. Immunohistochemistry detected OLR1 expression in HNSCC. We used the GSEA method to explore the potential signaling pathways in which OLR1 is involved, and a correlation analysis to investigate the relationships between OLR1 and epithelial-mesenchymal transition (EMT) and cuproptosis. In addition, the effects of OLR1 knockdown on the EMT process, invasion, stemness, and cuproptosis of HNSCC cells were examined by scratch, Transwell, CCK8, sphere formation, and flow cytometry, while changes in related proteins were detected using the immunoblotting method. OLR1 is highly expressed in most cancers, and it is associated with patient prognosis. OLR1 expression positively correlates with immunosuppressive cell infiltration and immune checkpoint molecules, while being negatively associated with effector T cells. Moreover, significant correlations are observed between OLR1 expression and tumor mutation burden (TMB) and microsatellite instability (MSI) in some cancers. In HNSCC, OLR1 expression is related to advanced clinicopathological factors and unfavorable outcomes. Patients with high OLR1 expression levels are prone to experience immune escape and benefit less from immune checkpoint inhibitor (ICI) therapy. Moreover, OLR1 expression may affect EMT, stemness, and cuproptosis resistance outcomes. OLR1 is an immune-related prognostic biomarker with potential as a prognostic indicator for immunotherapy, and it may also be involved in regulating the EMT process and cuproptosis in HNSCC.

Developing neural network diagnostic models and potential drugs based on novel identified immune-related biomarkers for celiac disease

BackgroundAs one of the most common intestinal inflammatory diseases, celiac disease (CD) is typically characterized by an autoimmune disorder resulting from ingesting gluten proteins. Although the incidence and prevalence of CD have increased over time, the diagnostic methods and treatment options are still limited. Therefore, it is urgent to investigate the potential biomarkers and targeted drugs for CD.MethodsGene expression data was downloaded from GEO datasets. Differential gene expression analysis was performed to identify the dysregulated immune-related genes. Multiple machine algorithms, including randomForest, SVM-RFE, and LASSO, were used to select the hub immune-related genes (HIGs). The immune-related genes score (IG score) and artificial neural network (ANN) were constructed based on HIGs. Potential drugs targeting HIGs were identified by using the Enrichr platform and molecular docking method.ResultsWe identified the dysregulated immune-related genes at a genome-wide level and demonstrated their roles in CD-related immune pathways. The hub genes (MR1, CCL25, and TNFSF13B) were further screened by integrating several machine algorithms. Meanwhile, the CD patients were divided into distinct subtypes with either high- or low-immunoactivity using single-sample gene set enrichment analysis (ssGSEA) and consensus clustering. By constructing IG score based on HIGs, we found that patients with high IG score were mainly attributed to high-immunoactivity subgroups, which suggested a strong link between HIGs and immunoactivity of CD patients. In addition, the novel constructed ANN model showed the sound diagnostic ability of HIGs. Mechanistically, we validated that the HIGs play pivotal roles in regulating CD's immune and inflammatory state. Through targeting the HIGs, we also found potential drugs for anti-CD treatment by using the Enrichr platform and molecular docking method.ConclusionsThis study unveils the HIGs and elucidates the networks regulated by these genes in the context of CD. It underscores the pivotal significance of HIGs in accurately predicting the presence or absence of CD in patients. Consequently, this research offers promising prospects for the development of diagnostic biomarkers and therapeutic targets for CD.

Immune-related biomarkers in myocardial infarction; diagnostic/prognostic value and therapeutic potential.

The incidence of myocardial infarction (MI) is increasing worldwide on an annual basis. The incorporation of circulating biomarkers, along with electrocardiography, echocardiography, coronary angiograms, and other diagnostic techniques, is essential in the evaluation, prediction, and therapeutic efficacy assessment of patients afflicted with MI. Biomarker evaluation has been employed in the diagnosis of MI for over five decades. Further biomarker research can be carried out as newer biomarkers have been discovered in pathways such as inflammatory response, neurohormonal stimulation, or myocardial stress that initiate significantly earlier than myocyte necrosis and the diagnostic establishment of cardiac troponins. The assessment of biomarkers for MI is on the brink of a significant transformation due to advancements in comprehending the intricate pathophysiology of the condition. This has led to a pursuit of innovative biomarkers that could potentially overcome the limitations of current biomarkers. For individuals with a high-risk profile, this may facilitate tailoring of appropriate treatment. This review places emphasis on a diverse array of biomarkers that have the potential to offer diagnostic and prognostic information, as well as the latest clinical and preclinical evidence that is driving theoretical advancements in cardiovascular immunotherapy.

Association of the LEP gene with immune infiltration as a diagnostic biomarker in preeclampsia.

Objective: Preeclampsia (PE) is a serious condition in pregnant women and hence an important topic in obstetrics. The current research aimed to recognize the potential and significant immune-related diagnostic biomarkers for PE. Methods: From the Gene Expression Omnibus (GEO) data sets, three public gene expression profiles (GSE24129, GSE54618, and GSE60438) from the placental samples of PE and normotensive pregnancy were downloaded. Differentially expressed genes (DEGs) were selected and determined among 73 PE and 85 normotensive control pregnancy samples. The DEGs were used for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA). The candidate biomarkers were identified by the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analysis. The receiver operating characteristic curve (ROC) was applied to evaluate diagnostic ability. For further confirmation, the expression levels and diagnostic value of biomarkers in PE were verified in the GSE75010 data set (80 PE and 77 controls) and validated by qRT-RCR, Western blot, and immunohistochemistry (IHC). The CIBERSORT algorithm was used to calculate the compositional patterns of 22 types of immune cells in PE. Results: In total, 15 DEGs were recognized. The GO and KEGG analyses revealed that the DEGs were enriched in the steroid metabolic process, receptor ligand activity, GnRH secretion, and neuroactive ligand-receptor interaction. The recognized DEGs were primarily implicated in cell-type benign neoplasm, kidney failure, infertility, and PE. Gene sets related to hormone activity, glycosylation, multicellular organism process, and response to BMP were activated in PE. The LEP gene was distinguished as a diagnostic biomarker of PE (AUC = 0.712) and further certified in the GSE75010 data set (AUC = 0.850). The high expression of LEP was associated with PE in clinical samples. In addition, the analysis of the immune microenvironment showed that gamma delta T cells, memory B cells, M0 macrophages, and regulatory T cells were positively correlated with LEP expression (P < 0.05). Conclusion: LEP expression can be considered to be a diagnostic biomarker of PE and can offer a novel perspective for future studies regarding the occurrence and molecular mechanisms of PE.

Impact of biorefinery processing conditions on the bioactive properties of fucoidan extracts from Saccharina latissima on SHK-1 cells

Fucoidans from brown macroalgae exhibit a wide array of bioactivities, from antioxidant to immunostimulatory effects. Their specific effects and efficacy depend on the molecular weight and structure of the polysaccharide chains, which in turn depends on the seaweed species and the methods used for fucoidan extraction and purification. In this study, a bench scale, three factor, three level full factorial experiment was used to determine the relationship between the extraction parameters (duration, hydrochloric acid concentration in extraction liquor, and temperature), the sulfation level and fucoidan yield. Three of the extractions representing the least severe and most severe extractions as well as a calculated sweet spot were scaled up to a pilot biorefinery (5 kg biomass per extraction). To evaluate the potential of using these complex polysaccharides as functional ingredients in salmon aquaculture, bioactivity of the three different fucoidan preparations was examined by in vitro assays with cultures of Atlantic salmon head kidney cells (SHK-1). All pilot extraction products induced an increase in SHK1 cell viability. In addition, “Too careful” also induced an upregulation of il1-β and sod1, both immune-related biomarkers. Interestingly, the expression of these biomarkers showed a positive correlation in SHK-1 cells stimulated with products from both “Too careful” and “Sweet spot” extractions. The data presented here show the extent of shift in bioactivity resulting from changes in the extraction severity in fucoidan biorefining and present the potential for fucoidan application as an immunostimulant for salmon cells.

Identification of Potential Biomarkers of Septic Shock Based on Pathway and Transcriptome Analyses of Immune-Related Genes.

Immunoregulation is crucial to septic shock (SS) but has not been clearly explained. Our aim was to explore potential biomarkers for SS by pathway and transcriptional analyses of immune-related genes to improve early detection. GSE57065 and GSE95233 microarray data were used to screen differentially expressed genes (DEGs) in SS. Gene Ontology and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses of DEGs were performed, and correlations between immune cell and pathway enrichment scores were analyzed. The predictive value of candidate genes was evaluated by receiver operating characteristic (ROC) curves. GSE66099, GSE4607, and GSE13904 datasets were used for external validation. Blood samples from six patients and six controls were collected for validation by qRT-PCR and western blotting. In total, 550 DEGs in SS were identified; these genes were involved in the immune response, inflammation, and infection. Immune-related pathways and levels of infiltration of CD4 + TCM, CD8 + T cells, and preadipocytes differed between SS cases and controls. Seventeen genes were identified as potential biomarkers of SS (areas under ROC curves >0.9). The downregulation of CD8A, CD247, CD3G, LCK, and HLA-DRA in SS was experimentally confirmed. We identified several immune-related biomarkers in SS that may improve early identification of disease risk.

Identification of novel immune-related biomarker and therapeutic drugs in Parkinson disease via integrated bioinformatics analysis.

The present study was designed to identify immune-related biomarker and candidate drugs for Parkinson disease (PD) by weighted gene co-expression network analysis. Differentially expressed genes were identified in PD and healthy samples in the Gene Expression Omnibus (GEO) database. Besides, immune-related genes were obtained from the immunology database. Then, a co-expression network was constructed by the weighted gene co-expression network analysis package. Diagnostic model for PD was constructed by Lasso and multivariate Cox regression. Furthermore, differentially expressed genes (DEGs) were used to establish PPI and competing endogenous RNA (ceRNA) networks. Functional enrichment and pathway analysis were performed. Drug-hub gene interaction analysis was performed via DGIdb database. PD samples and normal samples were found to have 220 upregulated genes and 216 downregulated genes in the GSE6613 dataset. The differentially expressed genes contained 50 immune-related genes, with 40 upregulated genes and 10 downregulated genes. We obtained 7 hub genes by intersecting the DEGs and candidate hub genes. As potential diagnostic markers, 2 immune-related DEGs were identified among the 7 hub genes. According to functional enrichment analysis, these DEGs were mainly enriched in immune response, inflammatory response, and cytokine-cytokine receptor interactions. Totally, we obtained 182 drug-gene interaction pairs in Drug-Gene Interaction database (DGIdb). Our results revealed crucial genes and candidate drugs for PD patients and deepen our understanding of the molecular mechanisms involved in PD.

Immune Biomarkers in Triple-Negative Breast Cancer: Improving the Predictivity of Current Testing Methods.

Triple-negative breast cancer (TNBC) poses a significant challenge in terms of prognosis and disease recurrence. The limited treatment options and the development of resistance to chemotherapy make it particularly difficult to manage these patients. However, recent research has been shifting its focus towards biomarker-based approaches for TNBC, with a particular emphasis on the tumor immune landscape. Immune biomarkers in TNBC are now a subject of great interest due to the presence of tumor-infiltrating lymphocytes (TILs) in these tumors. This characteristic often coincides with the presence of PD-L1 expression on both neoplastic cells and immune cells within the tumor microenvironment. Furthermore, a subset of TNBC harbor mismatch repair deficient (dMMR) TNBC, which is frequently accompanied by microsatellite instability (MSI). All of these immune biomarkers hold actionable potential for guiding patient selection in immunotherapy. To fully capitalize on these opportunities, the identification of additional or complementary biomarkers and the implementation of highly customized testing strategies are of paramount importance in TNBC. In this regard, this article aims to provide an overview of the current state of the art in immune-related biomarkers for TNBC. Specifically, it focuses on the various testing methodologies available and sheds light on the immediate future perspectives for patient selection. By delving into the advancements made in understanding the immune landscape of TNBC, this study aims to contribute to the growing body of knowledge in the field. The ultimate goal is to pave the way for the development of more personalized testing strategies, ultimately improving outcomes for TNBC patients.

TSC22D3 as an immune-related prognostic biomarker for acute myeloid leukemia

Acute myeloid leukemia (AML) is the type of hematologic neoplasm most common in adults. Glucocorticoid-induced gene TSC22D3 regulates cell proliferation through its function as a transcription factor. However, there is no consensus on the prognostic and immunoregulatory significance of TSC22D3 in AML. In the present study, we evaluated the correlation between TSC22D3 expression, immunoinfiltration, and prognostic significance in AML. Knockdown of TSC22D3 significantly attenuated the proliferation of Hel cells and increased sensitivity to cytarabine (Ara-c) drugs. Furthermore, TSC22D3 reduced the release of interleukin-1β (IL-1β) by inhibiting the NF-κB/NLRP3 signaling pathway, thereby inhibiting macrophage polarization to M1 subtype, and attenuating the pro-inflammatory tumor microenvironment. In conclusion, this study identified TSC22D3 as an immune-related prognostic biomarker for AML patients and suggested that therapeutic targeting of TSC22D3 may be a potential treatment option for AML through tumor immune escape.

A Blood-Based Immune Gene Signature with Prognostic Significance in Localized Prostate Cancer.

Prostate cancer (PCa) is one of the most common male cancers worldwide and one of the deadliest if unsuccessfully treated. Τhe need for reliable, easily accessible immune-related molecular biomarkers that could be combined with clinically defined criteria, including PSA and Gleason score, to accurately predict PCa patients' clinical outcomes is emerging. Herein, we describe for the first time a blood-identified immune-related gene signature comprising eight upregulated multi-functional genes associated with poor prognosis. Next-generation sequencing (NGS) analysis of PCa patients' peripheral blood samples revealed a more than three-fold upregulation of each of the eight genes as compared to samples originating from healthy donors. The construction of gene and protein interaction networks revealed different extents of the functional implications of these genes in the regulation of cell proliferation and immune responses. Analysis of the available data from The Cancer Genome Atlas (TCGA) regarding gene expression and survival of prostate adenocarcinoma (PRAD) and pan-cancer (PANCAN) patients revealed that intra-tumoral upregulation of this eight-gene signature (8-GS) was associated with poor 5-year progression-free intervals in PCa patients, even in those with high Gleason scores, and also with an unfavorable prognosis for cancer patients irrespective of the cancer type and even in the early stages. These observations suggest that further investigation of the 8-GS prospectively in randomized clinical trials, in which clinical benefit in terms of evaluating time to disease progression can be assessed, is warranted.

Artificial intelligence and bioinformatics analyze markers of children's transcriptional genome to predict autism spectrum disorder.

Autism spectrum disorder (ASD), characterized by difficulties in social interaction and communication as well as restricted interests and repetitive behaviors, is extremely challenging to diagnose in toddlers. Early diagnosis and intervention are crucial however. In this study, we developed a machine learning classification model based on mRNA expression data from the peripheral blood of 128 toddlers with ASD and 126 controls. Differentially expressed genes (DEGs) between ASD and controls were identified. We identified genes such as UBE4B, SPATA2 and RBM3 as DEGs, mainly involved in immune-related pathways. 21 genes were screened as key biomarkers using LASSO regression, yielding an accuracy of 86%. A neural network model based on these 21 genes achieved an AUC of 0.88. Our findings suggest that the identified neurotransmitters and 21 immune-related biomarkers may facilitate the early diagnosis of ASD. The mRNA expression profile sheds light on the biological underpinnings of ASD in toddlers and potential biomarkers for early identification. Nevertheless, larger samples are needed to validate these biomarkers.

Oral difelikefalin reduces moderate to severe pruritus and expression of pruritic and inflammatory biomarkers in subjects with atopic dermatitis

Pruritus is the most common and burdensome symptom of atopic dermatitis (AD). Pruritus-targeted treatments in AD are lacking, particularly for patients with milder skin disease. We sought to evaluate the impact of the selective κ-opioid receptor agonist difelikefalin (DFK) on pruritus intensity and pruritus- and immune-related biomarkers in subjects with moderate to severe AD-related pruritus. A phase 2 clinical trial investigated the efficacy and safety of oral DFK 0.25, 0.5, and 1.0 mg in subjects with moderate to severe AD-related pruritus. Abiomarker substudy evaluated the effects of DFK on the expression of pruritus, TH2-associated genes, and skin barrier-related genes. In the clinical trial (N= 401), all DFK doses reduced itch versus placebo; however, the results were not statistically significant at week 12. In a subgroup of subjects in the trial with mild to moderate skin inflammation and moderate to severe itch (itch-dominant AD phenotype), DFK reduced itch at week 12 versus placebo. In the biomarker substudy, DFK downregulated the expression of key pruritus-related genes (eg, IL-31 and TRPV1) and the AD phenotype (eg, CCL17). Gene set variation analysis confirmed that DFK, but not placebo, downregulated pruritus-related genes and TH2 pathways. DFK improved skin barrier integrity markers and upregulated the expression of claudins and lipid metabolism-associated genes (eg, SEC14L6, ELOVL3, CYP1A2, and AKR1D1). DFK treatment reduced itch in subjects with moderate to severe AD-related pruritus, particularly those with an "itch-dominant" AD phenotype, and had an impact on the expression of pruritus, TH2-associated genes, and skin barrier-related genes. DFK is a promising therapy for AD-related pruritus; further clinical studies are warranted.

An immune-related gene prognostic index for predicting prognosis in patients with colorectal cancer.

Colorectal cancer (CRC) is one of the most common solid malignant burdens worldwide. Cancer immunology and immunotherapy have become fundamental areas in CRC research and treatment. Currently, the method of generating Immune-Related Gene Prognostic Indices (IRGPIs) has been found to predict patient prognosis as an immune-related prognostic biomarker in a variety of tumors. However, their role in patients with CRC remains mostly unknown. Therefore, we aimed to establish an IRGPI for prognosis evaluation in CRC. RNA-sequencing data and clinical information of CRC patients were retrieved from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) databases as training and validation sets, respectively. Immune-related gene data was obtained from the ImmPort and InnateDB databases. The weighted gene co-expression network analysis (WGCNA) was used to identify hub immune-related genes. An IRGPI was then constructed using Cox regression methods. Based on the median risk score of IRGPI, patients could be divided into high-risk and low-risk groups. To further investigate the immunologic differences, Gene set variation analysis (GSVA) studies were conducted. In addition, immune cell infiltration and related functional analysis were used to identify the differential immune cell subsets and related functional pathways. We identified 49 immune-related genes associated with the prognosis of CRC, 17 of which were selected for an IRGPI. The IRGPI model significantly differentiates the survival rates of CRC patients in the different groups. The IRGPI as an independent prognostic factor significantly correlates with clinico-pathological factors such as age and tumor stage. Furthermore, we developed a nomogram to improve the clinical utility of the IRGPI score. Immuno-correlation analysis in different IRGPI groups revealed distinct immune cell infiltration (CD4+ T cells resting memory) and associated pathways (macrophages, Type I IFNs responses, iDCs.), providing new insights into the tumor microenvironment. At last, drug sensitivity analysis revealed that the high-risk IRGPI group was sensitive to 11 and resistant to 15 drugs. Our study established a promising immune-related risk model for predicting survival in CRC patients. This could help to better understand the correlation between immunity and the prognosis of CRC providing a new perspective for personalized treatment of CRC.

Diagnostic biomarker KIF23 is associated with immune infiltration and immunotherapy response in gastric cancer.

Kinesin family member 23 (KIF23), an index of tumor proliferation, can serve as a prognostic marker in numerous tumors. However, the relationship between KIF23 expression and diagnostic value, immune infiltration, and immunotherapy response remains unclear in gastric cancer(GC). We primarily demonstrated that GC tissue had higher levels of KIF23 expression than the adjacent normal tissue on mRNA and protein levels. The ROC analysis revealed KIF23 had an outstanding diagnostic value of GC in the training and validation set (AUC = 0.958, and AUC = 0.86793, respectively). We discovered that KIF23 was positively associated with age, histological type, and H. pylori infection of GC. Subsequently, the KIF23 expression level was correlated with the gene mutation, function enrichment, immune cell infiltration, and immune cell marker of GC based on multiple online websites and R software. KIF23 expression was related to the infiltration of CD8+ T cells, CD4+T cells, macrophages, and dendritic cells in GC. Especially, KIF23 expression was positively significantly associated with the Th1 cell marker STAT1 (Signal transducer and activator of transcription 1). Patients with high KIF23 expression exhibited greater immune cell infiltrates, including T cell CD4+ memory helper, Treg, and M1 cells, which indicated that high KIF23 expression is more conducive to immunosuppression. Finally, KIF23 expression had a positive relationship with TMB and MSI, and affected the immune microenvironment in GC tissues by increased expression of ICPs such as CD274(PD-L1), CTLA4, HAVCR2, and LAG3. Our study uncovered that KIF23 can serve as an immune-related biomarker for diagnosis and immunotherapy response of GC.

High endothelial venule is a prognostic immune-related biomarker in patients with resected intrahepatic cholangiocarcinoma.

Having been reported to be a crucial prognostic factor in solid tumours, the role of high endothelial venule (HEV) in intrahepatic cholangiocarcinoma (ICC) remains unclear, however. The data of ICC and healthy individuals were downloaded from the Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) databases. Meanwhile, a cutting-edge ICC high-resolution spatial transcriptome was also acquired before these data were comprehensively analysed using bioinformatics approaches. Moreover, 95 individuals with ICC who had undergone resection surgery were enrolled in this study to investigate the relationship between HEV and tumour microenvironment (TME) applying immunohistochemistry and multiple immunofluorescence techniques. The high-HEV subtype contains rich immune infiltrates including tertiary lymphoid structure (TLS), CD8+ T cells, and CD20+ B cells. Furthermore, HEV and TLS exhibited a strong relationship of spatial colocalization. Correlated with improved prognostic outcomes in ICC, the high-HEV subtype could be an independent prognostic indicator for individuals with ICC. This study revealed the association of HEV with immune function and observed a strong spatial colocalization correlation between HEV and TLS. Moreover, correlated with immunotherapeutic response, HEV could improve prognostic outcomes, which may be a potential indicator of immunotherapy pathology in ICC.