[Construction and Evaluation of a Prognostic Risk Prediction Model of Pancreatic Ductal Adenocarcinoma Based on Immune-Related Genes].

Objective To construct a risk prediction model by integrating the molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) and immune-related genes.Methods With GSE71729 data set (n=145) as the training set,the differentially expressed genes and differential immune-related genes between the squamous and non-squamous subtypes of PDAC were integrated to construct a regulatory network,on the basis of which five immune marker genes regulating the squamous subtype were screened out.An integrated immune score (IIS) model was constructed based on patient survival information and immune marker genes to predict the clinical prognosis of PDAC patients,and its predictive performance was tested with 5 validation sets (n=758).Results PDAC patients were assigned into high risk and low risk groups according to the IIS.In both training and validation sets,the overall survival of patients in the high risk group was shorter than that in the low risk group (both P<0.001).The multivariable Cox regression showed that IIS was an independent prognostic factor for PDAC (HR=2.16,95%CI=1.50-3.10,P<0.001).Conclusion IIS can be used for risk stratification of PDAC patients and may become a potential prognostic marker for PDAC.

Clinically relevant immune subtypes based on alternative splicing landscape of immune-related genes for lung cancer advanced PPPM approach.

Background: Alternative splicing (AS) occurs in the process of gene post-transcriptional process, which is very important for the correct synthesis and function of protein. The change of AS pattern may lead to the change of expression level or function of lung cancer-related genes, and then affect the occurrence and development of lung cancers. The specific AS pattern might be used as a biomarker for early warning and prognostic assessment of a cancer in the framework of predictive, preventive, and personalized medicine (PPPM; 3PM). AS events of immune-related genes (IRGs) were closely associated with tumor progression and immunotherapy. We hypothesize that IRG-AS events are significantly different in lung adenocarcinomas (LUADs) vs. controls or in lung squamous cell carcinomas (LUSCs) vs. controls. IRG-AS alteration profiling was identified to construct IRG-differentially expressed AS (IRG-DEAS) signature models. Study on the selective AS events of specific IRGs in lung cancer patients might be of great significance for further exploring the pathogenesis of lung cancer, realizing early detection and effective monitoring of lung cancer, finding new therapeutic targets, overcoming drug resistance, and developing more effective therapeutic strategies, and better used for the prediction, diagnosis, prevention, and personalized medicine of lung cancer. Methods: The transcriptomic, clinical, and AS data of LUADs and LUSCs were downloaded from TCGA and its SpliceSeq databases. IRG-DEAS events were identified in LUAD and LUSC, followed by their functional characteristics, and overall survival (OS) analyses. OS-related IRG-DEAS prognostic models were constructed for LUAD and LUSC with Lasso regression, which were used to classify LUADs and LUSCs into low- and high-risk score groups. Furthermore, the immune cell distribution, immune-related scores, drug sensitivity, mutation status, and GSEA/GSVA status were analyzed between low- and high-risk score groups. Also, low- and high-immunity clusters and AS factor (SF)-OS-related-AS co-expression network and verification of cell function of CELF6 were analyzed in LUAD and LUSC. Results: Comprehensive analysis of transcriptomic, clinical, and AS data of LUADs and LUSCs identified IRG-AS events in LUAD (n = 1607) and LUSC (n = 1656), including OS-related IRG-AS events in LUAD (n = 127) and LUSC (n = 105). A total of 66 IRG-DEAS events in LUAD and 89 IRG-DEAS events in LUSC were identified compared to controls. The overlapping analysis between IRG-DEASs and OS-related IRG-AS events revealed 14 OS-related IRG-DEAS events for LUAD and 16 OS-related IRG-DEAS events for LUSC, which were used to identify and optimize a 12-OS-related-IRG-DEAS signature prognostic model for LUAD and an 11-OS-related-IRG-DEAS signature prognostic model for LUSC. These two prognostic models effectively divided LUAD or LUSC samples into low- and high-risk score groups that were closely associated with OS, clinical characteristics, and tumor immune microenvironment, with significant gene sets and pathways enriched in the two groups. Moreover, weighted gene co-expression network (WGCNA) and nonnegative matrix factorization method (NMF) analyses identified four OS-relevant subtypes of LUAD and six OS-relevant subtypes of LUSC, and ssGSEA identified five immunity-relevant subtypes of LUAD and five immunity-relevant subtypes of LUSC. Interestingly, splicing factors-OS-related-AS network revealed hub molecule CELF6 was significantly related to the malignant phenotype in lung cancer cells. Conclusions: This study established two reliable IRG-DEAS signature prognostic models and constructed interesting splicing factor-splicing event networks in LUAD and LUSC, which can be used to construct clinically relevant immune subtypes, patient stratification, prognostic prediction, and personalized medical services in the PPPM practice. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00366-4.

Adjuvant Effects of a CC Chemokine for Enhancing the Efficacy of an Inactivated Streptococcus agalactiae Vaccine in Nile Tilapia (Oreochromis niloticus).

In this study, the ability of a CC chemokine (On-CC1) adjuvant to enhance the efficacy of a formalin-killed Streptococcus agalactiae vaccine (WC) in inducing immune responses against S. agalactiae in Nile tilapia was investigated through immune-related gene expression analysis, enzyme-linked immunosorbent assay (ELISA), transcriptome sequencing, and challenge tests. Significantly higher S. agalactiae-specific IgM levels were detected in fish in the WC+CC group than in the WC alone or control groups at 8 days postvaccination (dpv). The WC vaccine group exhibited increased specific IgM levels at 15 dpv, comparable to those of the WC+CC group, with sustained higher levels observed in the latter group at 29 dpv and after challenge with S. agalactiae for 14 days. Immune-related gene expression analysis revealed upregulation of all target genes in the control group compared to those in the vaccinated groups, with notable differences between the WC and WC+CC groups at various time intervals. Additionally, transcriptome analysis revealed differential gene expression profiles between the vaccinated (24 and 96 hpv) and control groups, with notable upregulation of immune-related genes in the vaccinated fish. Differential gene expression (DGE) analysis revealed significant upregulation of immunoglobulin and other immune-related genes in the control group compared to those in the vaccinated groups (24 and 96 hpv), with distinct patterns observed between the WC and WC+CC vaccine groups. Finally, challenge with a virulent strain of S. agalactiae resulted in significantly higher survival rates for fish in the WC and WC+CC groups compared to fish in the control group, with a notable increase in survival observed in fish in the WC+CC group.

Construction of an immune-related prognostic model and potential drugs screening for esophageal cancer based on bioinformatics analyses and network pharmacology.

BACKGROUND: Esophageal cancer (ESCA) is a highly invasive malignant tumor with poor prognosis. This study aimed to discover a generalized and high-sensitivity immune prognostic signature that could stratify ESCA patients and predict their overall survival, and to discover potential therapeutic drugs by the connectivity map. METHODS: The key gene modules significantly related to clinical traits (survival time and state) of ESCA patients were selected by weighted gene coexpression network analysis (WCGNA), then the univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to construct a 15-immune-related gene prognostic signature. RESULTS: The immune-related risk model was related to clinical and pathologic factors and remained an effective independent prognostic factor. Enrichment analyses revealed that the differentially expressed genes (DEGs) of the high- and low-risk groups were associated with tumor cell proliferation and immune mechanisms. Based on the gathered data, a small molecule drug named perphenazine (PPZ) was elected. The pharmacological analysis indicates that PPZ could help in adjuvant therapy of ESCA through regulation of metabolic process and cellular proliferation, enhancement of immunologic functions, and inhibition of inflammatory reactions. Furthermore, molecular docking was performed to explore and verify the PPZ-core target interactions. CONCLUSION: We succeed in structuring the immune-related prognostic model, which could be used to distinguish and predict patients' survival outcome, and screening a small molecule drug named PPZ. Prospective studies also are needed to further validate its analytical accuracy for estimating prognoses and confirm the potential use of PPZ for treating ESCA.

Impact of Sel-Plex® dietary supplementation on growth performance, physiological response, oxidative status, and immunity-linked gene expression in Nile tilapia (Oreochromis niloticus) fingerlings challenged with Aeromonas hydrophila.

Background: Organic selenium (Sel-Plex®) supplementation holds considerable promise for improving the effectiveness of fish production. Aim: This experiment was accomplished to judge the potential benefits of Sel-Plex® nutritional additive on growth outcomes, physiological response, oxidative status, and immunity-linked gene expression in Nile tilapia (Oreochromis niloticus) fingerlings exposed to bacterial infection with Aeromonas hydrophila. Methods: Utilizing a basal diet of 30% protein, four experimental diets were prepared, each of which contained Sel-Plex® at concentrations of 0.0, 0.5, 1, and 2 mg/kg, respectively. Three replicates of 20 fish/treatment were used using 240 healthy Nile tilapia fingerlings. Fish were placed in 12 glass aquariums and separated into 4 groups at random. For the entire span of 8 weeks, diets were admitted to fish at a 3% rate of fish biomass/aquarium. After the feeding trial, pathogenic A. hydrophila was intraperitoneally injected into fish of each treatment, and fish were observed for 15 days to track the survival rate (SR) after the challenge. Results: Growth performance, physiological response, immunological parameters (phagocytic activity, phagocytic index, and lysozyme), and antioxidant parameters [catalase, superoxide dismutase (SOD), malondialdehyde, and glutathione peroxidase (GPx)] were noticeably improved in Sel-Plex® treated groups. Moreover, Sel-Plex® increased gene expression linked with the immune system in the liver (tumor necrosis factor-alpha and interleukin 1ß), to growth (insulin-like growth factor 1 and growth hormone receptor), and antioxidants (SOD and GPx). Under pathogen-challenge conditions, the employed dietary Sel-Plex® supplementation could successfully lower fish oxidative stress, offering a potential preventive additive for Nile tilapia instead of antibiotics. On the other hand, Sel-Plex® significantly enhanced each of three intestinal morphological measurements (villus width, villus length, and crypt depth), demonstrating the greatest influence on the improvement of intestinal structure overall. In the Nile tilapia control group, the infection with A. hydrophila caused noticeable degenerative alterations in the gut, hepatopancreas, spleen, and posterior kidney. The severity of the lesion was significantly reduced and significantly improved with higher Sel-Plex® concentrations. Sel-Plex® supplemented groups had 100% SRs among the A. hydrophila-challenged groups. Conclusion: It could be advised to enrich the diets of Nile tilapia fingerlings with 1-2 mg.kg-1 of Sel-Plex® to enhance growth rate, physiological response, immunological reaction, and intestinal absorptive capacity.

Construction of immune-related gene pairs signature to predict the overall survival of multiple myeloma patients based on whole bone marrow gene expression profiling.

Multiple myeloma (MM) is a plasma cell dyscrasia that is characterized by the uncontrolled proliferation of malignant PCs in the bone marrow. Due to immunotherapy, attention has returned to the immune system in MM, and it appears necessary to identify biomarkers in this area. In this study, we created a prognostic model for MM using immune-related gene pairs (IRGPs), with the advantage that it is not affected by technical bias. After retrieving microarray data of MM patients, bioinformatics analyses like COX regression and least absolute shrinkage and selection operator (LASSO) were used to construct the signature. Then its prognostic value is assessed via time-dependent receiver operating characteristic (ROC) and the Kaplan-Meier (KM) analysis. We also used XCELL to examine the status of immune cell infiltration among MM patients. 6-IRGP signatures were developed and proved to predict MM prognosis with a P-value of 0.001 in the KM analysis. Moreover, the risk score was significantly associated with clinicopathological characteristics and was an independent prognostic factor. Of note, the combination of age and ß2-microglobulin with risk score could improve the accuracy of determining patients' prognosis with the values of the area under the curve (AUC) of 0.73 in 5 years ROC curves. Our model was also associated with the distribution of immune cells. This novel signature, either alone or in combination with age and ß2-microglobulin, showed a good prognostic predictive value and might be used to guide the management of MM patients in clinical practice.

Identification of an immune-related signature as a prognostic classifier for patients with early-stage head and neck squamous cell carcinoma.

Background: Head and neck squamous cell carcinoma (HNSCC) is the most common type and accounts for 90% of all head and neck cancer cases. Despite advances in early diagnosis and treatment strategies-chemotherapy, surgical resection, and radiotherapy-5-year survival remains grim. For patients with early-stage HNSCC, accurately predicting clinical outcomes is challenging. Considering the pivotal role of the immune system in HNSCC, we developed a reliable immune-related gene signature (IRGS) and explored its predictive accuracy in patients with early-stage HNSCC. Methods: We examined immune gene expression profiles and clinical information from 230 early-stage HNSCC specimens, including 100 cases from The Cancer Genome Atlas (TCGA), 49 cases from the Gene Expression Omnibus (GEO; GSE65858), and 81 cases from an independent clinical cohort. The prognostic signature was constructed using Kaplan-Meier analysis and the least absolute shrinkage and selection operator (LASSO) Cox algorithm. We also explored the IRGS-related biological pathways and immune landscape using bioinformatics analysis. Results: A nine-immune-gene signature was generated to significantly stratify patients into high and low-risk groups. High risk patients exhibited shorter survival time [hazard ratio (HR) =13.795, 95% confidence interval (CI): 3.275-58.109, P<0.001]. The signature demonstrated robust prognostic ability in the training and validation sets and could independently predict overall survival (OS) and relapse-free survival (RFS). Subsequently, the receiver operating characteristic (ROC) curve and C-index confirmed the signature's predictive accuracy compared to clinical parameters. Additionally, cases classified as low risk showed more immune cell infiltration than high-risk cases. Conclusions: Our novel IRGS is a reliable and robust classifier for accurate patient stratification and prognostic evaluation. Future studies will attempt to affirm the signature's clinical application to early-stage HNSCC.

Based on the prognosis model of immunogenes, the prognosis model was constructed to predict the invasion of immune genes and immune cells related to primary liver cancer and its experimental validation.

Background: Primary liver cancer (PLC) is a prevalent malignancy of the digestive system characterized by insidious symptom onset and a generally poor prognosis. Recent studies have highlighted a significant correlation between the initiation and prognosis of liver cancer and the immune function of PLC patients. Purpose: Revealing the expression of PLC-related immune genes and the characteristics of immune cell infiltration provides assistance for the analysis of clinical pathological parameters and prognosis of PLC patients. Methods: PLC-related differentially expressed genes (DEGs) with a median absolute deviation (MAD > 0.5) were identified from TCGA and GEO databases. These DEGs were intersected with immune-related genes (IRGs) from the ImmPort database to obtain PLC-related IRGs. The method of constructing a prognostic model through immune-related gene pairs (IRGPs) is used to obtain IRGPs and conduct the selection of central immune genes. The central immune genes obtained from the selection of IRGPs are validated in PLC. Subsequently, the relative proportions of 22 types of immune cells in different immune risk groups are evaluated, and the differential characteristics of PLC-related immune cells are verified through animal experiments. Results: Through database screening and the construction of an IRGP prognosis model, 84 pairs of IRGPs (P < 0.001) were ultimately obtained. Analysis of these 84 IRGPs revealed 11 central immune genes related to PLC, showing differential expression in liver cancer tissues compared to normal liver tissues. Results from the CiberSort platform indicate differential expression of immune cells such as naive B cells, macrophages, and neutrophils in different immune risk groups. Animal experiments demonstrated altered immune cell proportions in H22 tumor-bearing mice, validating findings from peripheral blood and spleen homogenate analyses. Conclusion: Our study successfully predicted and validated PLC-related IRGs and immune cells, suggesting their potential as prognostic indicators and therapeutic targets for PLC.

Construction of an immune-related gene signature for overall survival prediction and immune infiltration in gastric cancer.

BACKGROUND: Treatment options for patients with gastric cancer (GC) continue to improve, but the overall prognosis is poor. The use of PD-1 inhibitors has also brought benefits to patients with advanced GC and has gradually become the new standard treatment option at present, and there is an urgent need to identify valuable biomarkers to classify patients with different characteristics into subgroups. AIM: To determined the effects of differentially expressed immune-related genes (DEIRGs) on the development, prognosis, tumor microenvironment (TME), and treatment response among GC patients with the expectation of providing new biomarkers for personalized treatment of GC populations. METHODS: Gene expression data and clinical pathologic information were downloaded from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were searched from ImmPort. DEIRGs were extracted from the intersection of the differentially-expressed genes (DEGs) and IRGs lists. The enrichment pathways of key genes were obtained by analyzing the Kyoto Encyclopedia of Genes and Genomes (KEGGs) and Gene Ontology (GO) databases. To identify genes associated with prognosis, a tumor risk score model based on DEIRGs was constructed using Least Absolute Shrinkage and Selection Operator and multivariate Cox regression. The tumor risk score was divided into high- and low-risk groups. The entire cohort was randomly divided into a 2:1 training cohort and a test cohort for internal validation to assess the feasibility of the risk model. The infiltration of immune cells was obtained using 'CIBERSORT,' and the infiltration of immune subgroups in high- and low-risk groups was analyzed. The GC immune score data were obtained and the difference in immune scores between the two groups was analyzed. RESULTS: We collected 412 GC and 36 adjacent tissue samples, and identified 3627 DEGs and 1311 IRGs. A total of 482 DEIRGs were obtained. GO analysis showed that DEIRGs were mainly distributed in immunoglobulin complexes, receptor ligand activity, and signaling receptor activators. KEGG pathway analysis showed that the top three DEIRGs enrichment types were cytokine-cytokine receptors, neuroactive ligand receptor interactions, and viral protein interactions. We ultimately obtained an immune-related signature based on 10 genes, including 9 risk genes (LCN1, LEAP2, TMSB15A mRNA, DEFB126, PI15, IGHD3-16, IGLV3-22, CGB5, and GLP2R) and 1 protective gene (LGR6). Kaplan-Meier survival analysis, receiver operating characteristic curve analysis, and risk curves confirmed that the risk model had good predictive ability. Multivariate COX analysis showed that age, stage, and risk score were independent prognostic factors for patients with GC. Meanwhile, patients in the low-risk group had higher tumor mutation burden and immunophenotype, which can be used to predict the immune checkpoint inhibitor response. Both cytotoxic T lymphocyte antigen4+ and programmed death 1+ patients with lower risk scores were more sensitive to immunotherapy. CONCLUSION: In this study a new prognostic model consisting of 10 DEIRGs was constructed based on the TME. By providing risk factor analysis and prognostic information, our risk model can provide new directions for immunotherapy in GC patients.

Identification of immune-related gene signature for non-small cell lung cancer patients with immune checkpoint inhibitors.

Background: The utilization of immune checkpoint inhibitors (ICIs) has become the established protocol for treating advanced non-small cell lung cancer (NSCLC). This work aimed to identify the immune-related gene signature that can predict the prognosis of NSCLC patients receiving ICI treatment. Methods: The ImmPort database was queried to obtain a list of immune-related genes (IRGs). Differentially expressed IRGs in NSCLC patients were identified using the TCGA database. RNA-seq data and clinical information from NSCLC patients receiving immunotherapy were obtained from the GEO database (GSE93157 and ////). A gene signature was generated through multivariate Cox and LASSO regression analyses. The prognostic value and function of this gene signature were thoroughly investigated using comprehensive bioinformatics analyses. Results: A total of 6 prognostic-related genes were identified from 617 differentially expressed genes, and two prognostic-related differentially expressed genes (CAMP and IL17A) were determined to construct gene signature. Our gene signature demonstrated superior performance compared to other clinicopathological parameters in predicting the prognosis of NSCLC patients receiving immunotherapy, with an area under the ROC curve (AUC) of 0.812. Furthermore, immune infiltration analysis indicated that the high-risk group was enriched with resting CD4 T cell memory, while the low-risk group showed a "hot" tumor microenvironment that promotes anti-tumor immunity in NSCLC patients. Conclusion: Gene signatures based on immune-related genes exhibited excellent indicator performance of prognosis and immune infiltration, which has the potential to be an effective biomarker for NSCLC with ICI treatment.

Meta-analysis of the Selected Genetic Variants in Immune-Related Genes and Multiple Sclerosis Risk.

Previous studies have suggested that certain variants in immune-related genes may participate in the pathogenesis of multiple sclerosis (MS), including rs17824933 in the CD6 gene, rs1883832 in the CD40 gene, rs2300747 in the CD58 gene, rs763361 in the CD226 gene, rs16944 in the IL-1ß gene, rs2243250 in the IL-4 gene, and rs12722489 and rs2104286 in the IL-2Rα gene. However, the results remained inconclusive and conflicting. In view of this, a comprehensive meta-analysis including all eligible studies was conducted to investigate the association between these 8 selected genetic variants and MS risk. Up to June 2023, 64 related studies were finally included in this meta-analysis. The odds ratios (ORs) and corresponding 95% confidence intervals (CIs) calculated by the random-effects model were used to evaluate the strength of association. Publication bias test, sensitivity analyses, and trial sequential analysis (TSA) were conducted to examine the reliability of statistical results. Our results indicated that rs17824933 in the CD6 gene, rs1883832 in the CD40 gene, rs2300747 in the CD58 gene, rs763361 in the CD226 gene, and rs12722489 and rs2104286 in the IL-2Rα gene may serve as the susceptible factors for MS pathogenesis, while rs16944 in the IL-1ß gene and rs2243250 in the IL-4 gene may not be associated with MS risk. However, the present findings need to be confirmed and reinforced in future studies.

Construction of an immune-related prognostic signature and lncRNA-miRNA-mRNA ceRNA network in acute myeloid leukemia.

The progression of acute myeloid leukemia (AML) is influenced by the immune microenvironment in the bone marrow and dysregulated intracellular competing endogenous RNA (ceRNA) networks. Our study utilized data from UCSC Xena, The Cancer Genome Atlas Program, the Gene Expression Omnibus, and the Immunology Database and Analysis Portal. Using Cox regression analysis, we identified an immune-related prognostic signature. Genomic analysis of prognostic messenger RNA (mRNA) was conducted through Gene Set Cancer Analysis (GSCA), and a prognostic ceRNA network was constructed using the Encyclopedia of RNA Interactomes. Correlations between signature mRNAs and immune cell infiltration, checkpoints, and drug sensitivity were assessed using R software, gene expression profiling interactive analysis (GEPIA), and CellMiner, respectively. Adhering to the ceRNA hypothesis, we established a potential long noncoding RNA (lncRNA)/microRNA (miRNA)/mRNA regulatory axis. Our findings pinpointed 9 immune-related prognostic mRNAs (KIR2DL1, CSRP1, APOBEC3G, CKLF, PLXNC1, PNOC, ANGPT1, IL1R2, and IL3RA). GSCA analysis revealed the impact of copy number variations and methylation on AML. The ceRNA network comprised 14 prognostic differentially expressed lncRNAs (DE-lncRNAs), 6 prognostic DE-miRNAs, and 3 prognostic immune-related DE-mRNAs. Correlation analyses linked these mRNAs' expression to 22 immune cell types and 6 immune checkpoints, with potential sensitivity to 27 antitumor drugs. Finally, we identified a potential LINC00963/hsa-miR-431-5p/CSRP1 axis. This study offers innovative insights for AML diagnosis and treatment through a novel immune-related signature and ceRNA axis. Identified novel biomarkers, including 2 mRNAs (CKLF, PNOC), 1 miRNA (hsa-miR-323a-3p), and 10 lncRNAs (SNHG25, LINC01857, AL390728.6, AC127024.5, Z83843.1, AP002884.1, AC007038.1, AC112512, AC020659.1, AC005921.3) present promising candidates as potential targets for precision medicine, contributing to the ongoing advancements in the field.

An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence.

Background: This study aimed to establish and validate a prognostic model based on immune-related genes (IRGPM) for predicting disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy, and to elucidate the immune profiles associated with different prognostic outcomes. Methods: Transcriptomic and clinical data were sourced from the Gene Expression Omnibus (GEO) database and the West China Hospital database. We focused on genes from the RNA immune-oncology panel. The elastic net approach was employed to pinpoint immune-related genes significantly impacting DFS. We developed the IRGPM for rectal cancer using the random forest technique. Based on the IRGPM, we calculated prognostic risk scores to categorize patients into high-risk and low-risk groups. Comparative analysis of immune characteristics between these groups was conducted. Results: In this study, 407 LARC samples were analyzed. The elastic net identified a signature of 20 immune-related genes, forming the basis of the IRGPM. Kaplan-Meier survival analysis revealed a lower 5-year DFS in the high-risk group compared to the low-risk group. The receiver operating characteristic (ROC) curve affirmed the model's robust predictive capability. Validation of the model was performed in the GSE190826 cohort and our institution's cohort. Gene expression differences between high-risk and low-risk groups predominantly related to cytokine-cytokine receptor interactions. Notably, the low-risk group exhibited higher immune scores. Further analysis indicated a greater presence of activated B cells, activated CD8 T cells, central memory CD8 T cells, macrophages, T follicular helper cells, and type 2 helper cells in the low-risk group. Additionally, immune checkpoint analysis revealed elevated PDCD1 expression in the low-risk group. Conclusions: The IRGPM, developed through random forest and elastic net methodologies, demonstrates potential in distinguishing DFS among LARC patients receiving standard treatment. Notably, the low-risk group, as defined by the IRGPM, showed enhanced activation of adaptive immune responses within the tumor microenvironment.

[Screening of immune related gene and survival prediction of lung adenocarcinoma patients based on LightGBM model].

Lung cancer is one of the malignant tumors with the greatest threat to human health, and studies have shown that some genes play an important regulatory role in the occurrence and development of lung cancer. In this paper, a LightGBM ensemble learning method is proposed to construct a prognostic model based on immune relate gene (IRG) profile data and clinical data to predict the prognostic survival rate of lung adenocarcinoma patients. First, this method used the Limma package for differential gene expression, used CoxPH regression analysis to screen the IRG to prognosis, and then used XGBoost algorithm to score the importance of the IRG features. Finally, the LASSO regression analysis was used to select IRG that could be used to construct a prognostic model, and a total of 17 IRG features were obtained that could be used to construct model. LightGBM was trained according to the IRG screened. The K-means algorithm was used to divide the patients into three groups, and the area under curve (AUC) of receiver operating characteristic (ROC) of the model output showed that the accuracy of the model in predicting the survival rates of the three groups of patients was 96%, 98% and 96%, respectively. The experimental results show that the model proposed in this paper can divide patients with lung adenocarcinoma into three groups [5-year survival rate higher than 65% (group 1), lower than 65% but higher than 30% (group 2) and lower than 30% (group 3)] and can accurately predict the 5-year survival rate of lung adenocarcinoma patients.

Bulk and single-cell transcriptome reveal the immuno-prognostic subtypes and tumour microenvironment heterogeneity in HCC.

BACKGROUND & AIMS: Accumulating evidences suggest tumour microenvironment (TME) profoundly influence clinical outcome in hepatocellular carcinoma (HCC). Existing immune subtypes are susceptible to batch effects, and integrative analysis of bulk and single-cell transcriptome is helpful to recognize immune subtypes and TME in HCC. METHODS: Based on the relative expression ordering (REO) of 1259 immune-related genes, an immuno-prognostic signature was developed and validated in 907 HCC samples from five bulk transcriptomic cohorts, including 72 in-house samples. The machine learning models based on subtype-specific gene pairs with stable REOs were constructed to jointly predict immuno-prognostic subtypes in single-cell RNA-seq data and validated in another single-cell data. Then, cancer characteristics, immune landscape, underlying mechanism and therapeutic benefits between subtypes were analysed. RESULTS: An immune-related signature with 29 gene pairs stratified HCC samples individually into two risk subgroups (C1 and C2), which was an independent prognostic factor for overall survival. The machine learning models verified the immune subtypes from five bulk cohorts to two single-cell transcriptomic data. Integrative analysis revealed that C1 had poorer outcomes, higher CNV burden and malignant scores, higher sensitivity to sorafenib, and exhibited an immunosuppressive phenotype with more regulators, e.g., myeloid-derived suppressor cells (MDSCs), Mø_SPP1, while C2 was characterized with better outcomes, higher metabolism, more benefit from immunotherapy, and displayed active immune with more effectors, e.g., tumour infiltrating lymphocyte and dendritic cell. Moreover, both two single-cell data revealed the crosstalk of SPP1-related L-R pairs between cancer and immune cells, especially SPP1-CD44, might lead to immunosuppression in C1. CONCLUSIONS: The REO-based immuno-prognostic subtypes were conducive to individualized prognosis prediction and treatment options for HCC. This study paved the way for understanding TME heterogeneity between immuno-prognostic subtypes of HCC.

Unleashing the potential of gene signatures as prognostic and predictive tools: A step closer to personalized medicine in hepatocellular carcinoma (HCC).

Hepatocellular carcinoma (HCC) is one of the growing malignancies globally, affecting a myriad of people and causing numerous cancer-related deaths. Despite therapeutic improvements in treatment strategies over the past decades, HCC still remains one of the leading causes of person-years of life lost. Numerous studies have been conducted to assess the characteristics of HCC with the aim of predicting its prognosis and responsiveness to treatment. However, the identified biomarkers have shown limited sensitivity, and the translation of these findings into clinical practice has faced challenges. The development of sequencing techniques has facilitated the exploration of a wide range of genes, leading to the emergence of gene signatures. Although several studies assessed differentially expressed genes in normal and HCC tissues to find the unique gene signature with prognostic value, to date, no study has reviewed the task, and to the best of our knowledge, this review represents the first comprehensive analysis of relevant studies in HCC. Most gene signatures focused on immune-related genes, while others investigated genes related to metabolism, autophagy, and apoptosis. Even though no identical gene signatures were found, NDRG1, SPP1, BIRC5, and NR0B1 were the most extensively studied genes with prognostic value. Finally, despite challenges such as the lack of consistent patterns in gene signatures, we believe that comprehensive analysis of pertinent gene signatures will bring us a step closer to personalized medicine in HCC, where treatment strategies can be tailored to individual patients based on their unique molecular profiles.

Transcriptomic analysis of skin immunity genes in the Chinese spiny frog (Quasipaa spinosa) after Proteus mirabilis infection.

Recently, populations of Chinese spiny frogs (Quasipaa spinosa), an important amphibian species in China, have decreased, mainly due to a disease caused by the gram-negative bacteria Proteus mirabilis. To elucidate the immune response of the frogs, this study aimed to identify novel candidate genes functionally associated with P. mirabilis infection-induced "rotting skin" disease. Chinese spiny frogs were infected with P. mirabilis, and the skin transcriptome was sequenced using the MGISEQ-2000 platform. A total of 233,965 unigenes were obtained by sequencing, of which 27.23 % were known genes. Screening of differentially expressed genes (DEGs) indicated 210 unigenes differentially expressed after P. mirabilis infection, of which 132 unigenes were up-regulated, and 78 unigenes were down-regulated. Using Kyoto Encyclopedia of Genes and Genomes enrichment analysis, DEGs were identified as enriched in signal pathways, such as oxidative phosphorylation, apoptosis, and the Janus kinase-signal transducer and activator of transcription pathway. Of the DEGs, there was a significant upregulation of the colony stimulating factor 2 receptor beta common subunit, interleukin 2 receptor subunit gamma, cathelicidin antimicrobial peptide, interleukin-17 receptor E, receptor-interacting serine/threonine-protein kinase 3, and pulmonary surfactant-associated protein D immune genes following P. mirabilis infection. Conversely, scavenger receptor cysteine-rich domain-containing group B protein, tumor protein p53 inducible nuclear protein 2, suppressor of cytokine signaling 2, and metalloreductase STEAP3 were significantly downregulated. In conclusion, the first skin transcriptome database of Chinese spiny frogs was established, and several immune genes were identified to elucidate the pathogenic mechanism of "skin rot" in Chinese spiny frogs and other cultured frogs.

Molecular characterization and functional analysis of a beta-1,3-glucan recognition protein from oriental fruit moth Grapholita molesta (Lepidoptera: Tortricidae).

The beta-1,3-glucan recognition protein (BGRP) is an important pattern recognition protein (PRP), which plays an important role in immune recognition and signaling pathway of insect innate immunity. Herein, a BGRP gene was obtained from the transcriptome of Grapholita molesta and its expression was verified by PCR. The full cDNA of the GmBGRP gene was 1691 bp encoding 486 amino acid residues. The calculated molecular mass of the mature protein was 54.83 kDa with an estimated pI of 6.14. The amino acid sequence of GmBGRP was highly homologous to BGRPs of other lepidopterans including Leguminivora glycinivorella BGRP-3. Expression profile of GmBGRP at different developmental stages and different tissues of 5th instar larvae showed that the expression level of this gene tends to slightly increase and then decrease at the adult stage, with the highest at the pupa stage; and mainly expressed in the epidermis, fat body and hemocytes compared with other tissues. In addition, we investigated the transcription levels of other immune-related genes, such as Serine-1, Serine-2, Serine-3, Serpin, SRCB (scavenger receptor gene), Toll, PPO (prophenoloxidase) upon GmBGRP gene silencing, indicating that GmBGRP expression is associated with immune responses of G. molesta. This was further supported by the upregulation of the mRNA level of GmBGRP following fungal infection. Taken together, these results provide experimental evidence for the role of GmBGRP gene in immune defense in G. molesta larvae.

Effects of the combination of chitosan and Acinetobacter KU011TH on the growth and health performances and disease resistance of juvenile hybrid catfish (Clarias gariepinus × C. macrocephalus).

Aquatic animal health management has become a crucial component in the goal of increasing catfish aquaculture productivity. Additionally, hybrid catfish (Clarias gariepinus × C. macrocephalus) has been promoted as a highly profitable freshwater fish in Asia. Interestingly, the crucial diseases induced by Aeromonas hydrophila have been reported to greatly impede catfish production. To overcome this challenge, the aim was to investigate the effects of the oral administration of potentially synbiotic chitosan (CH) and Acinetobacter KU011TH (AK) on the growth performance, immunological responses, and disease resistance of hybrid catfish against A. hydrophila. The control group was fed a basal diet (A), the diet fed to treatment group B was supplemented with 20 mL of CH/kg diet (B), and the experimental feed fed to groups C-D was mixed with 1 × 108, 1 × 109 and 1 × 1010 CFU/mL AK coated with 20 mL of CH/kg diet. Five different groups of juvenile hybrid catfish were continuously fed the 5 formulated feeds for 4 weeks. The results revealed that all tested feeds did not significantly enhance the hybrid catfish's average daily gain, specific growth rate, feed conversion ratio, hematocrit and erythrocyte counts. Interestingly, the application of CH and AK significantly increased the leukocyte counts, respiratory burst, lysozyme activity, alternative complement pathway hemolytic activity, and bactericidal activity (P < 0.05). The expression levels of the immune-related genes in the whole blood, head kidney, and spleen were significantly increased after CH-AK application (P < 0.05), but this finding was not observed in the liver (P > 0.05). Additionally, after 14 days of A. hydrophila peritoneal injection, the fish in group C showed significantly higher survival rates of approximately 70.0 % compared with the control fish in groups B, D, and E (52.5 %, 40.0 %, 45.0 %, and 45.0 %, respectively) (P < 0.05). These results collectively suggest that short-term application of the diet fed to group C effectively boosted the immune responses and disease resistance of hybrid catfish against A. hydrophila.

Molecular cytogenetic analysis of the olive flounder embryonic cell line FGBC8 and its applicability to biotechnology.

We explored the biotechnological applicability of a previously established olive flounder (Paralichthys olivaceus) embryonic cell line (FGBC8). FGBC8 was transfected with pEGFP-c1 and pluripotency-related genes, then infected with viral hemorrhagic septicemia virus (VHSV), and the expression of immune-related genes was observed through quantitative real-time polymerase chain reaction. Transfected cells showed strong green fluorescence 48 h after transfection, and pluripotency-related genes were successfully transfected. In addition, FGBC8 cells were highly susceptible to VHSV and the expression of immune-related genes was induced during infection. Our results demonstrate that FGBC8 cells are valuable research tools for assessing host-pathogen interactions and biotechnological applications.