OBJECTIVE: This study aims to explore ADH4 expression in hepatocellular carcinoma (HCC), its prognostic impact, and its immune correlation to provide novel insights into HCC prognostication and treatment.
METHODS: HCC prognostic marker genes were rigorously selected using GEO database, Lasso regression, GEPIA, Kaplan-Meier and pROC analyses. The expression of interested markers (ADH4, DNASE1L3, RDH16, LCAT, HGFAC) in HCC and adjacent tissues was assessed by Immunohistochemistry (IHC). We observed that ADH4 exhibited low expression levels in liver cancer tissues and high expression levels in normal liver tissues. However, the remaining four genes did not manifest any statistically significant differences between hepatocellular carcinoma (HCC) tissue and adjacent non-cancerous tissue. Consequently, ADH4 became the primary focus of our research. ADH4 expression was validated by signed-rank tests and unpaired Wilcoxon rank sum tests across pan-cancer and HCC datasets. Clinical significance and associations with clinicopathological variables were determined using Kaplan-Meier, logistic regression and Cox analyses on TCGA data. The ADH4-related immune responses were explored by Spearman correlation analysis using TIMER2 data. CD68, CD4, and CD19 protein levels were confirmed by IHC in HCC and non-cancerous tissues.
RESULTS: ADH4 showed significant downregulation in various cancers, particularly in HCC. Moreover, low ADH4 expression was associated with clinicopathological variables and served as an independent prognostic marker for HCC patients. Additionally, ADH4 affects a variety of biochemical functions and may influence cancer development, prognosis, and treatment by binding to immune cells. Furthermore, at the immune level, the low expression pattern of ADH4 is TME-specific, indicating that ADH4 has the potential to be used as a target for cancer immunotherapy.
CONCLUSIONS: This study highlights the diagnostic, prognostic and immunomodulatory roles of ADH4 in HCC. ADH4 could serve as a valuable biomarker for HCC diagnosis and prognosis, as well as a potential target for immunotherapeutic interventions.

OBJECTIVE: This study aimed to improve lung adenocarcinoma (LUAD) prognosis prediction based on a signature of immune-related long non-coding RNAs (lncRNAs).
METHODS: LUAD samples from the TCGA database were divided into the immunity_H group and the immunity_L group. Differentially expressed RNAs (DERs) between the two groups were identified. Optimized immune-related lncRNAs combination was obtained using LASSO Cox regression. A prognostic risk prediction (RS) model was built and further validated in the training and validation datasets. A network among lncRNAs in the RS model, their co-expressed DERs, and the related KEGG pathways were established. Critical lncRNAs were validated in LUAD tissue samples.
RESULTS: In total, 255 DERs were obtained, and 11 immune-related lncRNAs were significantly related to prognosis. Six lncRNAs were demonstrated as an optimal combination for building the RS model, including LINC00944, LINC00930, LINC00607, LINC00582, LINC00543, and LINC00319. The KM curve and ROC curve revealed the RS model to be a reliable indicator for LUAD prognosis. LINC00944 and LINC00582 showed a co-expression relationship with the MS4A1. LINC00944, LINC00582, and MS4A1 were successfully validated in LUAD samples.
CONCLUSIONS: We have established a promising LUAD patient survival prediction model based on six immune-related lncRNAs. For LUAD patients, this prognostic model could guide personalized treatment.

BACKGROUND: Approximately 60% of patients with autoimmune encephalitis (AE) exhibit secondary acute symptomatic seizures and showed highly sensitive to immunotherapy. However, it is difficult for many patients to receive early immunotherapy since the early identification of the cause in AE is more complex. This study aimed to investigate the early predictors of initial immune-related seizures and to guide the evaluation of treatment and prognosis.
METHODS: One hundred and fifty-four patients with new-onset \"unknown etiology\" seizures with a course of disease less than 6 months were included. Serum and/or cerebrospinal fluid neuron-specific autoantibodies (NSAbs), including N-methyl-D-aspartate receptor (NMDAR), α-amino-3-hydroxy-5- Methyl-4-isoxazole propionic acid receptor 1 (AMPAR1), AMPAR2, anti-leucine rich glioma inactivated 1 antibody (LGI1), anti-gamma-aminobutyric acid type B receptor (GABABR), anti-contact protein-related protein-2 (CASPR2) were used to screen for immune etiology of the seizures. In addition, patients with epilepsy and encephalopathy were also examined via brain MRI, long-term video EEG, antibody prevalence in epilepsy and encephalopathy (APE2) score, and modified Rankin Scale (mRS). A logistic regression model was used to analyze the early predictors of immune etiology.
RESULTS: Thirty-four cases (22.1%) were positive for NSAbs. Among all 154 patients, 23 cases of autoimmune encephalitis (AE) (21 cases of NSAbs positive), 1 case of ganglionic glioma (NSAbs positive), 130 cases of epilepsy or seizures (12 cases of NSAbs positive) were recorded. Also, there were 17 patients (11.0%) with APE2 ≥ 4 points, and all of them met the clinical diagnosis of AE. The sensitivity and specificity of APE2 ≥ 4 points for predicting AE were 73.9% and 100%. The results of multivariate analysis showed that the NSAbs and APE2 scores independently influenced the early prediction of initial immune-related seizures (P < 0.05).
CONCLUSIONS: NSAbs and APE2 scores could act as early predictors of initial immune-related seizures.

UNASSIGNED: This study aimed to investigate the role of ficolin-2 (FCN2) in the development and course of hepatocellular carcinoma (HCC) and to contribute to the evolution of innovative HCC therapeutics.
UNASSIGNED: Oncomine, GEPIA (Gene Expression Profiling Interactive Analysis), TISIDB (Tumor Immune System Interactions and Drug Bank database), UALCAN (University of Alabama at Birmingham Cancer data analysis portal), UCSC (University of California, Santa Cruz), R package, the Kaplan-Meier technique, Cox regression analysis, LinkedOmics, Pearson\'s correlation, and a nomogram were used to investigate the prognostic value of FCN2 in HCC. Co-expressed genes were screened. A protein-protein interaction network was created using the STRING database. Finally, immunohistochemistry was performed to establish the expression of FCN2 in HCC tissues. A pan-cancer study centered on HCC-related molecular analysis was also conducted to look for a link between FCN2 and immune infiltration, immune modulators, and chemokine receptors.
UNASSIGNED: In HCC tissues, the expression of FCN2 was observed to be lower than that in normal tissues. This was connected to the HCC marker alpha-fetoprotein, showing that FCN2 is involved in the development and progression of cancer. FCN2 may act through Staphylococcus aureus infection, lectins, and other pathways. Furthermore, at the immune level, the expression of FCN2 in HCC was associated with some immune cell infiltration, immunomodulators, and chemokine receptors.
UNASSIGNED: FCN2 may be an immune checkpoint inhibitor for HCC, creating a breakthrough in the treatment of HCC.

Hepatocellular carcinoma (HCC) is the most common neoplasm and the major cause of cancer-associated death worldwide. The high mortality rate of HCC is mainly attributed to its widespread prevalence and the lack of effective treatment. Immunotherapy as a promising, innovative approach has revolutionised the treatment of solid tumours. However, owing to the heterogeneity and complex tumour microenvironment of HCC, an efficient biomarker for immunotherapy has yet to be identified. We investigated the role of immune-related long non-coding RNAs (lncRNAs) as prognostic biomarkers in patients with HCC from The Cancer Genome Atlas (TCGA) database. Spearman correlation, univariate and multivariate Cox, and lasso regression analyses were utilised to screen lncRNAs associated with prognosis. Four lncRNAs were filtered out to develop an immune-associated lncRNA prognostic signature in TCGA training as well as validation cohorts. Patients with HCC were then categorised into low- and high-risk groups according to the median value of the risk scores to evaluate the ability of the prognostic model between training and validation cohorts. A nomogram (based on risk score and stage) was constructed to appraise the general overall survival (OS) of patients with HCC. Differences in immune cell infiltration, immune checkpoint inhibitor (ICI) treatment response, gene mutation, and drug sensitivity were observed between the two groups. Thus, the lncRNA prognostic signature can serve as a sensitive prognostic biomarker with potential in individualised immunotherapy for HCC patients.

The immune response is an important part of secondary brain injury following intracerebral hemorrhage (ICH), and is related to neurological deficits and prognosis. The mechanisms underlying the immune response and inflammation are of great significance for brain injury and potential functional restoration; however, the immune-related biomarkers and competing endogenous ribonucleic acid (RNA) (ceRNA) networks in the peripheral blood of ICH patients have not yet been constructed. We collected the peripheral blood from ICH patients and controls to assess their ceRNA profiles using LCHuman ceRNA microarray, and to verify their expression with qRT-PCR. Two-hundred-eleven DElncRNAs and one-hundred-one DEmRNAs were detected in the ceRNA microarray of ICH patients. The results of functional enrichment analysis showed that the immune response was an important part of the pathological process of ICH. Twelve lncRNAs, ten miRNAs, and seven mRNAs were present in our constructed immune-related ceRNA network, combining weighted gene co-expression network analysis (WGCNA). Our study was the first to establish the network of the immune-related ceRNAs derived from WGCNA, and to identify leukemia inhibitory factor (LIF) and B cell lymphoma 2-like 13 (BCL2L13) as pivotal immune-related biomarkers in the peripheral blood of ICH patients, which are likely associated with PI3K-Akt, the MAPK signaling pathway, and oxidative phosphorylation. The MOXD2P-miR-211-3p -LIF and LINC00299-miR-198-BCL2L13 axes were indicated to participate in the immune regulatory mechanism of ICH. The goal of our study was to offer innovative insights into the underlying immune regulatory mechanism and to identify possible immune intervention targets for ICH.

Clear-cell renal cell carcinoma (ccRCC) is one of the most common urological cancers. The tumor microenvironment plays an important role in tumor development. The present study was conducted to identify novel immune-related biomarkers. The differentially expressed genes were identified using the ESTIMATE algorithm base on GEO and TCGA databases. The Kaplan-Meier survival curve and univariate and multivariate analyses were performed. The association between ST8SIA1 and the immune system was explored. The gene set enrichment analysis (GSEA) and online databases were used for functional annotation. ST8SIA1 was identified as a potential prognostic gene. Elevated ST8SIA1 was observed in the tumor tissues compared with adjacent normal tissues and associated with higher T stage and advanced TNM stage (all p < 0.05). The mRNA and protein levels of ST8SIA1 in cancer tissues and cells are also upregulated. The Kaplan-Meier survival curve and univariate and multivariate analyses showed that higher expression of ST8SIA1 was associated with worse OS (all p < 0.05). ST8SIA1 expression levels were negatively correlated with tumor purity and positively associated with infiltrated immune cells and expression of immune checkpoint genes. Function analysis also revealed that ST8SIA1 was significantly associated with immune-related pathways. In conclusion, ST8SIA1 was identified as an immune-related gene and a potential target in ccRCC patients. Further relevant studies are required to validate our findings.

BACKGROUND: Bladder cancer (BCa) shows its potential immunogenity in current immune-checkpoint inhibitor related immunotherapies. However, its therapeutic effects are improvable and could be affected by tumor immune microenvironment. Hence it is interesting to find some more prognostic indicators for BCa patients concerning immunotherapies.
METHODS: In the present study, we retrospect 129 muscle-invasive BCa (MIBC) patients with radical cystectomy in Shanghai General Hospital during 2007 to 2018. Based on the results of proteomics sequencing from 9 pairs of MIBC tissue from Shanghai General Hospital, we focused on 13 immune-related differential expression proteins and their related genes. An immune-related prognostic signature (IRPS) was constructed according to Cancer Genome Atlas (TCGA) dataset. The IRPS was verified in ArrayExpress (E-MTAB-4321) cohort and Shanghai General Hospital (General) cohort, separately. A total of 1010 BCa patients were involved in the study, including 405 BCa patients in TCGA cohort, 476 BCa patients in E-MTAB-4321 cohort and 129 MIBC patients in General cohort.
RESULTS: It can be indicated that high IRPS score was related to poor 5-year overall survival and disease-free survival. The IRPS score was also evaluated its immune infiltration. We found that the IRPS score was adversely associated with GZMB, IFN-γ, PD-1, PD-L1. Additionally, higher IRPS score was significantly associated with more M2 macrophage and resting mast cell infiltration.
CONCLUSIONS: The study revealed a novel BCa prognostic signature based on IRPS score, which may be useful for BCa immunotherapies.

Purpose: Our research developed immune-related long noncoding RNAs (lncRNAs) for risk stratification in cervical cancer (CC) and explored factors of prognosis, inflammatory microenvironment infiltrates, and chemotherapeutic therapies. Methods: The RNA-seq data and clinical information of CC were collected from the TCGA TARGET GTEx database and the TCGA database. lncRNAs and immune-related signatures were obtained from the GENCODE database and the ImPort database, respectively. We screened out immune-related lncRNA signatures through univariate Cox, LASSO, and multivariate Cox regression methods. We established an immune-related risk model of hub immune-related lncRNAs to evaluate whether the risk score was an independent prognostic predictor. The xCell and CIBERSORTx algorithms were employed to appraise the value of risk scores which are in competition with tumor-infiltrating immune cell abundances. The estimation of tumor immunotherapy response through the TIDE algorithm and prediction of innovative recommended medications on the target to immune-related risk model were also performed on the basis of the IC50 predictor. Results: We successfully established six immune-related lncRNAs (AC006126.4, EGFR-AS1, RP4-647J21.1, LINC00925, EMX2OS, and BZRAP1-AS1) to carry out prognostic prediction of CC. The immune-related risk model was constructed in which we observed that high-risk groups were strongly linked with poor survival outcomes. Risk scores varied with clinicopathological parameters and the tumor stage and were an independent hazard factor that affect prognosis of CC. The xCell algorithm revealed that hub immune-related signatures were relevant to immune cells, especially mast cells, DCs, megakaryocytes, memory B cells, NK cells, and Th1 cells. The CIBERSORTx algorithm revealed an inflammatory microenvironment where naive B cells (p < 0.01), activated dendritic cells (p < 0.05), activated mast cells (p < 0.0001), CD8+ T cells (p < 0.001), and regulatory T cells (p < 0.01) were significantly lower in the high-risk group, while macrophages M0 (p < 0.001), macrophages M2 (p < 0.05), resting mast cells (p < 0.0001), and neutrophils (p < 0.01) were highly conferred. The result of TIDE indicated that the number of immunotherapy responders in the low-risk group (124/137) increased significantly (p = 0.00000022) compared to the high-risk group (94/137), suggesting that the immunotherapy response of CC patients was completely negatively correlated with the risk scores. Last, we compared differential IC50 predictive values in high- and low-risk groups, and 12 compounds were identified as future treatments for CC patients. Conclusion: In this study, six immune-related lncRNAs were suggested to predict the outcome of CC, which is beneficial to the formulation of immunotherapy.

Aim: To explore the incidence, risk factors and overall outcome of the first episode of immune checkpoint inhibitor-related acute kidney injury (ICI-AKI) in Chinese patients receiving PD-1 inhibitors. Methods: Data for patients receiving PD-1 inhibitors at Jiangsu Province Hospital between December 2017 and January 2020 were retrospectively reviewed. Results: A total of 5.6% of 551 patients receiving PD-1 inhibitors developed ICI-AKI. Concomitant use of NSAIDs, ICI cycles and extrarenal immune-related adverse events may be independently associated with ICI-AKI. ICI-AKI may not be a risk factor for increased mortality or worse progression-free survival. Conclusions: ICI-AKI is relatively rare and its occurrence may not affect the overall 6-month outcome of patients receiving PD-1 inhibitors. Further studies are needed to verify these findings.
Immune checkpoint inhibitors (ICIs) have been more and more commonly used in patients with cancer. Therefore, it is important to understand the immune-related adverse events (irAEs), including immune-related renal adverse events, caused by ICIs. In this article, the authors explore the incidence, clinical features, risk factors and overall outcome of immune checkpoint inhibitor related-acute kidney injury (ICI-AKI) in Chinese patients treated with PD-1 inhibitors for the first time. Among 551 patients treated with PD-1 inhibitors, 65 patients experienced AKI and 31 patients experienced ICI-AKI. Patients with ICI-AKI may be more likely to receive nonsteroidal anti-inflammatory drugs, to receive PD-1 inhibitors for longer cycles or to experience extrarenal immune-related adverse events prior to or concomitant with ICI-AKI. The occurrence of ICI-AKI may not affect the survival time or disease progression of patients with cancer.