Six immune-related promising biomarkers may promote hepatocellular carcinoma prognosis: a bioinformatics analysis and experimental validation.

BACKGROUND: Abnormal miRNA and mRNA expression and dysregulated immune microenvironment have been found to frequently induce the progression of hepatocellular carcinoma (HCC) in recent reports. In particular, the immune-related competing endogenous RNAs (ceRNA) mechanism plays a crucial role in HCC progression. However, the underlying mechanisms remain unclear. METHODS: Differentially expressed immune-related genes were obtained from the Immport, GEO, and TCGA databases. The mRNA and protein expression levels in HCC tissues and adjacent normal tissues were confirmed, and we further investigated the methylation levels of these biomarkers to explore their function. Then, the TIMER and TISCH databases were used to assess the relationship between immune infiltration and hub genes. Survival analysis and univariate and multivariate Cox models were used to evaluate the association between hub genes and HCC diagnosis. Hub gene expression was experimentally validated in six HCC cell lines and 15 HCC samples using qRT-PCR and immunohistochemistry. The hub genes were uploaded to DSigDB for drug prediction enrichment analysis. RESULTS: We identified that patients with abnormal miRNAs (hsa-miR-125b-5p and hsa-miR-21-5p) and their targeted genes (NTF3, PSMD14, CD320, and SORT1) had a worse prognosis. Methylation analysis of miRNA-targeted genes suggested that alteration of methylation levels is also a factor in the induction of tumorigenesis. We also found that the development of HCC progression caused by miRNA-mRNA interactions may be closely correlated with the infiltration of immunocytes. Moreover, the GSEA, GO, and KEGG analysis suggested that several common immune-related biological processes and pathways were related to miRNA-targeted genes. The results of qRT-PCR, immunohistochemistry, and western blotting were consistent with our bioinformatics results, suggesting that abnormal miRNAs and their targeted genes may affect HCC progression. CONCLUSIONS: Briefly, our study systematically describes the mechanisms of miRNA-mRNA interactions in HCC and predicts promising biomarkers that are associated with immune filtration for HCC progression.

Prognostication of a 13-immune-related-gene signature in patients with early triple-negative breast cancer.

PURPOSE: We investigated the expression profiles of immune genes in patients with triple-negative breast cancer (TNBC) to identify the prognostic value of immune genes and their clinical implications. METHODS: NanoString nCounter Analysis of 770 immune-related genes was used to measure immune gene expression in patients with TNBC who underwent curative surgery followed by adjuvant chemotherapy at Samsung Medical Center between 2000 and 2004. Statistical analyses were conducted to identify the associations between gene expression and distant recurrence-free survival (DRFS). RESULTS: Of 1189 patients who underwent curative BC surgery, 200 TNBC patients were included and stage was the only clinical factor predictive of DRFS. In terms of immune genes, 155 of 770 genes were associated with DRFS (p < 0.01). Further multivariate analysis revealed that 13 genes, CD1B, CD53, CT45A1, GTF3C1, IL11RA, IL1RN, LRRN3, MAPK1, NEFL, PRKCE, PTPRC, SPACA3 and TNFSF11, were associated with patient prognosis (p < 0.05). The prognostic value of stage and expression levels of 13 immune genes was analyzed and the area under the receiver operating characteristic curve (AUC) was 0.923. Based on the AUC, we divided patients into three genetic risk groups and DRFS rate was significantly different according to genetic risk groups, even in the same stage (p < 0.001). CONCLUSIONS: In this study, a 13-gene expression profile in combination with stage precisely predicted distant recurrence of early TNBC. Therefore, this 13-immune-gene signature could help predict TNBC prognosis and provide guidance for treatment as well as the opportunity to develop new targets for immunotherapy in TNBC patients.

Identification of Immune Gene Signature Associated with T Cells and Natural Killer Cells in Type 1 Diabetes.

Purpose: This study aimed to investigate the abnormal infiltration of immune cells in type 1 diabetes mellitus (T1D) and elucidate their regulatory mechanisms. Methods: Public T1D-related gene expression data were obtained from the Gene Expression Omnibus database.The GSE123658 dataset analyzed whole blood RNA-seq data from type 1 diabetic patients and healthy volunteers. The GSE110914 dataset analyzed neutrophils purified from peripheral blood of patients with symptomatic and pre-symptomatic type 1 diabetes (T1D), at risk of T1D, and healthy controls. Immune cell infiltration analysis was performed to identify abnormally infiltrating immune cells. Differentially expressed immune genes (DEIGs) in T1D samples were identified, followed by the construction of an immune gene signature (IGS) using a protein-protein interaction (PPI) network and Least absolute shrinkage and selection operator Cox regression analyses (LASSO Cox regression analyses). The regulatory mechanisms underlying IGS were explored using gene set enrichment analysis. Furthermore, expression validation, diagnostic efficacy evaluation, and upstream miRNA prediction of hub signature genes were performed. We verified the miRNA expression of the key gene colony stimulating factor 1 (CSF1) and microRNA-326 (miR-326) by reverse transcription-quantitative PCR (RT‒qPCR). Results: The proportion of infiltrating T and natural killer (NK) cells differed between the T1D and control samples, and 207 immune genes (IGs) related to these immune cells were extracted. After differential expression, PPI, and LASSO Cox regression analyses, four signature DEIGs were identified for IGS construction: notch receptor 1 (NOTCH1), Janus kinase 3 (JAK3), tumor necrosis factor receptor superfamily member 4(TNFRSF4), and CSF1. Key pathways such as the Toll-like receptor signaling pathway were significantly activated in the high-risk group. Moreover, the upregulation of CSF1 in T1D samples was confirmed using a validation dataset, and CSF1 showed high diagnostic efficacy for T1D. Furthermore, CSF1 was targeted by miR-326.We used validated key genes in T1D patients, several of which were confirmed by RT‒qPCR. Conclusion: In conclusion, the identified key IGs may play an important role in T1D. CSF1 can be developed as a novel diagnostic biomarker for T1D.

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in ER (+) and/or PR (+) and HER2 (-) Breast Cancer.

Background: Although intrinsic molecular subtype has been widely used, there remains great clinical heterogeneity of prognosis in the estrogen receptor (ER)- and/or progesterone receptor (PR)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer (BC). Methods: The transcriptome expression data of messenger RNA (mRNA) were downloaded from The Cancer Genome Atlas (TCGA), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and the Gene Expression Omnibus (GEO) databases. Immune-related genes were acquired from the ImmPort database and additional literature search. Univariate Cox, LASSO regression, and multivariate Cox regression were used to screen prognostic immune-related genes and establish the risk signature. The correlation between the risk signature and clinical characteristics, the abundances of immune cells within the tumor microenvironment, and cancer phenotypes were further assessed. Results: Of note, 102 immune-related prognostic genes were identified in the METABRIC dataset by univariate Cox analysis. Consecutively, 7 immune-related genes (SHMT2, AGA, COL17A1, FLT3, SLC7A2, ATP6AP1, and CCL19) were selected to establish the risk signature by LASSO regression and multivariate Cox analysis. Its performance was further verified in TCGA and GSE21653 datasets. Multivariate Cox analysis showed that the risk signature was an independent prognostic factor. The 7-gene signature showed a significant correlation with intrinsic molecular subtypes and 70-gene signature. Furthermore, the CD4+ memory T cells were significantly higher in the low-risk group while a significantly higher proportion of M0-type macrophages was found in the high-risk group in both METABRIC and TCGA cohorts, which may have an influence on the prognosis. Furthermore, we found that the low-risk group may be associated with the immune-related pathway and the high-risk group was with the cell cycle-related pathway, which also showed an impact on the prognosis. Conclusion: These seven immune-related gene risk signatures provided an effective method for prognostic stratification in ER (+) and/or PR (+) and HER2 (-) BC.

Nalidixic acid potentiates the antitumor activity in sorafenib-resistant hepatocellular carcinoma via the tumor immune microenvironment analysis.

Sorafenib resistance is often developed and impedes the benefits of clinical therapy in hepatocellular carcinoma (HCC) patients. However, the relationship between sorafenib resistance and tumor immune environment and adjuvant drugs for sorafenib-resistant HCC are not systemically identified. This study first analyzed the expression profiles of sorafenib-resistant HCC cells to explore immune cell infiltration levels and differentially expressed immune-related genes (DEIRGs). The prognostic value of DEIRGs was analyzed using Cox regression and Kaplan-Meier analysis based on The Cancer Genome Atlas. The primary immune cells infiltrated in sorafenib-resistant HCC mice were explored using flow cytometry (FCM). Finally, small-molecule drugs for sorafenib-resistant HCC treatment were screened and validated by experiments. The CIBERSORT algorithm and mice model showed that macrophages and neutrophils are highly infiltrated, while CD8+ T cells are downregulated in sorafenib-resistant HCC. Totally, 34 DEIRGs were obtained from sorafenib-resistant and control groups, which were highly enriched in immune-associated biological processes and pathways. NR6A1, CXCL5, C3, and TGFB1 were further identified as prognostic markers for HCC patients. Finally, nalidixic acid was identified as a promising antagonist for sorafenib-resistant HCC treatment. Collectively, our study reveals the tumor immune microenvironment changes and explores a promising adjuvant drug to overcome sorafenib resistance in HCC.

Comprehensive analysis of ceRNA networks to determine genes related to prognosis, overall survival, and immune infiltration in clear cell renal carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is one of the common subtypes of kidney cancer. Circular RNAs (circRNAs) act as competing endogenous RNAs (ceRNAs) to affect the expression of microRNAs (miRNAs), and hence the expression of genes involved in the development and progression of ccRCC. However, these interactions have not been sufficiently explored. METHODS: The differential expression of circRNAs (DEC) was extracted from the GEO database, and the expression of circRNAs was analyzed by the Limma R package. The interaction of miRNAs with circRNAs was predicted using (cancer-specific circRNA database) CSCD and circinteractome database. The genes affected by the miRNAs were predicted by miRwalk version 3, and the differential expression was retrieved using TCGA. Functional enrichment was assessed and a PPI network was created using DAVID and Cytoscape, respectively. The genes with significant interactions (hub-genes) were screened, and the total survival rate of ccRCC patients was extracted from the Gene Expression Profiling Interactive Analysis (GEPIA) database. To confirm the expression of OS genes we used the Immunohistochemistry (IHC) data and TCGA database. The correlation between gene expression and immune cell infiltration was investigated using TIMER2.0. Finally, potential drug candidates were predicted by the cMAP database. RESULTS: Four DECs (hsa_circ_0003340, hsa_circ_0007836, hsa_circ_0020303, and hsa_circ_0001873) were identified, along with 11 interacting miRNAs (miR-1224-3p, miR-1294, miR-1205, miR-1231, miR-615-5p, miR-940, miR-1283, and miR-1305). These miRNAs were predicted to affect 1282 target genes, and function enrichment was used to identify the genes involved in cancer biology. 18 hub-genes (CCR1, VCAM1, NCF2, LAPTM5, NCKAP1L, CTSS, BTK, LILRB2, CD53, MPEG1, C3AR1, GPR183, C1QA, C1QC, P2RY8, LY86, CYBB, and IKZF1) were identified from a PPI network. VCAM1, NCF2, CTSS, LILRB2, MPEG1, C3AR1, P2RY8, and CYBB could affect the survival of ccRCC patients. The hub-gene expression was correlated with tumor immune cell infiltration and patient prognosis. Two potantial drug candidates, naphazoline and lithocholic acid could play a role in ccRCC therapy, as well other cancers. CONCLUSION: This bioinformatics analysis brings a new insight into the role of circRNA/miRNA/mRNA interactions in ccRCC pathogenesis, prognosis, and possible drug treatment or immunotherapy.

Correlation of PD-L1 Expression with Tumor Mutation Burden and Gene Signatures for Prognosis in Early-Stage Squamous Cell Lung Carcinoma.

OBJECTIVES: Anti-programmed cell death 1 (PD-1)/programmed death ligand 1 (PD-L1) immunotherapy has demonstrated success in the treatment of advanced NSCLC. Recently, PD-1/PD-L1 blockade also has demonstrated interesting results in small trials of neoadjuvant treatment in stage IB to IIIA NSCLC. In addition, several clinical trials using anti-PD-1/PD-L1 immunotherapy as an adjuvant or neoadjuvant treatment in patients with resectable stage NSCLC are ongoing. However, few analyses of anti-PD-1/PD-L1 immunotherapy-related biomarkers in early-stage squamous cell lung carcinoma (SqCLC) have been reported. In this study, we evaluated PD-L1 protein expression, tumor mutation burden, and expression of an immune gene signature in early-stage SqCLC, providing data for identifying the potential role for patients with anti-PD-1/PD-L1 treatment in early-stage SqCLC. METHODS: A total of 255 specimens from patients with early-stage SqCLC were identified within participating centers of the Strategic Partnering to Evaluate Cancer Signatures program. PD-L1 protein expression by immunohistochemistry was evaluated by using the Dako PD-L1 22C3 pharmDx kit on the Dako Link 48 auto-stainer (Dako, Carpinteria, CA). Tumor mutation burden (TMB) was calculated on the basis of data from targeted genome sequencing. The T-effector and interferon gamma (IFN-γ) gene signature was determined from Affymetrix gene chip data (Affymetrix, Santa Clara, CA) from frozen specimens. RESULTS: The prevalence of PD-L1 expression was 9.8% at a tumor proportion score cutoff of at least 50%. PD-L1 mRNA and programmed cell death 1 ligand 2 mRNA positively correlated with PD-L1 protein expression on tumor cells (TCs) and tumor-infiltrating immune cells. PD-L1 protein expression on tumor-infiltrating immune cells was correlated with the T-effector and IFN-γ gene signature (p < 0.001), but not with TMB. For TCs, all of these biomarkers were independent of each other and neither PD-L1 protein expression, TMB, or T-effector and IFN-γ gene signatures were independently prognostic for patient outcomes. CONCLUSIONS: Evaluation of PD-L1 expression, TMB, and T-effector and IFN-γ gene signatures in the cohort with early-stage SqCLC found them to be independent of each other, and none was associated with overall survival. Our results also support the hypothesis that PD-L1 expression is regulated by an intrinsic mechanism on TCs and an adaptive mechanism on immune cells.

Two Distinct Subtypes Revealed in Blood Transcriptome of Breast Cancer Patients With an Unsupervised Analysis.

Background: Breast cancer (BC) is a highly heterogeneous cancer. The interaction between immune system and BC is complex, widespread yet unclear. In this study, we aimed to reveal the heterogeneity of host systemic immune response to BC and understand the possible mechanisms that may drive the heterogeneity using transcriptomic data from peripheral blood mononuclear cells (PBMCs). Methods: Transcriptome-wide gene expressions of PBMCs in 33 BC patients were generated by RNA sequencing. An unsupervised clustering algorithm was employed to discover PBMC transcriptome subtypes among BC patients. Association analysis between PBMC subtypes and age, clinical stage, abundance of immune cells, and other clinical factors was performed to understand the underlying biological processes that may drive this heterogeneity. Immune gene signature identification and in silico survival analysis were performed to investigate the potential clinical implications of these PBMC subtypes. The findings were validated using the whole blood transcriptomes of an independent cohort. Results: We observed that established BC subtypes were not associated with PBMC gene expression profiles. Instead, we discovered and validated two new BC subtypes using PBMC transcriptome, which have distinct immune cell proportions, especially for lymphocytes (P = 5.22 × 10-12) and neutrophils (P = 1.13 × 10-14). Enrichment analysis of differentially expressed genes revealed that these two subtypes had distinct patterns of immune responses, including osteoclast differentiation and interleukin-10 signaling pathway. We developed two immune gene signatures that can differentiate these two BC PBMC subtypes. Further analysis suggested they had the ability to predict the clinical outcome of BC patients. Conclusions: PBMC transcriptome profiles can classify BC patients into two distinct subtypes. These two subtypes are mainly shaped by different immune cell abundance, which may have implications on clinical outcomes.

Immune-Phenotyping and Transcriptomic Profiling of Peripheral Blood Mononuclear Cells From Patients With Breast Cancer: Identification of a 3 Gene Signature Which Predicts Relapse of Triple Negative Breast Cancer.

Background: Interactions between the immune system and tumors are highly reciprocal in nature, leading to speculation that tumor recurrence or therapeutic resistance could be influenced or predicted by immune events that manifest locally, but can be detected systemically. Methods: Multi-parameter flow cytometry was used to examine the percentage and phenotype of natural killer (NK) cells, myeloid-derived suppressor cells (MDSCs), monocyte subsets and regulatory T (Treg) cells in the peripheral blood of of 85 patients with breast cancer (50 of whom were assessed before and after one cycle of anthracycline-based chemotherapy), and 23 controls. Transcriptomic profiles of peripheral blood mononuclear cells (PBMCs) in 23 patients were generated using a NanoString gene profiling platform. Results: An increased percentage of immunosuppressive cells such as granulocytic MDSCs, intermediate CD14++CD16+ monocytes and CD127negCD25highFoxP3+ Treg cells was observed in patients with breast cancer, especially patients with stage 3 and 4 disease, regardless of ER status. Following neoadjuvant chemotherapy, B cell numbers decreased significantly, whereas monocyte numbers increased. Although chemotherapy had no effect on the percentage of Treg, MDSC and NK cells, the expression of inhibitory receptors CD85j, LIAR and NKG2A and activating receptors NKp30 and NKp44 on NK cells increased, concomitant with a decreased expression of NKp46 and DNAM-1 activating receptors. Transcriptomic profiling revealed a distinct group of 3 patients in the triple negative breast cancer (TNBC) cohort who expressed high levels of mRNA encoding genes predominantly involved in inflammation. The analysis of a large transcriptomic dataset derived from the tumors of patients with TNBC revealed that the expression of CD163, CXCR4, THBS1 predicted relapse-free survival. Conclusions: The peripheral blood immunome of patients with breast cancer is influenced by the presence and stage of cancer, but not by molecular subtypes. Furthermore, immune profiling coupled with transcriptomic analyses of peripheral blood cells may identify patients with TNBC that are at risk of relapse after chemotherapy.