Immunophenotypic Landscape and Prognosis-Related mRNA Signature in Diffuse Large B Cell Lymphoma

Diffuse large B cell lymphoma (DLBCL) exhibits a tightly complexity immune landscape. In this study, we intended to identify different immune phenotype and to examine the immune related mRNA signature for clinical characteristic, therapeutic responsiveness as well as risk stratification and survival prediction in DLBCL. We identified two immune infiltration subtypes of DLBCL patients based on 28 immune cell types. GSEA analysis uncovered the concordant classification of two robust significant subtypes of DLBCL. Considering the convenient application of the immune infiltration subtypes for prognostic prediction, we developed a risk score based on the differentially expressed genes between the Immunity-H and Immunity-L groups. By a least absolute shrinkage and selection operator (LASSO)-Cox regression model, a sixteen-gene risk signature, comprising ANTXR1, CD3D, TIMP1, FPR3, NID2, CTLA4, LPAR6, GPR183, LYZ, PTGDS, ITK, FBN1, FRMD6, PLAU, MICAL2, C1S, was established. The comprehensive results showed that the high-risk group was correlated with lower immune infiltration, more aggressive phenotypes, lower overall survival and more sensitive to lenalidomide. In contrast, a low-risk group score was associated with higher immune infiltration, less aggressive phenotypes, better overall survival and more likely to benefit from PD-1/PD-L1 inhibitors. Finally, a nomogram comprised of the risk score and IPI score was verified to more accurately predict the overall survival of DLBCL than traditional clinical prediction models. Altogether, our data demonstrate the heterogeneity of immune patterns within DLBCL and deepen our molecular understanding of this tumor entity.

Immunogenomic landscape analyses of immune molecule signature-based risk panel for patients with triple-negative breast cancer

Triple-negative breast cancer (TNBC) presented as high heterogeneous immunogenicity that lacks useful clinical signatures to risk-stratify immune-benefit subtypes. We hypothesized that molecular-based phenotypic characterization of TNBC tumors and their immunity may overcome these challenges. We enrolled 1,145 patients with TNBC for analysis. Through combining algorithm integration analysis and TNBC datasets, a tumor immune risk score (TIRS) panel consisting of 8 potential biomarkers was identified. The TIRS panel represented excellent effectiveness as an independent predictor. High- and low risk stratification of patients was further achieved by TIRS, and significant survival and immune-infiltration pattern differences were found in each cohort, both at the transcriptome and protein levels. Non-negative matrix factorization clustering further identified four different tumor immune microenvironment types (TIMTs), among which TIMT-II was associated with the best prognosis and immune status, whereas TIMT-IV had the opposite effect, TIMT-III was associated with highly unstable genomes, and TIMT-I displayed stem-cell-related characteristics along with high stromal scores and may have extensive enrichment of tumor-associated fibroblasts and vascular cells. In conclusion, our TIRS panel could serve as a robust prognostic signature and provide therapeutic benefits for immunotherapy. Additionally, coordinating four TIMTs may be helpful for clinical decision-making in TNBC patients.

Immune Infiltration Subtypes Characterization and Identification of Prognosis-Related lncRNAs in Adenocarcinoma of the Esophagogastric Junction

The incidence of adenocarcinoma of the esophagogastric junction (AEG) has markedly increased worldwide. However, the precise etiology of AEG is still unclear, and the therapeutic options thus remain limited. Growing evidence has implicated long non-coding RNAs (lncRNAs) in cancer immunomodulation. This study aimed to examine the tumor immune infiltration status and assess the prognostic value of immune-related lncRNAs in AEG. Using the ESTIMATE method and single-sample GSEA, we first evaluated the infiltration level of 28 immune cell types in AEG samples obtained from the TCGA dataset (N=201). Patients were assigned into high- and low-immune infiltration subtypes based on the immune cell infiltration’s enrichment score. GSEA and mutation pattern analysis revealed that these two immune infiltration subtypes had distinct phenotypes. We identified 1470 differentially expressed lncRNAs in two immune infiltration subtypes. From these differentially expressed lncRNAs, six prognosis-related lncRNAs were selected using the Cox regression analysis. Subsequently, an immune risk signature was constructed based on combining the values of the six prognosis-associated lncRNAs expression levels and multiple regression coefficients. To determine the risk model’s prognostic capability, we performed a series of survival analyses with Kaplan–Meier methods, Cox proportional hazards regression models, and the area under receiver operating characteristic (ROC) curve. The results indicated that the immune-related risk signature could be an independent prognostic factor with a significant predictive value in patients with AEG. Furthermore, the immune-related risk signature can effectively predict the response to immunotherapy and chemotherapy in AEG patients. In conclusion, the proposed immune-related lncRNA prognostic signature is reliable and has high survival predictive value for patients with AEG and is a promising potential biomarker for immunotherapy.