Immunological Classification of Pancreatic Carcinomas to Identify Immune Index and Provide a Strategy for Patient Stratification

BackgroundCancer immunotherapy has produced significant positive clinical effects in a variety of tumor types. However, pancreatic ductal adenocarcinoma (PDAC) is widely considered to be a “cold” cancer with poor immunogenicity. Our aim is to determine the detailed immune features of PDAC to seek new treatment strategies.MethodsThe immune cell abundance of PDAC patients was evaluated with the single-sample gene set enrichment analysis (ssGSEA) using 119 immune gene signatures. Based on these data, patients were classified into different immune subtypes (ISs) according to immune gene signatures. We analyzed their response patterns to immunotherapy in the datasets, then established an immune index to reflect the different degrees of immune infiltration through linear discriminant analysis (LDA). Finally, potential prognostic markers associated with the immune index were identified based on weighted correlation network analysis (WGCNA) that was functionally validated in vitro.ResultsThree ISs were identified in PDAC, of which IS3 had the best prognosis across all three cohorts. The different expressions of immune profiles among the three ISs indicated a distinct responsiveness to immunotherapies in PDAC subtypes. By calculating the immune index, we found that the IS3 represented higher immune infiltration, while IS1 represented lower immune infiltration. Among the investigated signatures, we identified ZNF185, FANCG, and CSTF2 as risk factors associated with immune index that could potentially facilitate diagnosis and could be therapeutic target markers in PDAC patients.ConclusionsOur findings identified immunologic subtypes of PDAC with distinct prognostic implications, which allowed us to establish an immune index to represent the immune infiltration in each subtype. These results show the importance of continuing investigation of immunotherapy and will allow clinical workers to personalized treatment more effectively in PDAC patients.

Pan-Cancer Analysis Shows Enrichment of Macrophages, Overexpression of Checkpoint Molecules, Inhibitory Cytokines, and Immune Exhaustion Signatures in EMT-High Tumors

Epithelial–mesenchymal transition (EMT) is a highly dynamic process that occurs under normal circumstances; however, EMT is also known to play a central role in tumor progression and metastasis. Furthermore, role of tumor immune microenvironment (TIME) in shaping anticancer immunity and inducing the EMT is also well recognized. Understanding the key features of EMT is critical for the development of effective therapeutic interventions. Given the central role of EMT in immune escape and cancer progression and treatment, we have carried out a pan-cancer TIME analysis of The Cancer Genome Atlas (TCGA) dataset in context to EMT. We have analyzed infiltration of various immune cells, expression of multiple checkpoint molecules and cytokines, and inflammatory and immune exhaustion gene signatures in 22 cancer types from TCGA dataset. A total of 16 cancer types showed a significantly increased (p < 0.001) infiltration of macrophages in EMT-high tumors (mesenchymal samples). Furthermore, out of the 17 checkpoint molecules we analyzed, 11 showed a significant overexpression (p < 0.001) in EMT-high samples of at least 10 cancer types. Analysis of cytokines showed significant enrichment of immunosuppressive cytokines—TGFB1 and IL10—in the EMT-high group of almost all cancer types. Analysis of various gene signatures showed enrichment of inflammation, exhausted CD8+ T cells, and activated stroma signatures in EMT-high tumors. In summary, our pan-cancer EMT analysis of TCGA dataset shows that the TIME of EMT-high tumors is highly immunosuppressive compared to the EMT-low (epithelial) tumors. The distinctive features of EMT-high tumors are as follows: (i) the enrichment of tumor-associated macrophages, (ii) overexpression of immune checkpoint molecules, (iii) upregulation of immune inhibitory cytokines TGFB1 and IL10, and (iv) enrichment of inflammatory and exhausted CD8+ T-cell signatures. Our study shows that TIMEs of different EMT groups differ significantly, and this would pave the way for future studies analyzing and targeting the TIME regulators for anticancer immunotherapy.

General Features and Novel Gene Signatures That Identify Epstein-Barr Virus-Associated Epithelial Cancers

Epstein-Barr virus (EBV) is associated with various types of human malignancies, including nasopharyngeal carcinoma (NPC), EBV-associated gastric carcinoma (EBVaGC), and oral squamous cell carcinoma (OSCC). The present study aimed to identify gene signatures and common signaling pathways that can be used to predict the prognosis of EBV-associated epithelial cancers (EBVaCAs) by performing an integrated bioinformatics analysis of cell lines and tumor tissues. We identified 12 differentially expressed genes (DEGs) in the EBVaCA cell lines. Among them, only four DEGs, including BAMBI, SLC26A9, SGPP2, and TMC8, were significantly upregulated. However, SLC26A9 and TMC8, but not BAMBI and SGPP2, were significantly upregulated in EBV-positive tumor tissues compared to EBV-negative tumor tissues. Next, we identified IL6/JAK/STAT3 and TNF-α/NF-κB signaling pathways as common hallmarks of EBVaCAs. The expression of key genes related to the two hallmarks was upregulated in both EBV-infected cell lines and EBV-positive tumor tissues. These results suggest that SLC26A9 and TMC8 might be gene signatures that can effectively predict the prognosis of EBVaCAs and provide new insights into the molecular mechanisms of EBV-driven epithelial cancers.

Novel Insights Into Gene Signatures and Their Correlation With Immune Infiltration of Peripheral Blood Mononuclear Cells in Behcet’s Disease

BackgroundBehcet’s disease (BD) is a chronic inflammatory disease that involves systemic vasculitis and mainly manifests as oral and genital ulcers, uveitis, and skin damage as the first clinical symptoms, leading to gastrointestinal, aortic, or even neural deterioration. There is an urgent need for effective gene signatures for BD’s early diagnosis and elucidation of its underlying etiology.MethodsWe identified 82 differentially expressed genes (DEGs) in BD cases compared with healthy controls (HC) after combining two Gene Expression Omnibus datasets. We performed pathway analyses on these DEGs and constructed a gene co-expression network and its correlation with clinical traits. Hub genes were identified using a protein–protein interaction network. We manually selected CCL4 as a central hub gene, and gene-set enrichment and immune cell subset analyses were applied on patients in high- and low-CCL4 expression groups. Meanwhile, we validated the diagnostic value of hub genes in differentiating BD patients from HC in peripheral blood mononuclear cells using real-time PCR.ResultsTwelve hub genes were identified, and we validated the upregulation of CCL4 and the downregulation of NPY2R mRNA expression. Higher expression of CCL4 was accompanied by larger fractions of CD8 + T cells, natural killer cells, M1 macrophages, and activated mast cells. Receiver operator characteristic curves showed good discrimination between cases and controls based on the expression of these genes.ConclusionCCL4 and NPY2R could be diagnostic biomarkers for BD that reveal inflammatory status and predict vascular involvement in BD, respectively.

Lung Adenocarcinoma Transcriptomic Analysis Predicts Adenylate Kinase Signatures Contributing to Tumor Progression and Negative Patient Prognosis

The ability to detect and respond to hypoxia within a developing tumor appears to be a common feature amongst most cancers. This hypoxic response has many molecular drivers, but none as widely studied as Hypoxia-Inducible Factor 1 (HIF-1). Recent evidence suggests that HIF-1 biology within lung adenocarcinoma (LUAD) may be associated with expression levels of adenylate kinases (AKs). Using LUAD patient transcriptome data, we sought to characterize AK gene signatures related to lung cancer hallmarks, such as hypoxia and metabolic reprogramming, to identify conserved biological themes across LUAD tumor progression. Transcriptomic analysis revealed perturbation of HIF-1 targets to correlate with altered expression of most AKs, with AK4 having the strongest correlation. Enrichment analysis of LUAD tumor AK4 gene signatures predicts signatures involved in pyrimidine, and by extension, nucleotide metabolism across all LUAD tumor stages. To further discriminate potential drivers of LUAD tumor progression within AK4 gene signatures, partial least squares discriminant analysis was used at LUAD stage-stage interfaces, identifying candidate genes that may promote LUAD tumor growth or regression. Collectively, these results characterize regulatory gene networks associated with the expression of all nine human AKs that may contribute to underlying metabolic perturbations within LUAD and reveal potential mechanistic insight into the complementary role of AK4 in LUAD tumor development.

COVID-19 lung disease shares driver AT2 cytopathic features with Idiopathic pulmonary fibrosis

Background: In the aftermath of Covid-19, a long-haul form of mysterious and progressive fibrotic lung disease has emerged, i.e., post-COVID-19 lung disease (PCLD), for which we currently lack insights into pathogenesis, disease models, or treatment options. Method: Using an AI-guided approach, we analyzed > 1000 human lung transcriptomic datasets associated with various lung conditions using two viral pandemic (ViP and sViP) and one covid lung gene signatures. Upon identifying similarities between COVID-19 and idiopathic pulmonary fibrosis (IPF), we subsequently dissected the basis for such similarity from molecular, cytopathic, and immunologic perspectives using a panel of IPF-specific gene signatures, alongside signatures of alveolar type II (AT2) cytopathies and of prognostic monocyte-driven processes that are known drivers of IPF. To pinpoint the AT2 processes that are shared points of convergence between COVID-19 and IPF, transcriptome-derived findings were used to construct protein-protein interaction (PPI) network. Key findings were validated in hamster and human adult lung organoid (ALO) pre-clinical models of COVID-19 using immunohistochemistry and qPCR. Findings: We found that COVID-19 resembles IPF at a fundamental level; it recapitulates the gene expression patterns (ViP and IPF signatures), cytokine storm (IL15-centric) and the AT2 cytopathic changes, e.g., injury, DNA damage, arrest in a transient, damage-induced progenitor state, and senescence-associated secretory phenotype (SASP). These immunocytopathic features were induced in pre-clinical COVID models (ALO and hamster) and reversed with effective anti-CoV-2 therapeutics in hamsters. PPI-network analyses pinpointed ER stress as one of the shared early triggers of both diseases, and IHC studies validated the same in the lungs of deceased subjects with COVID-19 and SARS-CoV-2-challenged hamster lungs. Lungs from tg-mice, in which ER stress is induced specifically in the AT2 cells, faithfully recapitulate the host immune response and alveolar cytopathic changes that are induced by SARS-CoV-2. Interpretation: Like IPF, COVID-19 may be driven by injury-induced ER stress that culminates into progenitor state arrest and SASP in AT2 cells. The ViP signatures in monocytes may be key determinants of prognosis. The insights, signatures, disease models identified here are likely to spur the development of therapies for patients with IPF and other fibrotic interstitial lung disease.