Hepatic recruitment of myeloid-derived suppressor cells upon liver injury promotes both liver regeneration and fibrosis.

BACKGROUND: The liver regeneration is a highly complicated process depending on the close cooperations between the hepatocytes and non-parenchymal cells involving various inflammatory cells. Here, we explored the role of myeloid-derived suppressor cells (MDSCs) in the processes of liver regeneration and liver fibrosis after liver injury. METHODS: We established four liver injury models of mice including CCl4-induced liver injury model, bile duct ligation (BDL) model, concanavalin A (Con A)-induced hepatitis model, and lipopolysaccharide (LPS)-induced hepatitis model. The intrahepatic levels of MDSCs (CD11b+Gr-1+) after the liver injury were detected by flow cytometry. The effects of MDSCs on liver tissues were analyzed in the transwell co-culture system, in which the MDSCs cytokines including IL-10, VEGF, and TGF-ß were measured by ELISA assay and followed by being blocked with specific antibodies. RESULTS: The intrahepatic infiltrations of MDSCs with surface marker of CD11b+Gr-1+ remarkably increased after the establishment of four liver injury models. The blood served as the primary reservoir for hepatic recruitment of MDSCs during the liver injury, while the bone marrow appeared play a compensated role in increasing the number of MDSCs at the late stage of the inflammation. The recruited MDSCs in injured liver were mainly the M-MDSCs (CD11b+Ly6G-Ly6Chigh) featured by high expression levels of cytokines including IL-10, VEGF, and TGF-ß. Co-culture of the liver tissues with MDSCs significantly promoted the proliferation of both hepatocytes and hepatic stellate cells (HSCs). CONCLUSIONS: The dramatically and quickly infiltrated CD11b+Gr-1+ MDSCs in injured liver not only exerted pro-proliferative effects on hepatocytes, but also accounted for the activation of profibrotic HSCs.

Identification of lncRNAs associated with T cells as potential biomarkers and therapeutic targets in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common and deadliest subtype of lung cancer. To select more targeted and effective treatments for individuals, further advances in classifying LUAD are urgently needed. The number, type, and function of T cells in the tumor microenvironment (TME) determine the progression and treatment response of LUAD. Long noncoding RNAs (lncRNAs), may regulate T cell differentiation, development, and activation. Thus, our aim was to identify T cell-related lncRNAs (T cell-Lncs) in LUAD and to investigate whether T cell-Lncs could serve as potential stratifiers and therapeutic targets. Seven T cell-Lncs were identified to further establish the T cell-related lncRNA risk score (TRS) in LUAD. Low TRS individuals were characterized by robust immune status, fewer genomic alterations, and remarkably longer survival than high TRS individuals. The excellent accuracy of TRS in predicting overall survival (OS) was validated in the TCGA-LUAD training cohort and the GEO-LUAD validation cohort. Our data demonstrated the favorable predictive power of the TRS-based nomogram, which had important clinical significance in estimating the survival probability for individuals. In addition, individuals with low TRS could respond better to chemotherapy and immunotherapy than those with high TRS. LINC00525 was identified as a valuable study target, and the ability of LUAD to proliferate or invade was significantly attenuated by downregulation of LINC00525. In conclusion, the TRS established by T cell-Lncs could unambiguously classify LUAD patients, predict their prognosis and guide their management. Moreover, our identified T cell-Lncs could provide potential therapeutic targets for LUAD.

Identification pyroptosis-related gene signature to predict prognosis and associated regulation axis in colon cancer.

Background: Pyroptosis is an important component of the tumor microenvironment and associated with the occurrence and progression of cancer. As the expression of pyroptosis-related genes and its impact on the prognosis of colon cancer (CC) remains unclear, we constructed and validated a pyroptosis-related genes signature to predict the prognosis of patients with CC. Methods: Microarray datasets and the follow-up clinical information of CC patients were obtained from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. Candidate genes were screened out for further analysis. Various methods were combined to construct a robust pyroptosis-related genes signature for predicting the prognosis of patients with CC. Based on the gene signature and clinical features, a decision tree and nomogram were developed to improve risk stratification and quantify risk assessment for individual patients. Results: The pyroptosis-related genes signature successfully discriminated CC patients with high-risk in the training cohorts. The prognostic value of this signature was further confirmed in independent validation cohort. Multivariable Cox regression and stratified survival analysis revealed this signature was an independent prognostic factor for CC patients. The decision tree identified risk subgroups powerfully, and the nomogram incorporating the gene signature and clinical risk factors performed well in the calibration plots. Conclusion: Pyroptosis-related genes signature was an independent prognostic factor, and can be used to predict the prognosis of patients with CC.

A Novel Artificial Neural Network Prognostic Model Based on a Cancer-Associated Fibroblast Activation Score System in Hepatocellular Carcinoma.

Introduction: Hepatocellular carcinoma (HCC) ranks fourth as the most common cause of cancer-related death. It is vital to identify the mechanism of progression and predict the prognosis for patients with HCC. Previous studies have found that cancer-associated fibroblasts (CAFs) promote tumor proliferation and immune exclusion. However, the information about CAF-related genes is still elusive. Methods: The data were obtained from The Cancer Genome Atlas, International Cancer Genome Consortium, and Gene Expression Omnibus databases. On the basis of single-cell transcriptome and ligand-receptor interaction analysis, CAF-related genes were selected. By performing Cox regression and random forest, we filtered 12 CAF-related prognostic genes for the construction of the ANN model based on the CAF activation score (CAS). Then, functional, immune, mutational, and clinical analyses were performed. Results: We constructed a novel ANN prognostic model based on 12 CAF-related prognostic genes. Cancer-related pathways were enriched, and higher activated cell crosstalk was identified in high-CAS samples. High immune activity was observed in high-CAS samples. We detected three differentially mutated genes (NBEA, RYR2, and FRAS1) between high- and low-CAS samples. In clinical analyses, we constructed a nomogram to predict the prognosis of patients with HCC. 5-Fluorouracil had higher sensitivity in high-CAS samples than in low-CAS samples. Moreover, some small-molecule drugs and the immune response were predicted. Conclusion: We constructed a novel ANN model based on CAF-related genes. We revealed information about the ANN model through functional, mutational, immune, and clinical analyses.

Identifying tumor antigens and immuno-subtyping in colon adenocarcinoma to facilitate the development of mRNA vaccine.

The mRNA vaccine has provided a promising approach for cancer immunotherapies. However, only a few mRNA vaccines have been developed against colon adenocarcinoma (COAD). Screening potential targets for mRNA vaccines from numerous candidates is a substantial challenge. Considering the tumor heterogeneity, only a subset of patients might respond to vaccinations. This study was conducted to identify potential candidates for mRNA vaccines, and distinguish appropriate subgroups of COAD patients for vaccination. A total of five tumor antigens with prognostic values were identified, including IGF2BP3, DPCR1, HOXD10, TRIM7, and ZIC5. The COAD patients were stratified into five immune subtypes (IS1-IS5), according to consensus clustering analysis. Higher tumor mutation burden (TMB) was observed in IS1 and IS5 subtypes. The IS1 and IS5 subtypes have shown the baseline of immune-hot tumor microenvironment, while other subtypes displayed immune desert phenotype. Distinct expressions of immune checkpoints (ICPs)-related genes and immunogenic cell death (ICD) modulators were observed among five immune subtypes. Finally, the immune landscape was conducted to narrow the immune components for better personalized mRNA-based vaccination. The IFIT3, PARP9, TAP1, STAT1, and OAS2 were confirmed as hub genes, and COAD patients with higher expressions of these genes might be more appropriate for mRNA vaccination. In conclusion, the IGF2BP3, DPCR1, HOXD10, TRIM7, and ZIC5 were identified as potential candidates for developing mRNA vaccines against COAD, and patients in IS1 and IS5 subtypes might respond better to mRNA vaccination.

Integratively Genomic Analysis Reveals the Prognostic and Immunological Characteristics of Pyroptosis and Ferroptosis in Pancreatic Cancer for Precision Immunotherapy.

The non-apoptotic cell death processes including pyroptosis and ferroptosis have been implicated in the progression and therapeutic responses of pancreatic adenocarcinoma (PAAD). However, the extent to which pyroptosis and ferroptosis influence tumor biology remains ambiguous, especially in PAAD, which is characterized with "cold" immunity. Considering the heterogeneity among different patients, it was more practical to quantify distinct cell death profiles in an individual tumor sample. Herein, we developed a pyroptosis-ferroptosis (P-F) score for PAAD patients in The Cancer Genome Atlas (TCGA) database. A high P-F score was associated with active immune phenotype, decreased genomic alterations, and significantly longer survival. Good accuracy of the P-F score in predicting overall survival (OS) was further confirmed in the TCGA-PAAD, ICGC-PACA-CA, and E-MTAB-6134 cohorts. Besides, one immunotherapy cohort (IMvigor210 dataset) has verified that patients with high P-F scores exhibited significant advantages in therapeutic responses and clinical benefits. The sensitivity to chemotherapeutics was analyzed through the Genomics of Drug Sensitivity in Cancer (GDSC), and patients with low P-F score might be more sensitive to paclitaxel and 5-fluorouracil. Collectively, the P-F score based on the systematic evaluation of cell death profiles could serve as an effective biomarker in predicting the outcomes and responses of PAAD patients to treatments with chemotherapeutic agents or immunotherapies.

Potential Immune Biomarker Candidates and Immune Subtypes of Lung Adenocarcinoma for Developing mRNA Vaccines.

mRNA vaccines against cancer have advantages in safety, improved therapeutic efficacy, and large-scale production. Therefore, our purpose is to identify immune biomarkers and to analyze immune status for developing mRNA vaccines and selecting appropriate patients for vaccination. We downloaded clinical information and RNA-seq data of 494 LUAD patients from TCGA. LUAD mutational information was hierarchically clustered by NMF package (Version 0.23.0). DeconstructSigs package (Version 1.8.0) and NMF consistency clustering were used to identify mutation signatures. Maftools package (Version 2.6.05) was used to select LUAD-related immune biomarkers. TIMER was used to discuss the correlation between genetic mutations and cellular components. Unsupervised clustering Pam method was used to identify LUAD immune subtypes. Log-rank test and univariate/multivariate cox regression were used to predict the prognosis of immune subtypes. Dimensionality reduction analysis was dedicated to the description of LUAD immune landscape. LUAD patients are classified into four signatures: T >C, APOBEC mutation, age, and tobacco. Then, GPRIN1, MYRF, PLXNB2, SLC9A4, TRIM29, UBA6, and XDH are potential LUAD-related immune biomarker candidates to activate the immune response. Next, we clustered five LUAD-related immune subtypes (IS1-IS5) by prognostic prediction. IS3 showed prolonged survival. The reliability of our five immune subtypes was validated by Thorsson's results. IS2 and IS4 patients had high tumor mutation burden and large number of somatic mutations. Besides, we identified that immune subtypes of cold immunity (patients with IS2 and IS4) are ideal mRNA vaccination recipients. Finally, LUAD immune landscape revealed immune cells and prognostic conditions, which provides important information to select patients for vaccination. GPRIN1, MYRF, PLXNB2, SLC9A4, TRIM29, UBA6, and XDH are potential LUAD-related immune biomarker candidates to activate the immune response. Patients with IS2 and IS4 might potentially be immunization-sensitive patients for vaccination.

The identification of gene signatures in patients with extranodal NK/T-cell lymphoma from a pair of twins.

BACKGROUND: There is no unified treatment standard for patients with extranodal NK/T-cell lymphoma (ENKTL). Cancer neoantigens are the result of somatic mutations and cancer-specific. Increased number of somatic mutations are associated with anti-cancer effects. Screening out ENKTL-specific neoantigens on the surface of cancer cells relies on the understanding of ENKTL mutation patterns. Hence, it is imperative to identify ENKTL-specific genes for ENKTL diagnosis, the discovery of tumor-specific neoantigens and the development of novel therapeutic strategies. We investigated the gene signatures of ENKTL patients. METHODS: We collected the peripheral blood of a pair of twins for sequencing to identify unique variant genes. One of the twins is diagnosed with ENKTL. Seventy samples were analyzed by Robust Multi-array Analysis (RMA). Two methods (elastic net and Support Vector Machine-Recursive Feature Elimination) were used to select unique genes. Next, we performed functional enrichment analysis and pathway enrichment analysis. Then, we conducted single-sample gene set enrichment analysis of immune infiltration and validated the expression of the screened markers with limma packages. RESULTS: We screened out 126 unique variant genes. Among them, 11 unique genes were selected by the combination of elastic net and Support Vector Machine-Recursive Feature Elimination. Subsequently, GO and KEGG analysis indicated the biological function of identified unique genes. GSEA indicated five immunity-related pathways with high signature scores. In patients with ENKTL and the group with high signature scores, a proportion of functional immune cells are all of great infiltration. We finally found that CDC27, ZNF141, FCGR2C and NES were four significantly differential genes in ENKTL patients. ZNF141, FCGR2C and NES were upregulated in patients with ENKTL, while CDC27 was significantly downregulated. CONCLUSION: We identified four ENKTL markers (ZNF141, FCGR2C, NES and CDC27) in patients with extranodal NK/T-cell lymphoma.

Identifying the hub genes in non-small cell lung cancer by integrated bioinformatics methods and analyzing the prognostic values.

BACKGROUND: Lung cancer, a malignant tumor, has the highest mortality and second most common morbidity worldwide. Non-small cell lung cancer (NSCLC) is the most common pathological subtype of lung cancer. This study aimed to identify the gene signature associated with the NSCLC prognosis using bioinformatics analysis. MATERIALS AND METHODS: The dataset GSE103512 was utilized to construct co-expression networks using weighted gene co-expression network analysis (WGCNA). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed using Database for Annotation, Visualization, and Integrated Discovery. Gene set enrichment analysis was conducted to ascertain the function of the hub genes more accurately. The relationship between the hub genes and immune infiltration was investigated using a single sample gene set enrichment analysis. Hub genes were screened and validated by other datasets and online websites. RESULTS: The results of WGCNA demonstrated that the blue module was most significantly related to tumor progression in NSCLC. Functional enrichment analysis showed that the blue module was associated with DNA replication, cell division, mitotic nuclear division, and cell cycle. A total of five hub genes (RFC5, UBE2S, CHAF1A, FANCI, and TMEM194A) were chosen to be identified and validated at transcriptional and translational levels. Receiver operating characteristic curve verified that the mRNA levels of these five genes can excellently discriminate between normal and tumor tissues. Survival analysis was also performed. Additionally, the protein levels of these five genes were also significantly different between tumor and normal tissues. Immune infiltration analysis showed that the expression levels of the hub genes had a negative correlation with the infiltration levels of many cells related to innate immune response, antigen-presenting process, humoral immune response, or T cell-mediated immune responses. CONCLUSIONS: We identified five hub genes associated with the NSCLC tumorigenesis. NSCLC patients with higher expressions of each hub gene had a worse prognosis than those with lower expressions. Moreover, the hub genes might serve as biomarkers and therapeutic targets for precise diagnosis, target therapy, and immunotherapy of NSCLC in the future.