T cell-mediated tumor killing sensitivity gene signature-based prognostic score for acute myeloid leukemia.

BACKGROUND AND OBJECTIVE: Acute myeloid leukemia (AML) is an aggressive, heterogenous hematopoetic malignancies with poor long-term prognosis. T-cell mediated tumor killing plays a key role in tumor immunity. Here, we explored the prognostic performance and functional significance of a T-cell mediated tumor killing sensitivity gene (GSTTK)-based prognostic score (TTKPI). METHODS: Publicly available transcriptomic data for AML were obtained from TCGA and NCBI-GEO. GSTTK were identified from the TISIDB database. Signature GSTTK for AML were identified by differential expression analysis, COX proportional hazards and LASSO regression analysis and a comprehensive TTKPI score was constructed. Prognostic performance of the TTKPI was examined using Kaplan-Meier survival analysis, Receiver operating curves, and nomogram analysis. Association of TTKPI with clinical phenotypes, tumor immune cell infiltration patterns, checkpoint expression patterns were analysed. Drug docking was used to identify important candidate drugs based on the TTKPI-component genes. RESULTS: From 401 differentially expressed GSTTK in AML, 24 genes were identified as signature genes and used to construct the TTKPI score. High-TTKPI risk score predicted worse survival and good prognostic accuracy with AUC values ranging from 75 to 96%. Higher TTKPI scores were associated with older age and cancer stage, which showed improved prognostic performance when combined with TTKPI. High TTKPI was associated with lower naïve CD4 T cell and follicular helper T cell infiltrates and higher M2 macrophages/monocyte infiltration. Distinct patterns of immune checkpoint expression corresponded with TTKPI score groups. Three agents; DB11791 (Capmatinib), DB12886 (GSK-1521498) and DB14773 (Lifirafenib) were identified as candidates for AML. CONCLUSION: A T-cell mediated killing sensitivity gene-based prognostic score TTKPI showed good accuracy in predicting survival in AML. TTKPI corresponded to functional and immunological features of the tumor microenvironment including checkpoint expression patterns and should be investigated for precision medicine approaches.

A comprehensive prognostic score for head and neck squamous cancer driver genes and phenotype traits.

BACKGROUND: Head and neck squamous cancer (HNSCC) presents variable phenotype and progression features. Clinically applicable, high-accuracy multifactorial prognostic models for HNSCC survival outcomes are warranted and an active area of research. This study aimed to construct a comprehensive prognostic tool for HNSCC overall survival by integrating cancer driver genes with tumor clinical and phenotype information. METHODS: Key overall survival-related cancer driver genes were screened from among main effector and reciprocal gene pairs using TCGA data using univariate Cox proportional hazard regression analysis. Independent validation was performed using the GSE41613 dataset. The main effector genes among these were selected using LASSO regression and transcriptome score modeling was performed using multivariate Cox regression followed by validation analysis of the prognostic score. Next, multivariate Cox regression analysis was performed using the transcriptome score combined with age, grade, gender, and stage. An 'Accurate Prediction Model of HNSCC Overall Survival Score' (APMHO) was computed and validated. Enriched functional pathways, gene mutational landscape, immune cell infiltration, and immunotherapy sensitivity markers associated with high and low APMHO scores were analyzed. RESULTS: Screening 107 overall survival-related cancer genes and 402 interacting gene pairs, 6 genes: CRLF2, HSP90AA1, MAP2K1, PAFAH1B2, MYCL and SET genes, were identified and a transcriptional score was obtained. Age, stage and transcriptional score were found to be significant predictors in Cox regression analysis and used to construct a final APMHO model showing an AUC > 0.65 and validated. Transcriptional score, age, pathologic_N, pathologic_T, stage, and TCGA_subtype were significantly different in distribution between high and low APMHO groups. High APMHO samples showed significantly higher mutation rate, enriched tumor-related pathways including Hypoxia, unfold_protein_response, Glycolysis, and mTORC1 signaling, along with differences in immune cell infiltration and immune checkpoint, interferon-γ pathway and m6A regulator expression patterns. CONCLUSION: The APMHO score combining transcriptional and clinical variables showed good prognostic ability for HNSCC overall survival outcomes and was associated with different patterns of phenotypical features, immune and mutational landscape, and immunotherapy sensitivity marker expression. Future studies should validate this score in independent clinical cohorts.

N6-methyladenosine methylation regulatory pattern of pulmonary lymphoepithelioma-like carcinoma based on exosomal transcriptome analysis.

Pulmonary lymphoepithelioma-like carcinoma (pLELC) is a rare malignancy that lacks specific biomarkers. N6-methyladenosine (m6 A) is the most widespread internal modification of messenger RNA (mRNA), and its dysregulation is involved in the development of many cancers. However, the expression of m6 A genes in pLELC and their roles are unknown. We obtained an exosomal transcriptome data set of patients diagnosed with pLELC and healthy controls using RNA sequencing and identified differentially expressed genes (DEGs) in the two groups using R software. The differential expression of the 37 m6 A genes in the two sets of samples was further analyzed, and receiver operating characteristic (ROC) curves were plotted for each gene to identify their grouping ability. The STRING database was used to construct a protein-protein interaction network for m6 A genes. An mRNA-miRNA regulatory network of m6 A-related DEGs was constructed using the miRNet database, and a prediction score formula was established. A nomogram was constructed based on the candidate m6 A genes and prediction scores. The expression of key genes was determined through the immunohistochemical (IHC) staining of clinical tissue sections. Using ROC curves, nine m6 A genes were revealed to have classification efficacy in both groups of samples. We screened seven m6 A-related DEGs (MAN2C1, HNRNPCL1, FUS, EIF6, DIP2A, COA3, and BUD13) that were beneficial for grouping and constructed nomogram models. Through IHC, we identified FUS and EIF6 as being possibly involved in the occurrence and development of pLELC. The m6 A gene expression patterns in pLELC-derived exosomes were significantly different from those in healthy controls. We screened several key genes to facilitate the development of diagnostic markers for pulmonary lymphoepithelioma.

AGRN promotes lung adenocarcinoma progression by activating Notch signaling pathway and acts as a therapeutic target.

Lung cancer is the main reason for cancer-associated death globally, and lung adenocarcinoma (LUAD) is the most prevalent subtype of lung cancer. Recently, AGRN is considered playing an vital role in the development of some cancers. However, the regulatory effects and mechanisms of AGRN in LUAD remain elusive. In this study, we clarified the significant upregulation of AGRN expression in LUAD by single-cell RNA sequencing combined with immunohistochemistry. Besides, we confirmed that LUAD patients with high AGRN expression are more susceptible to lymph node metastases and have a worse prognosis by a retrospective study of 120 LUAD patients. Next, we demonstrated that AGRN directly interact with NOTCH1, which results in the release of the intracellular structural domain of NOTCH1 and the subsequent activation of the NOTCH pathway. Moreover, we also found that AGRN promotes proliferation, migration, invasion, EMT and tumorigenesis of LUAD cells in vitro and in vivo, and that these effects are reversed by blocking the NOTCH pathway. Furthermore, we prepared several antibodies targeting AGRN, and clarify that Anti-AGRN antibody treatment could significantly inhibit proliferation and promote apoptosis of tumor cells. Our study highlights the important role and regulatory mechanism of AGRN in LUAD development and progression, and suggests that antibodies targeting AGRN have therapeutic potential for LUAD. We also provide theoretical and experimental evidence for further development of monoclonal antibodies targeting AGRN.

Knockdown of SMAD3 inhibits the growth and enhances the radiosensitivity of lung adenocarcinoma via p21 in vitro and in vivo.

Radiotherapy is an effective approach for the treatment of lung adenocarcinoma. However, evidence suggests that lung adenocarcinoma can easily develop tolerance to radiotherapy. The purpose of this study was to investigate the effect and mechanism of SMAD3 on the radiosensitivity of lung adenocarcinoma in vitro and in vivo. We found that knockdown of SMAD3 using two short hairpin RNAs in lentivirus vectors significantly inhibited cell growth and increased radiosensitivity of the lung adenocarcinoma cell lines A549, H1299, and H1975. Using RNA sequencing and bioinformatics analyses, we found that the significantly differentially expressed genes in SMAD3 knockdown cells were mainly enriched in the cell cycle process. We then showed that knockdown of SMAD3 significantly reduced expression of cyclin-dependent kinase inhibitor 1 (p21) and increased the proportion of G2/M phase cells and the radiosensitivity of lung adenocarcinoma. Chromatin immunoprecipitation results in the Gene Expression Omnibus (GEO) database and our luciferase assay verified that SMAD3 directly bound the p21 promoter. A series of rescue experiments showed that overexpression of p21 partly reversed the effect of SMAD3 on proliferation and radioresistance in vitro and in vivo. Moreover, we found that the expression levels of SMAD3 and p21 were highly correlated, and both correlated with the patients' survival in online databases and clinical specimens. Expression of SMAD3 and p21 was also significantly different between radioresistant and radiosensitive patients in our hospital. Our results indicate that SMAD3 is a potential prognosis and radiosensitivity indicator as well as a target for radiotherapy and other treatments of patients with lung adenocarcinoma.