PTTG1 knockdown enhances radiation-induced antitumour immunity in lung adenocarcinoma

Integrative analysis of single-cell and bulk RNA-sequencing data revealed T cell marker genes based molecular sub-types and a prognostic signature in lung adenocarcinoma

Immunotherapy has emerged as a promising modality for addressing advanced or conventionally drug-resistant malignancies. When it comes to lung adenocarcinoma (LUAD), T cells have demonstrated significant influence on both antitumor activity and the tumor microenvironment. However, their specific contributions remain largely unexplored. This investigation aimed to delineate molecular subtypes and prognostic indicators founded on T cell marker genes, thereby shedding light on the significance of T cells in LUAD prognosis and precision treatment. The cellular phenotypes were identified by scrutinizing the single-cell data obtained from the GEO repository. Subsequently, T cell marker genes derived from single-cell sequencing analyses were integrated with differentially expressed genes from the TCGA repository to pinpoint T cell-associated genes. Utilizing Cox analysis, molecular subtypes and prognostic signatures were established and subsequently verified using the GEO dataset. The ensuing molecular and immunological distinctions, along with therapy sensitivity between the two sub-cohorts, were examined via the ESTIMATE, CIBERSORT, and ssGSEA methodologies. Compartmentalization, somatic mutation, nomogram development, chemotherapy sensitivity prediction, and potential drug prediction analyses were also conducted according to the risk signature. Additionally, real-time qPCR and the HPA database corroborated the mRNA and protein expression patterns of signature genes in LUAD tissues. In summary, this research yielded an innovative T cell marker gene-based signature with remarkable potential to prognosis and anticipate immunotherapeutic outcomes in LUAD patients.

Identification of novel gene signature for lung adenocarcinoma by machine learning to predict immunotherapy and prognosis

Background

Lung adenocarcinoma (LUAD) as a frequent type of lung cancer has a 5-year overall survival rate of lower than 20% among patients with advanced lung cancer. This study aims to construct a risk model to guide immunotherapy in LUAD patients effectively.

Materials and methods

LUAD Bulk RNA-seq data for the construction of a model, single-cell RNA sequencing (scRNA-seq) data (GSE203360) for cell cluster analysis, and microarray data (GSE31210) for validation were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used the Seurat R package to filter and process scRNA-seq data. Sample clustering was performed in the ConsensusClusterPlus R package. Differentially expressed genes (DEGs) between two groups were mined by the Limma R package. MCP-counter, CIBERSORT, ssGSEA, and ESTIMATE were employed to evaluate immune characteristics. Stepwise multivariate analysis, Univariate Cox analysis, and Lasso regression analysis were conducted to identify key prognostic genes and were used to construct the risk model. Key prognostic gene expressions were explored by RT-qPCR and Western blot assay.

Results

A total of 27 immune cell marker genes associated with prognosis were identified for subtyping LUAD samples into clusters C3, C2, and C1. C1 had the longest overall survival and highest immune infiltration among them, followed by C2 and C3. Oncogenic pathways such as VEGF, EFGR, and MAPK were more activated in C3 compared to the other two clusters. Based on the DEGs among clusters, we confirmed seven key prognostic genes including CPA3, S100P, PTTG1, LOXL2, MELTF, PKP2, and TMPRSS11E. Two risk groups defined by the seven-gene risk model presented distinct responses to immunotherapy and chemotherapy, immune infiltration, and prognosis. The mRNA and protein level of CPA3 was decreased, while the remaining six gene levels were increased in clinical tumor tissues.

Conclusion

Immune cell markers are effective in clustering LUAD samples into different subtypes, and they play important roles in regulating the immune microenvironment and cancer development. In addition, the seven-gene risk model may serve as a guide for assisting in personalized treatment in LUAD patients.

PTTG1 Enhances Oncolytic Adenovirus 5 Entry into Pancreatic Adenocarcinoma Cells by Increasing CXADR Expression

Pituitary tumor-transforming gene 1 (PTTG1) is overexpressed in various types of tumors and functions as an oncogene; it could also be a potential target in tumor therapy. Meanwhile, the high mortality of pancreatic adenocarcinoma (PAAD) largely depends on the limited effectiveness of therapy. Based on the promising potential of PTTG1 in cancer treatment, we explored the influence of PTTG1 on the treatment of PAAD in this study. The Cancer Genome Atlas Program (TCGA) data showed that higher expression of PTTG1 was associated with higher clinical stages and worse prognosis of pancreatic cancer. In addition, the CCK-8 assay showed that the IC50 of gemcitabine and 5-fluorouracil (5-FU) was increased in BxPC-3-PTTG1^high and MIA PaCa-2-PTTG1^high cells. The TIDE algorithm indicated that the immune checkpoint blockades' (ICBs) efficiency is poor in the PTTG1 high group. Furthermore, we found that the efficiency of OAd5 was enhanced in BxPC-3-PTTG1^high and MIA PaCa-2-PTTG1^high cells and poor in BxPC-3-PTTG1^low and MIA PaCa-2-PTTG1^low cells. We used the OAd5 expressing GFP for transduction. As a result, the fluorescence intensity was enhanced in BxPC-3-PTTG1^high and MIA PaCa-2-PTTG1^high cells and decreased in BxPC-3-PTTG1^low and MIA PaCa-2-PTTG1^low cells 24 h after OAd5 transduction. The fluorescence intensity indicated that PTTG1 increased OAd5 entry. The flow cytometry assay showed that OAd5 receptor CXADR expression was enhanced by PTTG1. PTTG1 failed to further enhance OAd5 transduction in the case of CXADR knockdown. In summary, PTTG1 enhanced OAd5 transduction into pancreatic cancer cells by increasing CXADR expression on the cell surface.

Molecular characterization based on tumor microenvironment-related signatures for guiding immunotherapy and therapeutic resistance in lung adenocarcinoma

Background: Although the role of tumor microenvironment in lung adenocarcinoma (LUAD) has been explored in a number of studies, the value of TME-related signatures in immunotherapy has not been comprehensively characterized. Materials and Methods: Consensus clustering was conducted to characterize TME-based molecular subtypes using transcription data of LUAD samples. The biological pathways and immune microenvironment were assessed by CIBERSORT, ESTIMATE, and gene set enrichment analysis. A TME-related risk model was established through the algorithms of least absolute shrinkage and selection operator (Lasso) and stepwise Akaike information criterion (stepAIC). Results: Four TME-based molecular subtypes including C1, C2, C3, and C4 were identified, and they showed distinct overall survival, genomic characteristics, DNA methylation pattern, immune microenvironment, and biological pathways. C1 had the worst prognosis and high tumor proliferation rate. C3 and C4 had higher enrichment of anti-tumor signatures compared to C1 and C2. C4 had evidently low enrichment of epithelial-mesenchymal transition (EMT) signature and tumor proliferation rate. C3 was predicted to be more sensitive to immunotherapy compared with other subtypes. C1 is more sensitive to chemotherapy drugs, including Docetaxel, Vinorelbine and Cisplatin, while C3 is more sensitive to Paclitaxel. A five-gene risk model was constructed, which showed a favorable performance in three independent datasets. Low-risk group showed a longer overall survival, more infiltrated immune cells, and higher response to immunotherapy than high-risk group. Conclusion: This study comprehensively characterized the molecular features of LUAD patients based on TME-related signatures, demonstrating the potential of TME-based signatures in exploring the mechanisms of LUAD development. The TME-related risk model was of clinical value to predict LUAD prognosis and guide immunotherapy.

Clinical significance of securin expression in solid cancers: A PRISMA-compliant meta-analysis of published studies and bioinformatics analysis based on TCGA dataset

BACKGROUND

Numerous studies have investigated the clinical significance of securin expression in solid cancers; however, the results have been inconsistent. Hence, we performed a meta-analysis of published studies to assess the clinical value of securin expression in patients with solid cancers.

METHODS

The Chinese National Knowledge Infrastructure, Web of Science, PubMed, and EMDASE databases were searched for eligible studies (from inception up to April 2021). Bioinformatics analysis based on The Cancer Genome Atlas dataset was also performed to evaluate the prognostic value of securin expression.

RESULTS

A total of 25 articles with 26 studies were included in the meta-analysis. The results of the meta-analysis implied that high securin expression was positively correlated with unfavorable overall survival (OS) (hazard ratio = 1.52, 95% CI, 1.33-1.73; P < .001) and lymph node metastasis (odd ratio = 2.96, 95% CI, 2.26-3.86; P < .001). Consistently, our bioinformatics analysis showed that increased securin expression was associated with worse OS and shorter disease-free survival in cancer patients.

CONCLUSION

Our study indicated that securin overexpression was positively associated with metastasis and inversely related to the prognosis of patients with solid cancers. However, additional high-quality studies should be conducted to validate these findings.

Multiple datasets to explore the tumor microenvironment of cutaneous squamous cell carcinoma

BACKGROUND

Cutaneous squamous cell carcinoma (cSCC) is one of the most frequent types of cutaneous cancer. The composition and heterogeneity of the tumor microenvironment significantly impact patient prognosis and the ability to practice precision therapy. However, no research has been conducted to examine the design of the tumor microenvironment and its interactions with cSCC.

MATERIAL AND METHODS

We retrieved the datasets GSE42677 and GSE45164 from the GEO public database, integrated them, and analyzed them using the SVA method. We then screened the core genes using the WGCNA network and LASSO regression and checked the model's stability using the ROC curve. Finally, we performed enrichment and correlation analyses on the core genes.

RESULTS

We identified four genes as core cSCC genes: DTYMK, CDCA8, PTTG1 and MAD2L1, and discovered that RORA, RORB and RORC were the primary regulators in the gene set. The GO semantic similarity analysis results indicated that CDCA8 and PTTG1 were the two most essential genes among the four core genes. The results of correlation analysis demonstrated that PTTG1 and HLA-DMA, CDCA8 and HLA-DQB2 were significantly correlated.

CONCLUSIONS

Examining the expression levels of four primary genes in cSCC aids in our understanding of the disease's pathophysiology. Additionally, the core genes were found to be highly related with immune regulatory genes, suggesting novel avenues for cSCC prevention and treatment.

An aging-related signature predicts favorable outcome and immunogenicity in lung adenocarcinoma

Aging has been demonstrated to play vital roles in the prognosis and treatment efficacy of cancers, including lung adenocarcinoma (LUAD). This novel study aimed to construct an aging‐related risk signature to evaluate the prognosis and immunogenicity of LUAD. Transcriptomic profiles and clinical information were collected from a total of 2518 LUAD patients from 12 independent cohorts. The risk signature was developed by combining specific gene expression with the corresponding regression coefficients. One cohort treated with the immune checkpoint inhibitor (ICI) was also used. Subsequently, a risk signature was developed based on 21 aging‐related genes. LUAD patients with low‐risk scores exhibited improved survival outcomes in both the discovery and validation cohorts. Further immunology analysis revealed elevated lymphocyte infiltration, decreased infiltration of immune‐suppressive cells, immune response‐related pathways, and favorable ICI predictor enrichment in the low‐risk subgroup. Genomic mutation exploration indicated the enhanced mutation burden and higher mutation rates in significantly driver genes of TP53, KEAP1, SMARCA4, and RBM10 were enriched in patients with a low‐risk signature. In the immunotherapeutic cohort, it was observed that low‐risk aging scores were markedly associated with prolonged ICI prognosis. Overall, the estimated aging signature proved capable of evaluating the prognosis, tumor microenvironment, and immunogenicity, which further provided clues for tailoring prognosis prediction and immunotherapy strategies, apart from promoting individualized treatment plans for LUAD patients.