A prognostic and therapeutic hallmark developed by the integrated profile of basement membrane and immune infiltrative landscape in lung adenocarcinoma

Development and validation of a basement membrane-associated immune prognostic model for hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC), one of the most common malignant tumors worldwide, has a poor prognosis primarily due to its invasive and metastatic characteristics. Cancer invasion through basement membrane (BM) is the pivotal initial step in tumor dissemination and metastasis. This study aimed to identify gene signatures associated with the BM to enhance the overall prognosis of HCC.

Methods

In this study, we performed multiple bioinformatics analyses based on the RNA sequencing (RNA-seq) data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. An unsupervised consistent cluster analysis was conducted on 370 HCC patients, categorizing them into two distinct groups based on the expression profiles of 222 BM-related genes. Differentially expressed genes (DEGs) between these clusters were identified, followed by functional enrichment analysis. To explore the differences between the groups, the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) and Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) algorithms were applied, along with the analysis of immune checkpoint molecules and human leukocyte antigen (HLA) expression levels. This helped in understanding the relationship between the HCC immune microenvironment and BM-related genes. A prognostic model was constructed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, with its performance subsequently estimated and validated. Additionally, hub biomarkers genes were identified using machine learning techniques, followed by an analysis of their functions and relationships with clinical characteristics. Finally, single-cell clustering analysis was employed to further investigate the expression distribution of these genes within the HCC immune microenvironment.

Results

Following consistent cluster analysis, two groups were identified: the BM high group and the BM low group. Among the 6,221 DEGs between the two groups, 5,863 were upregulated and 358 were downregulated, with enrichment functions primarily associated with extracellular matrix (ECM) organization, cell adhesion, immune response, and metabolism. The expression levels of BM-related genes were found to regulate the HCC immune microenvironment. Using univariate Cox regression analysis, 60 prognostic BM-related genes were identified, leading to the establishment of an 11-gene prognostic model named BMscore to predict the overall survival (OS) of HCC patients. The high BMscore group indicated worse prognosis, and the model's predictive performance was validated using the GEO dataset. P3H1 and ADAMTS5 were identified as hub biomarkers, playing roles in cell proliferation and ECM metabolism, with their expression distributions mapped respectively.

Conclusions

A prognostic model based on BM-related genes was successfully developed and shows promise for evaluating prognoses and offering personalized treatment recommendations.

Bioinformatics analysis of an immunotherapy responsiveness-related gene signature in predicting lung adenocarcinoma prognosis

Background

Immune therapy has become first-line treatment option for patients with lung cancer, but some patients respond poorly to immune therapy, especially among patients with lung adenocarcinoma (LUAD). Novel tools are needed to screen potential responders to immune therapy in LUAD patients, to better predict the prognosis and guide clinical decision-making. Although many efforts have been made to predict the responsiveness of LUAD patients, the results were limited. During the era of immunotherapy, this study attempts to construct a novel prognostic model for LUAD by utilizing differentially expressed genes (DEGs) among patients with differential immune therapy responses.

Methods

Transcriptome data of 598 patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) database, which included 539 tumor samples and 59 normal control samples, with a mean follow-up time of 29.69 months (63.1% of patients remained alive by the end of follow-up). Other data sources including three datasets from the Gene Expression Omnibus (GEO) database were analyzed, and the DEGs between immunotherapy responders and nonresponders were identified and screened. Univariate Cox regression analysis was applied with the TCGA cohort as the training set and GSE72094 cohort as the validation set, and least absolute shrinkage and selection operator (LASSO) Cox regression were applied in the prognostic-related genes which fulfilled the filter criteria to establish a prognostic formula, which was then tested with time-dependent receiver operating characteristic (ROC) analysis. Enriched pathways of the prognostic-related genes were analyzed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and tumor immune microenvironment (TIME), tumor mutational burden, and drug sensitivity tests were completed with appropriate packages in R (The R Foundation of Statistical Computing). Finally, a nomogram incorporating the prognostic formula was established.

Results

A total of 1,636 DEGs were identified, 1,163 prognostic-related DEGs were extracted, and 34 DEGs were selected and incorporated into the immunotherapy responsiveness-related risk score (IRRS) formula. The IRRS formula had good performance in predicting the overall prognoses in patients with LUAD and had excellent performance in prognosis prediction in all LUAD subgroups. Moreover, the IRRS formula could predict anticancer drug sensitivity and immunotherapy responsiveness in patients with LUAD. Mechanistically, immune microenvironments varied profoundly between the two IRRS groups; the most significantly varied pathway between the high-IRRS and low-IRRS groups was ribonucleoprotein complex biogenesis, which correlated closely with the TP53 and TTN mutation burdens. In addition, we established a nomogram incorporating the IRRS, age, sex, clinical stage, T-stage, N-stage, and M-stage as predictors that could predict the prognoses of 1-year, 3-year, and 5-year survival in patients with LUAD, with an area under curve (AUC) of 0.718, 0.702, and 0.68, respectively.

Conclusions

The model we established in the present study could predict the prognosis of LUAD patients, help to identify patients with good responses to anticancer drugs and immunotherapy, and serve as a valuable tool to guide clinical decision-making.

Identification of Co-diagnostic Genes for Heart Failure and Hepatocellular Carcinoma Through WGCNA and Machine Learning Algorithms

This research delves into the intricate relationship between hepatocellular carcinoma (HCC) and heart failure (HF) by exploring shared genetic characteristics and molecular processes. Employing advanced methodologies such as differential analysis, weighted correlation network analysis (WGCNA), and algorithms like Random Forest (RF), Least Absolute Shrinkage Selection (LASSO), and XGBoost, we meticulously identified modular differential genes (DEGs) associated with both HF and HCC. Gene Set Variation Analysis (GSVA) and single sample gene set enrichment analysis (ssGSEA) were employed to unveil underlying biological mechanisms. The study revealed 88 core genes shared between HF and HCC, indicating a common mechanism. Enrichment analysis emphasized the roles of immune responses and inflammation in both diseases. Leveraging XGBoost, we crafted a robust multigene diagnostic model (including FCN3, MAP2K1, AP3M2, CDH19) with an area under the curve (AUC) > 0.9, showcasing exceptional predictive accuracy. GSVA and ssGSEA analyses unveiled the involvement of immune cells and metabolic pathways in the pathogenesis of HF and HCC. This research uncovers a pivotal interplay between HF and HCC, highlighting shared pathways and key genes, offering promising insights for future clinical treatments and experimental research endeavors.

Screening of hub inflammatory bowel disease biomarkers and identification of immune-related functions based on basement membrane genes

BACKGROUND

Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is a chronic, inflammatory, and autoimmune disease, but its specific etiology and pathogenesis are still unclear. This study aimed to better discover the causative basement membrane (BM) genes of their subtypes and their associations.

METHODS

The differential expression of BM genes between CD and UC was analyzed and validated by downloading relevant datasets from the GEO database. We divided the samples into 3 groups for comparative analysis. Construction of PPI networks, enrichment of differential gene functions, screening of Lasso regression models, validation of ROC curves, nomogram for disease prediction and other analytical methods were used. The immune cell infiltration was further explored by ssGSEA analysis, the immune correlates of hub BM genes were found, and finally, the hub central genes were screened by machine learning.

RESULTS

We obtained 6 candidate hub BM genes related to cellular immune infiltration in the CD and UC groups, respectively, and further screened the central hub genes ADAMTS17 and ADAMTS9 through machine learning. And in the ROC curve models, AUC > 0.7, indicating that this characteristic gene has a more accurate predictive effect on IBD. We also found that the pathogenicity-related BM genes of the CD and UC groups were mainly concentrated in the ADAMTS family (ADAMTS17 and ADAMTS9). Addition there are some differences between the two subtypes, and the central different hub BM genes are SPARC, POSTN, and ADAMTS2.

CONCLUSIONS

In the current study, we provided a nomogram model of CD and UC composed of BM genes, identified central hub genes, and clarified the similarities and differences between CD and UC. This will have potential value for preclinical, clinical, and translational guidance and differential research in IBD.

Comprehensive analysis of the basement membrane in lung adenocarcinoma by bulk and single-cell sequencing analysis

Background: The basement membrane (BM), as a critical component of the extracellular matrix, plays a role in cancer progression. However, the role of the BM in lung adenocarcinoma (LUAD) remains unclear. Methods: A total of 1383 patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts were enrolled in the study, and BM-related differentially expressed genes (BM-DEGs) were screened using weighted gene coexpression network analysis (WGCNA) and differential expression analysis. We next built a prognostic model using Cox regression analysis and separated patients into two groups based on the median risk score. This signature was validated with in vitro experiments, and its mechanism was investigated by enrichment and tumour microenvironment analyses. We also evaluated whether this signature could predict sensitivity to chemotherapy and immunotherapy. Finally, single-cell RNA sequencing analysis was utilized to analyse the expression of signature genes in different cells. Results: Thirsty-seven BM-DEGs were discovered, and a prognostic signature based on 4 BM-DEGs (HMCN2, FBLN5, ADAMTS15 and LAD1) was obtained in the TCGA cohort and validated in GEO cohorts. Survival curves and ROC curve analysis demonstrated that the risk score was a significant predictor of survival in all cohorts even when considering the effect of other clinical indexes. Low-risk patients had longer survival times, higher immune cell infiltration levels and better immunotherapeutic responses. Single-cell analysis showed that FBLN5 and LAD1 were overexpressed in fibroblasts and cancer cells, respectively, compared to normal cells. Conclusion: This study evaluated the clinical role of the BM in LUAD and primarily explored its mechanism.

Effects of spaceflight on the spleen and thymus of mice: Gene pathway analysis and immune infiltration analysis

During space flight, the immune system function of the body is disrupted due to continuous weightlessness, radiation and other factors, resulting in an increased incidence of infectious diseases in astronauts. However, the effect of space flight on the immune system at the molecular level is unknown. The aim of this study was to identify key genes and pathways of spatial environmental effects on the spleen and thymus using bioinformatics analysis of the GEO dataset. Differentially expressed genes (DEGs) in the spleen and thymus of mice preflight and postflight were screened by comprehensive analysis of gene expression profile data. Then, GO enrichment analysis of DEGs was performed to determine the biological role of DEGs. A protein-protein interaction network was used to identify hub genes. In addition, transcription factors in DEGs were screened, and a TF-target regulatory network was constructed. Finally, immune infiltration analysis was performed on spleen and thymus samples from mice. The results showed that DEGs in the spleen and thymus are mainly involved in immune responses and in biological processes related to platelets. Six hub genes were identified in the spleen and 13 in the thymus, of which Ttr, Aldob, Gc and Fabp1 were common to both tissues. In addition, 5 transcription factors were present in the DEGs of the spleen, and 9 transcription factors were present in the DEGs of the thymus. The spatial environment can influence the degree of immune cell infiltration in the spleen and thymus. Our study bioinformatically analyzed the GEO dataset of spacefaring mice to identify the effects of the space environment on the immune system and the genes that play key roles, providing insights for the treatment of spaceflight-induced immune system disorders.