The basement membrane-related gene signature is associated with immunity and predicts survival accurately in hepatocellular carcinoma

Genetic association of tertiary lymphoid structure-related gene signatures with HCC based on Mendelian randomization and machine learning and construction of prognosis model

BACKGROUND

Tertiary lymphoid structures (TLS) are formed in numerous cancer types. However, their value and significance in hepatocellular carcinoma (HCC) is unclear.

METHODS

We performed differential genes expression analysis of TLS-related Genes (TLSG) based on The Cancer Genome Atlas (TCGA) database, and performed Mendelian randomization (MR) analysis using expression quantitative trait loci, and then took their intersecting genes. A TLSG prognostic signature (TLSGPS)-based risk score was constructed using Least Absolute Shrinkage and Selection Operator (LASSO), univariate and multivariate COX regression analysis, and survival analysis was then performed. We used the International Cancer Genome Consortium for outside validation. We also performed biological function, tumor mutational burden, immune infiltration, single-cell analysis, CeRNA and drug sensitivity analysis based on TLSGPS.

RESULTS

Three TLSGs (HM13, CSTB, CDCA7L) were identified to construct the TLSGPS, which showed good predictive ability and outperformed most prognostic signatures. MR suggested that HM13 (OR = 0.9997, 95 %CI: 0.9994-0.9999, P = 0.014) and CSTB (OR = 0.9997, 95 %CI: 0.9995-0.9999, P = 0.048) were negatively correlated with the risk of HCC onset, while CDCA7L (OR = 1.0004, 1.0001-1.0007, P = 0.0161) was the opposite. The differences in biological functions between the TLSGPS-based high-risk group (HRG) and low-risk group (LRG) involved cell proliferation, differentiation, and drug metabolism. HRG plus high mutations exhibited extremely poor survival. HRG had higher abundance of immune cell-oncogenic phenotypes, higher immune escape ability, and greater sensitivity to Afatinib, Dasatinib, and Gefitinib.

CONCLUSION

3 TLSGs identified by machine learning and MR can predict the onset, prognosis and clinical treatment of HCC patients, and had significant genetic association with HCC.

A basement membrane-related signature for prognosis and immunotherapy benefit in bladder cancer based on machine learning

BACKGROUND

Bladder cancer has a poor clinical outcome because of its high aggressiveness. Basement membrane plays vital functions in tumor invasion and migration. Invasion and distant metastasis of cancer are facilitated by degradation of the basement membrane and extracellular matrix.

METHODS

Ten machine learning methods were utilized to develop the basement membrane-related signature (MRS) using datasets from TCGA, GSE13507, GSE31684, GSE32984 and GSE48276. Three anti-PD1 or anti-CTLA4 datasets and several predicting scores were used to investigate the performance of MRS in predicting the immunotherapy benefits.

RESULTS

A predicting model based on the Enet algorithm (alpha = 0.1) was chosen as the optimal MRS since it had the highest average C-index being 0.72. According to TCGA data, the MRS showed good performance in predicting bladder cancer patients' clinical outcomes, with area under curves of 0.744, 0.766 and 0.817 for 1, 3, and 5-year receiver operating characteristic curve, respectively. PD1 and CTLA4 immunophenoscopes were associated with a low MRS score, as well as a lower tumor immune dysfunction and exclusion score. As MRS score increased, immune-activated cells levels decreased, tumor immune dysfunction and exclusion score decreased, immune escape score decreased, intratumor heterogeneity score decreased, PD1&CTLA4 immunophenoscore increased, and tumor mutational burden score increased in bladder cancer, suggesting better immunotherapy benefits. Bladder cancer cases with high MRS score was correlated with higher cancer related hallmark scores, including NOTCH and glycolysis signaling.

CONCLUSION

A new MRS has been developed for bladder cancer, which could be used to predict prognosis and the success of immunotherapy.

Identification of a basement membrane-related gene signature for predicting prognosis, immune infiltration, and drug sensitivity in colorectal cancer

Background

Colorectal cancer (CRC) is the most common malignancy affecting the gastrointestinal tract. Extensive research indicates that basement membranes (BMs) may play a crucial role in the initiation and progression of the disease.

Methods

Data on the RNA expression patterns and clinicopathological information of patients with CRC were sourced from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. A BM-linked risk signature for the prediction of overall survival (OS) was formulated using univariate Cox regression and combined machine learning techniques. Survival outcomes, functional pathways, the tumor microenvironment (TME), and responses to both immunotherapy and chemotherapy within varying risk classifications were also investigated. The expression trends of the model genes were evaluated by reverse transcription polymerase chain reaction (RT-PCR) and the Human Protein Atlas (HPA) database.

Results

A nine-gene risk signature containing UNC5C, TINAG, TIMP1, SPOCK3, MMP1, AGRN, UNC5A, ADAMTS4, and ITGA7 was constructed for the prediction of outcomes in patients with CRC. The expression profiles of these candidate genes were verified using RT-PCR and the HPA database and were found to be consistent with the findings on differential gene expression in the TCGA dataset. The validity of the signature was confirmed using the GEO cohort. The patients were stratified into different risk groups according to differences in clinicopathological characteristics, TME features, enrichment functions, and drug sensitivities. Lastly, the prognostic nomogram model based on the risk score was found to be effective in identifying high-risk patients and predicting OS.

Conclusion

A basement membrane-related risk signature was constructed and found to be effective for predicting the prognosis of patients with CRC.

Integration of basement membrane-related genes in a risk signature for prognosis in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is characterized by high heterogeneity and recurrence rates, posing significant challenges for stratification and treatment. Basement membrane-related genes (BMGs) play a crucial role in tumor initiation and progression. Clinical and transcriptomic data of ccRCC patients were extracted from TCGA and GEO databases. We employed univariate regression and LASSO-Cox stepwise regression analysis to construct a BMscore model based on BMGs expression level. A nomogram combining clinical features and BMscore was constructed to predict individual survival probabilities. Further enrichment analysis and immune-related analysis were conducted to explore the enriched pathways and immune features associated with BMGs. High-risk individuals predicted by BMscore exhibited poorer overall survival, which was consistent with the validation dataset. BMscore was identified as an independent risk factor for ccRCC. Functional analysis revealed that BMGs were related to cell-matrix and tumor-associated signaling pathways. Immune profiling suggests that BMGs play a key role in immune interactions and the tumor microenvironment. BMGs serve as a novel prognostic predictor for ccRCC and play a role in the immune microenvironment and treatment response. Targeting the BM may represent an alternative therapeutic approach for ccRCC.

Analysis of m6A regulators related immune characteristics in ankylosing spondylitis by integrated bioinformatics and computational strategies

N6-methyladenosine (m6A) modification, as a common epigenetic modification, has been widely studied in autoimmune diseases. However, the role of m6A in the regulation of the immune microenvironment of ankylosing spondylitis (AS) remains unclear. Therefore, we aimed to investigate the effect of m6A modification on the immune microenvironment of AS. We first evaluated RNA modification patterns mediated by 26 m6A regulators in 52 AS samples and 20 healthy samples. Thereafter, an m6A related classifier composed of seven genes was constructed and could effectively distinguish healthy and AS samples. Then, the correlation between m6A regulators and immune characteristics were investigated, including infiltrating immunocytes, immune reactions activity, and human leukocyte antigen (HLA) genes expression. The results indicated that m6A regulators was closely correlated with immune characteristics. For example, EIF3A was significantly related to infiltrating immunocytes; IGF2BP2 and EIF3A were significant regulators in immune reaction of TGF-β family member, and the expression of HLA-DPA1 and HLA-E were affected by EIF3A and ALKBH5. Next, two distinct m6A expression patterns were identified through unsupervised clustering analysis, and diverse immune characteristics were found between them. A total of 5889 m6A phenotype-related genes were obtained between the two expression patterns, and their biological functions were revealed. Finally, we validated the expression status of m6A modification regulators using two additional datasets. Our findings illustrate that m6A modifications play a critical role in the diversity and complexity of the AS immune microenvironment.

Bioinformatics-based analysis of the roles of basement membrane-related gene AGRN in systemic lupus erythematosus and pan-cancer development

Introduction

Systemic lupus erythematosus (SLE) is an autoimmune disease involving many systems and organs, and individuals with SLE exhibit unique cancer risk characteristics. The significance of the basement membrane (BM) in the occurrence and progression of human autoimmune diseases and tumors has been established through research. However, the roles of BM-related genes and their protein expression mechanisms in the pathogenesis of SLE and pan-cancer development has not been elucidated.

Methods

In this study, we applied bioinformatics methods to perform differential expression analysis of BM-related genes in datasets from SLE patients. We utilized LASSO logistic regression, SVM-RFE, and RandomForest to screen for feature genes and construct a diagnosis model for SLE. In order to attain a comprehensive comprehension of the biological functionalities of the feature genes, we conducted GSEA analysis, ROC analysis, and computed levels of immune cell infiltration. Finally, we sourced pan-cancer expression profiles from the TCGA and GTEx databases and performed pan-cancer analysis.

Results

We screened six feature genes (AGRN, PHF13, SPOCK2, TGFBI, COL4A3, and COLQ) to construct an SLE diagnostic model. Immune infiltration analysis showed a significant correlation between AGRN and immune cell functions such as parainflammation and type I IFN response. After further gene expression validation, we finally selected AGRN for pan-cancer analysis. The results showed that AGRN's expression level varied according to distinct tumor types and was closely correlated with some tumor patients' prognosis, immune cell infiltration, and other indicators.

Discussion

In conclusion, BM-related genes play a pivotal role in the pathogenesis of SLE, and AGRN shows immense promise as a target in SLE and the progression of multiple tumors.

Identification of molecular subtypes based on PANoptosis-related genes and construction of a signature for predicting the prognosis and response to immunotherapy response in hepatocellular carcinoma

Background

Previous studies have demonstrated that PANoptosis is strongly correlated with cancer immunity and progression. This study aimed to develop a PANoptosis-related signature (PANRS) to explore its potential value in predicting the prognosis and immunotherapy response of hepatocellular carcinoma (HCC).

Methods

Based on the expression of PANoptosis-related genes, three molecular subtypes were identified. To construct a signature, the differentially expressed genes between different molecular subtypes were subjected to multivariate least absolute shrinkage and selection operator Cox regression analyses. The risk scores of patients in the training set were calculated using the signature. The patients were classified into high-risk and low-risk groups based on the median risk scores. The predictive performance of the signature was evaluated using Kaplan-Meier plotter, receiving operating characteristic curves, nomogram, and calibration curve. The results were validated using external datasets. Additionally, the correlation of the signature with the immune landscape and drug sensitivity was examined. Furthermore, the effect of LPCAT1 knockdown on HCC cell behavior was verified using in vitro experiments.

Results

This study developed a PANRS. The risk score obtained by using the PANRS was an independent risk factor for the prognosis of patients with HCC and exhibited good prognostic predictive performance. The nomogram constructed based on the risk score and clinical information can accurately predicted the survival probability of patients with HCC. Patients with HCC in the high-risk groups have high immune scores and tend to generate an immunosuppressive microenvironment. They also exhibited a favorable response to immunotherapy, as evidenced by high tumor mutational burden, high immune checkpoint gene expression, high human leukocyte antigen gene expression, low tumor immune dysfunction and low exclusion scores. Additionally, the PANRS enabled the identification of 15 chemotherapeutic agents, including sorafenib, for patients with HCC with different risk levels, guiding clinical treatment. The signature gene LPCAT1 was upregulated in HCC cell lines. LPCAT1 knockdown markedly decreased HCC cell proliferation and migration.

Conclusion

PANRS can accurately predict the prognosis and immunotherapy response of patients with HCC and consequently guide individualized treatment.

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.