Identification and validation of autophagy-related gene expression for predicting prognosis in patients with idiopathic pulmonary fibrosis

Signature identification based on immunogenic cell death-related lncRNAs to predict the prognosis and immune activity of patients with endometrial carcinoma

Background

Endometrial carcinoma (EC) is one of the most prevalent gynecologic malignancies and requires further classification for treatment and prognosis. Long non-coding RNAs (lncRNAs) and immunogenic cell death (ICD) play a critical role in tumor progression. Nevertheless, the role of lncRNAs in ICD in EC remains unclear. This study aimed to explore the role of ICD related-lncRNAs in EC via bioinformatics and establish a prognostic risk model based on the ICD-related lncRNAs. We also explored immune infiltration and immune cell function across prognostic groups and made treatment recommendations.

Methods

A total of 552 EC samples and clinical data of 548 EC patients were extracted from The Cancer Genome Atlas (TCGA) database and University of California Santa Cruz (UCSC) Xena, respectively. A prognostic-related feature and risk model was developed using the least absolute shrinkage and selection operator (LASSO). Subtypes were classified with consensus cluster analysis and validated with t-Distributed Stochastic Neighbor Embedding (tSNE). Kaplan-Meier analysis was conducted to assess differences in survival. Infiltration by immune cells was estimated by single sample gene set enrichment analysis (ssGSEA), Tumor IMmune Estimation Resource (TIMER) algorithm. Quantitative polymerase chain reaction (qPCR) was used to detect lncRNAs expression in clinical samples and cell lines. A series of studies was conducted in vitro and in vivo to examine the effects of knockdown or overexpression of lncRNAs on ICD.

Results

In total, 16 ICD-related lncRNAs with prognostic values were identified. Using SCARNA9, FAM198B-AS1, FKBP14-AS1, FBXO30-DT, LINC01943, and AL161431.1 as risk model, their predictive accuracy and discrimination were assessed. We divided EC patients into high-risk and low-risk groups. The analysis showed that the risk model was an independent prognostic factor. The prognosis of the high- and low-risk groups was different, and the overall survival (OS) of the high-risk group was lower. The low-risk group had higher immune cell infiltration and immune scores. Consensus clustering analysis divided the samples into four subtypes, of which cluster 4 had higher immune cell infiltration and immune scores.

Conclusions

A prognostic signature composed of six ICD related-lncRNAs in EC was established, and a risk model based on this signature can be used to predict the prognosis of patients with EC.

S100A8/A9hi neutrophils induce mitochondrial dysfunction and PANoptosis in endothelial cells via mitochondrial complex I deficiency during sepsis

S100a8/a9, largely released by polymorphonuclear neutrophils (PMNs), belongs to the S100 family of calcium-binding proteins and plays a role in a variety of inflammatory diseases. Although S100a8/a9 has been reported to trigger endothelial cell apoptosis, the mechanisms of S100a8/a9-induced endothelial dysfunction during sepsis require in-depth research. We demonstrate that high expression levels of S100a8/a9 suppress Ndufa3 expression in mitochondrial complex I via downregulation of Nrf1 expression. Mitochondrial complex I deficiency contributes to NAD+-dependent Sirt1 suppression, which induces mitochondrial disorders, including excessive fission and blocked mitophagy, and mtDNA released from damaged mitochondria ultimately activates ZBP1-mediated PANoptosis in endothelial cells. Moreover, based on comprehensive scRNA-seq and bulk RNA-seq analyses, S100A8/A9hi neutrophils are closely associated with the circulating endothelial cell count (a useful marker of endothelial damage), and S100A8 is an independent risk factor for poor prognosis in sepsis patients.

A novel model for predicting prognosis in patients with idiopathic pulmonary fibrosis based on endoplasmic reticulum stress-related genes

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic disease of unknown pathogenic origin. Endoplasmic reticulum (ER) stress refers to the process by which cells take measures to ER function when the morphology and function of the reticulum are changed. Recent studies have demonstrated that the ER was involved in the evolution and progression of IPF. In this study, we obtained transcriptome data and relevant clinical information from the Gene Expression Omnibus database and conducted bioinformatics analysis. Among the 544 ER stress-related genes (ERSRGs), 78 were identified as differentially expressed genes (DEGs). These DEGs were primarily enriched in response to ER stress, protein binding, and protein processing. Two genes (HTRA2 and KTN1) were included for constructing an accurate molecular signature. The overall survival of patients was remarkably worse in the high-risk group than in the low-risk group. We further analyzed the difference in immune cells between high-risk and low-risk groups. M0 and M2 macrophages were significantly increased in the high-risk group. Our results suggested that ERSRGs might play a critical role in the development of IPF by regulating the immune microenvironment in the lungs, which provide new insights on predicting the prognosis of patients with IPF.

Predictive biomarkers of disease progression in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial disease that cannot be cured, and treatment options for IPF are very limited. Early diagnosis, close monitoring of disease progression, and timely treatment are therefore the best options for patients due to the irreversibility of IPF. Effective markers help doctors judge the development and prognosis of disease. Recent research on traditional biomarkers (KL-6, SP-D, MMP-7, TIMPs, CCL18) has provided novel ideas for predicting disease progression and prognosis. Some emerging biomarkers (HE4, GDF15, PRDX4, inflammatory cells, G-CSF) also provide more possibilities for disease prediction. In addition to markers in serum and bronchoalveolar lavage fluid (BALF), some improvements related to the GAP model and chest HRCT also show good predictive ability for disease prognosis.

Machine learning-based approach for efficient prediction of diagnosis, prognosis and lymph node metastasis of papillary thyroid carcinoma using adhesion signature selection

The association between adhesion function and papillary thyroid carcinoma (PTC) is increasingly recognized; however, the precise role of adhesion function in the pathogenesis and prognosis of PTC remains unclear. In this study, we employed the robust rank aggregation algorithm to identify 64 stable adhesion-related differentially expressed genes (ARDGs). Subsequently, using univariate Cox regression analysis, we identified 16 prognostic ARDGs. To construct PTC survival risk scoring models, we employed Lasso Cox and multivariate + stepwise Cox regression methods. Comparative analysis of these models revealed that the Lasso Cox regression model (LPSRSM) displayed superior performance. Further analyses identified age and LPSRSM as independent prognostic factors for PTC. Notably, patients classified as low-risk by LPSRSM exhibited significantly better prognosis, as demonstrated by Kaplan-Meier survival analyses. Additionally, we investigated the potential impact of adhesion feature on energy metabolism and inflammatory responses. Furthermore, leveraging the CMAP database, we screened 10 drugs that may improve prognosis. Finally, using Lasso regression analysis, we identified four genes for a diagnostic model of lymph node metastasis and three genes for a diagnostic model of tumor. These gene models hold promise for prognosis and disease diagnosis in PTC.

Seven bacterial response-related genes are biomarkers for colon cancer

BACKGROUND

Colon cancer (CC) is a common tumor that causes significant harm to human health. Bacteria play a vital role in cancer biology, particularly the biology of CC. Genes related to bacterial response were seldom used to construct prognosis models. We constructed a bacterial response-related risk model based on three Molecular Signatures Database gene sets to explore new markers for predicting CC prognosis.

METHODS

The Cancer Genome Atlas (TCGA) colon adenocarcinoma samples were used as the training set, and Gene Expression Omnibus (GEO) databases were used as the test set. Differentially expressed bacterial response-related genes were identified for prognostic gene selection. Univariate Cox regression analysis, least absolute shrinkage and selection operator-penalized Cox regression analysis, and multivariate Cox regression analysis were performed to construct a prognostic risk model. The individual diagnostic effects of genes in the prognostic model were also evaluated. Moreover, differentially expressed long noncoding RNAs (lncRNAs) were identified. Finally, the expression of these genes was validated using quantitative polymerase chain reaction (qPCR) in cell lines and tissues.

RESULTS

A prognostic signature was constructed based on seven bacterial response genes: LGALS4, RORC, DDIT3, NSUN5, RBCK1, RGL2, and SERPINE1. Patients were assigned a risk score based on the prognostic model, and patients in the TCGA cohort with a high risk score had a poorer prognosis than those with a low risk score; a similar finding was observed in the GEO cohort. These seven prognostic model genes were also independent diagnostic factors. Finally, qPCR validated the differential expression of the seven model genes and two coexpressed lncRNAs (C6orf223 and SLC12A9-AS1) in 27 pairs of CC and normal tissues. Differential expression of LGALS4 and NSUN5 was also verified in cell lines (FHC, COLO320DM, SW480).

CONCLUSIONS

We created a seven-gene bacterial response-related gene signature that can accurately predict the outcomes of patients with CC. This model can provide valuable insights for personalized treatment.

Development and validation of the prognostic model based on autophagy-associated genes in idiopathic pulmonary fibrosis

Background

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease. Many studies suggest that autophagy may be related to disease progression and prognosis in IPF. However, the mechanisms involved have not been fully elucidated.

Methods

We incorporated 232 autophagy-associated genes (AAGs) and two datasets, GSE28042 and GSE27957, from the GEO database. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression were used to construct the autophagy-associated prognostic model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the functions of these autophagy-associated genes. CIBERSORT algorithm was used to calculate the immune cell infiltration between patients in the high-risk score and low-risk score groups. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) was performed to explore the mRNA expression of five genes in the autophagy-associated risk model.

Results

We constructed a 5-autophagy-associated genes signature based on Univariate Cox analysis and LASSO regression. In our autophagy-associated risk model, IPF patients in the high-risk group demonstrated a poor overall survival rate compared to patients in the low-risk group. For 1-, 2-, and 3-year survival rates, the AUC predictive value of the AAG signature was 0.670, 0.787, and 0.864, respectively. These results were validated in the GSE27957 cohort, confirming the good prognostic effect of our model. GO and KEGG pathway analyses enriched immune-related pathways between the high-risk and low-risk groups. And there was also a significant difference in immune cell infiltration between two groups. And the results of qRT-PCR showed that the expression levels of FOXO1, IRGM, MYC, and PRKCQ were significantly decreased in the Peripheral Blood Mononuclear Cell (PBMC) of IPF patient samples.

Conclusion

Our study constructed and validated an autophagy-associated risk model based on MYC, MAPK1, IRGM, PRKCQ, and FOXO1. And those five genes may influence the progression of IPF by regulating immune responses and immune cells.