Construction and validation of a novel prognostic model for lung squamous cell cancer based on N6-methyladenosine-related genes

Combining single-cell and bulk RNA sequencing, NK cell marker genes reveal a prognostic and immune status in pancreatic ductal adenocarcinoma

The NK cell is an important component of the tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC), also plays a significant role in PDAC development. This study aimed to explore the relationship between NK cell marker genes and prognosis, immune response of PDAC patients. By scRNA-seq data, we found the proportion of NK cells were significantly downregulated in PDAC and 373 NK cell marker genes were screened out. By TCGA database, we enrolled 7 NK cell marker genes to construct the signature for predicting prognosis in PDAC patients. Cox analysis identified the signature as an independent factor for pancreatic cancer. Subsequently, the predictive power of signature was validated by 6 GEO datasets and had an excellent evaluation. Our analysis of relationship between the signature and patients' immune status revealed that the signature has a strong correlation with immunocyte infiltration, inflammatory reaction, immune checkpoint inhibitors (ICIs) response. The NK cell marker genes are closely related to the prognosis and immune capacity of PDAC patients, and they have potential value as a therapeutic target.

Knowledge mapping and current trends of m6A methylation in the field of cancer

Background

Cancer is a serious threat to people's lives and health, killing millions of people every year. Here, we performed a bibliometric analysis of tumor N6-methyladenosine methylation data between 2001 and 2022 to understand research trends and potential future directions.

Methods

A total of 890 papers published in the Web of Science core collection database between January 1, 2001 and December 31, 2022 were analyzed. Bibliometric analysis was performed using VOSviewer software to explore citations, co-authorship, co-citations, and co-occurrence.

Results

Although few papers were published before 2018, there was a rapid increase in publications after 2018. The People's Republic of China published 810 papers with 16,957 citations, both ranking first in the word. Sun Yat Sen University had the highest number of citations and published articles (67 published papers and 2702 citations), indicative of its active collaborative research status. Wang Xiao was the most co-cited author with 546 co-citations. Huang Yufei and Meng Jia ranked first with a link strength of 22, making them the most active collaborative authors. Frontiers in Oncology and Nature were the most active and co-cited journals, with 57 papers and 1953 co-citations, respectively. Studies of tumor N6-methyladenosine methylation can be divided into three categories: "tumor metabolism", "tumor bioinformatics and immunity", and "tumor progression".

Conclusions

This study systematically summarized the research on tumor N6-methyladenosine methylation during the past 20 years and suggested potential ways to explore its biomarkers and immunotherapy in the future.

Construction and verification of a novel prognostic risk model for kidney renal clear cell carcinoma based on immunity-related genes

Background: Currently, there are no useful biomarkers or prognostic risk markers for the diagnosis of kidney renal clear cell carcinoma (KIRC), although recent research has shown that both, the onset and progression of KIRC, are substantially influenced by immune-associated genes (IAGs). Objective: This work aims to create and verify the prognostic value of an immune risk score signature (IRSS) based on IAGs for KIRC using bioinformatics and public databases. Methods: Differentially expressed genes (DEGs) related to the immune systems (IAGs) in KIRC tissues were identified from The Cancer Genome Atlas (TCGA) databases. The DEGs between the tumor and normal tissues were identified using gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, a prognostic IRSS model was constructed and its prognostic and predictive performance was analyzed using survival analyses and nomograms. Kidney renal papillary cell carcinoma (KIRP) sets were utilized to further validate this model. Results: Six independent immunity-related genes (PAEP, PI3, SAA2, SAA1, IL20RB, and IFI30) correlated with prognosis were identified and used to construct an IRSS model. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer prognoses than those of patients in the low-risk group in both, the verification set (p <0.049; HR = 1.84; 95% CI = 1.02-3.32) and the training set (p < 0.001; HR = 3.12, 95% CI = 2.23-4.37). The numbers of regulatory T cells (Tregs) were significantly positively correlated with the six immunity-related genes identified, with correlation coefficients were 0.385, 0.415, 0.399, 0.451, 0.485, and 0.333, respectively (p <0.001). Conclusion: This work investigated the association between immune infiltration, immunity-related gene expression, and severity of KIRC to construct and verify a prognostic risk model for KIRC and KIRP.

Prognostic analysis of m6A-related genes as potential biomarkers in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal lung disease with limited treatment options. N6-methyladenosine (m6A) is a reversible RNA modification and has been implicated in various biological processes. However, there are few studies on m6A in IPF. This project mainly explores the prognostic value of m6A-related genes as potential biomarkers in IPF, in order to establish a set of accurate prognostic prediction model. In this study, we used GSE28042 dataset in GEO database to screen out 218 m6A-related candidate genes with high IPF correlation and high differential expression through differentially expressed gene analysis, WGCNA and m6A correlation analysis. The genes associated with the prognosis of IPF were screened out by univariate Cox regression analysis, LASSO analysis, and multivariate Cox regression analysis, and the multivariate Cox model of prognostic risk of related genes was constructed. We found that RBM11, RBM47, RIC3, TRAF5 and ZNF14 were key genes in our model. Finally, the prognostic prediction ability and independent prognostic characteristics of the risk model were evaluated by survival analysis and independent prognostic analysis, and verified by the GSE93606 dataset, which proved that the prognostic risk model we constructed has a strong and stable prediction efficiency.

Identification of a TGF-β signaling-related gene signature for prediction of immunotherapy and targeted therapy for lung adenocarcinoma

BACKGROUND

Transforming growth factor (TGF)-β signaling functions importantly in regulating tumor microenvironment (TME). This study developed a prognostic gene signature based on TGF-β signaling-related genes for predicting clinical outcome of patients with lung adenocarcinoma (LUAD).

METHODS

TGF-β signaling-related genes came from The Molecular Signature Database (MSigDB). LUAD prognosis-related genes were screened from all the genes involved in TGF-β signaling using least absolute shrinkage and selection operator (LASSO) Cox regression analysis and then used to establish a risk score model for LUAD. ESTIMATE and CIBERSORT analyzed infiltration of immune cells in TME. Immunotherapy response was analyzed by the TIDE algorithm.

RESULTS

A LUAD prognostic 5-gene signature was developed based on 54 TGF-β signaling-related genes. Prognosis of high-risk patients was significantly worse than low-risk patients. Both internal validation and external dataset validation confirmed a high precision of the risk model in predicting the clinical outcomes of LUAD patients. Multivariate Cox analysis demonstrated the model independence in OS prediction of LUAD. The risk model was significantly related to the infiltration of 9 kinds of immune cells, matrix, and immune components in TME. Low-risk patients tended to respond more actively to anti-PD-1 treatment, while high-risk patients were more sensitive to chemotherapy and targeted therapy.

CONCLUSIONS

The 5-gene signature based on TGF-β signaling-related genes showed potential for LUAD management.

B7-H3 is eligible for predicting clinical outcomes in lung adenocarcinoma patients treated with EGFR tyrosine kinase inhibitors

BACKGROUND

Not all lung adenocarcinoma (LUAD) patients with activating epidermal growth factor receptor (EGFR) mutations respond to tyrosine kinase inhibitors (TKIs) as intended. Thus, biomarkers are needed to identify patients who benefit most from EGFR-targeted therapy. Our previous in vitro data has shown that the co-signal molecule B7-H3 determines EGFR-TKI gefitinib susceptibility of EGFR-mutated LUAD cell lines, based on the potential crosslinking between B7-H3-induced signaling and EGFR signaling.

METHODS

We detected tumoral B7-H3 expression in the original biopsy from 56 treatment-naïve LUAD patients and analyzed the association between high/low B7-H3 expression with the clinical outcomes of first-line anti-EGFR therapy. The main criteria for the analysis of response were overall response rate (ORR), disease control rate (DCR), and progression-free survival (PFS), and the secondary criterion was overall survival (OS).

RESULTS

In the subgroups of B7-H3 high and low expression, the ORR were 16.0% (4/25) and 74.2% (23/31) (p<0.001), and the DCR were 36.0% (9/25) and 87.1% (27/31) (p<0.001), respectively. The PFS of B7-H3 high [median 8.7, 95% confidence interval (CI) 4.0-13.4] was significantly worse than that of B7-H3 low (median not reached) [HR 6.54 (95% CI 2.18-19.60), p=0.001]. The median OS was 15.9 (95% CI 10.0-21.8) months in the B7-H3 high cohort and 25.7 (95% CI 9.0-42.4) months in the B7-H3 low subjects [HR 2.08 (95% CI 1.07-4.02), p=0.03], respectively. Both the univariate and multivariate analyses identified B7-H3 as an independent factor associated with poor PFS (p=0.001, p=0.000) and OS (p=0.03, p=0.015).

CONCLUSION

B7-H3 may serve as a potential biomarker to predict clinical outcomes in EGFR-mutated LUAD patients treated with first-line EGFR-TKIs.

UCHL1 acts as a potential oncogene and affects sensitivity of common anti-tumor drugs in lung adenocarcinoma

Background

Lung adenocarcinoma is the leading cause of cancer death worldwide. Recently, ubiquitin C-terminal hydrolase L1 (UCHL1) has been demonstrated to be highly expressed in many tumors and plays the role of an oncogene. However, the functional mechanism of UCHL1 is unclear in lung adenocarcinoma progression.

Methods

We analyzed the differential expression of the UCHL1 gene in lung adenocarcinoma and normal lung tissues, and the correlation between the UCHL1 gene and prognosis was also analyzed by the bioinformatics database TCGA. Meanwhile, we detected and analyzed the expression of UCHL1 and Ki-67 protein in a tissue microarray (TMA) containing 150 patients with lung adenocarcinoma by immunohistochemistry (IHC) and clinicopathological characteristics by TCGA database. In vitro experiments, we knocked down the UCHL1 gene of A549 cells and detected the changes in cell migration, invasion, and apoptosis. At the same time, we analyzed the effect of UCHL1 on anti-tumor drug sensitivity of lung adenocarcinoma by a bioinformatics database. In terms of the detection rate of lung adenocarcinoma indicators, we analyzed the impact of UCHL1 combined with common clinical indicators on the detection rate of lung adenocarcinoma through a bioinformatics database.

Results

In this study, the analysis of UCHL1 protein expression in lung adenocarcinoma proved that obviously higher UCHL1 protein level was discovered in lung adenocarcinoma tissues. The expression of UCHL1 was closely related to poor clinical outcomes. Interestingly, a significantly positive correlation between the expression of UCHL1 and Ki-67-indicated UCHL1 was associated with tumor migration and invasion. Through executing loss of function tests, we affirmed that silencing of UCHL1 expression significantly inhibited migration and invasion of lung adenocarcinoma cells in vitro. Furthermore, lung adenocarcinoma cells with silenced UCHL1 showed a higher probability of apoptosis. In terms of the detection rate of lung adenocarcinoma indicators, we discovered UCHL1 could improve the detection rate of clinical lung adenocarcinoma and affect drug sensitivity.

Conclusion

In lung adenocarcinoma, UCHL1 promotes tumor migration, invasion, and metastasis by inhibiting apoptosis and has an important impact on the clinical drug treatment of lung adenocarcinoma. In addition, UCHL1 can improve the detection rate of clinical lung adenocarcinoma. Above all, UCHL1 may be a new marker for the diagnosis of lung adenocarcinoma and provide a new target for the treatment of clinical diseases.