Expression signature, prognosis value and immune characteristics of cathepsin F in non-small cell lung cancer identified by bioinformatics assessment

Song, Libin; Wang, Xianhui; Wang, Cheng; Wu, Yi; Liu, Min; Liu, Ruizi; Zhang, Shenyi; Xia, Hong; Líu, Hao; Tai, Xuejiao; Zhao, Hai Tao; Li, Xihua; Ji, Fuyun

doi:10.1186/s12890-021-01796-w

Cited by 18 publications

(18 citation statements)

References 73 publications

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“…CTSF was a member of the cysteine cathepsin protease family, which was among the most intensively studied protease families in cancer research 108 . Numerous studies have reported abnormal expression of cathepsin family members in a variety of cancers 109 , and their regulatory roles in cancer have been detailed through extensive mouse model studies 110 . RSPO3, recognized as an oncogenic protein, was implicated in cancer progression in multiple body systems and influenced cancer clinical features 111 .…”

Section: Resultsmentioning

confidence: 99%

Large-scale imputation models for multi-ancestry proteome-wide association analysis

Wu,

Zhang,

Yang

et al. 2023

Preprint

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Proteome-wide association studies (PWAS) decode the intricate proteomic landscape of biological mechanisms for complex diseases. Traditional PWAS model training relies heavily on individual-level reference proteomes, thereby restricting its capacity to harness the emerging summary-level protein quantitative trait loci (pQTL) data in the public domain. Here we introduced a novel framework to train PWAS models directly from pQTL summary statistics. By leveraging extensive pQTL data from the UK Biobank, deCODE, and ARIC studies, we applied our approach to train large-scale European PWAS models (totaln= 88,838 subjects). Furthermore, we developed PWAS models tailored for Asian and African ancestries by integrating multi-ancestry summary and individual-level data resources (totaln= 914 for Asian and 3,042 for African ancestries). We validated the performance of our PWAS models through a systematic multi-ancestry analysis of over 700 phenotypes across five major genetic data resources. Our results bridge the gap between genomics and proteomics for drug discovery, highlighting novel protein-phenotype links and their transferability across diverse ancestries. The developed PWAS models and data resources are freely available atwww.gcbhub.org.

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Section: Resultsmentioning

confidence: 99%

Large-scale imputation models for multi-ancestry proteome-wide association analysis

Wu,

Zhang,

Yang

et al. 2023

Preprint

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“…According to a study by Wei S and colleagues, elevated expression of Cathepsin F in non-small cell lung cancer brain metastasis tissues is correlated with poor progression-free survival and is an independent prognostic factor [56]. Additionally, compared to normal tissues, Cathepsin F is downregulated in non-small cell lung cancer samples, and high expression of Cathepsin F is associated with a favorable prognosis in non-small cell lung cancer [57]. Our data indicate that, in univariable MR analysis, Cathepsin F is a protective factor for the 28-day mortality rate of sepsis.…”

Section: Discussionmentioning

confidence: 99%

The Relationship between Cathepsins and Sepsis: A Mendelian Randomization Study

Zhou,

Liu,

Wen

et al. 2024

Preprint

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Background An increasing body of evidence suggests a connection between cathepsins and sepsis. However, more research is needed to better understand the causal relationship between the two. To gain a deeper insight into the association between cathepsins and sepsis, we conducted multiple Mendelian randomization (MR) analyses. Methods In this study, MR analysis was employed to investigate the causal associations between nine cathepsins and various severities of sepsis (sepsis, 28-day death, under 75). The research design consisted of forward MR analysis, reverse MR analysis and multivariable MR analysis. Under the premise of meeting the three fundamental assumptions of MR studies, genetic instrumental variables (IVs) were selected for specific analyses. The MR studies utilized the inverse variance-weighted (IVW) method as the primary analytical approach. Results In the forward MR analysis, the IVW method indicated that Cathepsin E and Cathepsin O are identified as potential risk factors for the occurrence of sepsis and sepsis (under 75). Additionally, a negative correlation was observed between Cathepsin F and 28-day mortality. Conversely, results from the reverse MR analysis suggested that the occurrence of sepsis might contribute to a reduction in the levels of Cathepsin Z. Within the multivariable MR analysis, incorporating nine cathepsins as covariates, Cathepsin O emerged as a risk factor for 28-day mortality. Interestingly, the multivariable MR analysis also affirmed that with an elevation in the levels of Cathepsin E, the risk of sepsis (under 75) increases. Conclusion Our MR study, for the first time from a genetic perspective, provides evidence of a causal relationship between cathepsins and the susceptibility to sepsis. This discovery holds significant implications for guiding clinical practices in the treatment of sepsis.

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“…In research done by Song and colleagues, the expression of CTSF in non-small cell lung cancer was evaluated and downregulated levels of CTST were observed in NSCLC samples despite normal tissues and good prognosis of NSCLC being correlated with high expression of CTSF. Besides using GeneMANIA, the gene-gene interaction network was established for CTSF and showed that CTSF had a similar function as CTSW genes [34]. A study on endometrial cancer reported that the CTSW gene had a positive correlation with tumor in ltration levels of B cells, CD8 + T cells, CD4 + T cells, macrophages, and dendritic cells [33].…”

Section: Discussionmentioning

confidence: 99%

Down regulation of Cathepsin W is associated with poor prognosis in Pancreatic cancer

Khojasteh-Leylakoohi

Mohit

Khalili-Tanha

et al. 2022

Preprint

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Background: Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on the identification of new biomarkers for the early diagnosis of PDAC and prediction of patient survival. Genome-wide RNA and microRNA sequencing were used using bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs) followed by validation in additional cohort of PDAC patients. Methods: genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA) to identify DEGs. We used Kaplan-Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest, (RF), Max Voting, Adaboost, Gradient boosting machines (GBM) and Extreme Gradient Boosting (XGB) techniques were used and Gradient boosting machines (GBM) were selected with 100 % accuracy for analysis. Moreover, protein-protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs and a combination of these obtained from machine learning algorithms and survival analysis. Results: Machine learning results showed 23 genes with negative regulation, 5 genes with positive regulation, 7 microRNAs with negative regulation and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of disease. In addition, the survival analysis results showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9 and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Conclusion: Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in pathogenesis of the diseases can be used for detection of patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC.

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Expression signature, prognosis value and immune characteristics of cathepsin F in non-small cell lung cancer identified by bioinformatics assessment

Cited by 18 publications

References 73 publications

Large-scale imputation models for multi-ancestry proteome-wide association analysis

Large-scale imputation models for multi-ancestry proteome-wide association analysis

The Relationship between Cathepsins and Sepsis: A Mendelian Randomization Study

Down regulation of Cathepsin W is associated with poor prognosis in Pancreatic cancer

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