“… 17 , 18 Figure 1 Predictive value and immunological correlations of SECTM1 in the PRJEB23709 cohort Reproduced with permission from iScience (Mei et al. 1 ). (A) SECTM1 expression levels in tumors from patients with different responses.…”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“… Figure 2 Predictive value and immunological correlations of SECTM1 in six cohorts Reproduced with permission from iScience (Mei et al. 1 ). (A–F) Comparison of predictive values of SECTM1, PD-L1, IFN-γ and the SECTM1/PD-L1 combination for immunotherapy responses in six cohorts.…”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“… Figure 3 Associations between SECTM1 expression and established immunotherapy biomarkers Reproduced with permission from iScience (Mei et al. 1 ). (A) Expression of SECTM1 in tumors with various PD-L1 IC score.…”
“… 17 , 18 Figure 1 Predictive value and immunological correlations of SECTM1 in the PRJEB23709 cohort Reproduced with permission from iScience (Mei et al. 1 ). (A) SECTM1 expression levels in tumors from patients with different responses.…”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“… Figure 2 Predictive value and immunological correlations of SECTM1 in six cohorts Reproduced with permission from iScience (Mei et al. 1 ). (A–F) Comparison of predictive values of SECTM1, PD-L1, IFN-γ and the SECTM1/PD-L1 combination for immunotherapy responses in six cohorts.…”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“… Figure 3 Associations between SECTM1 expression and established immunotherapy biomarkers Reproduced with permission from iScience (Mei et al. 1 ). (A) Expression of SECTM1 in tumors with various PD-L1 IC score.…”
“…A public dataset consisting of clinical and RNA-seq data from NSCLC patients undergoing immunotherapy was downloaded from the Gene Expression Omnibus (GEO) databases by following the links ( http://www.ncbi.nlm.nih.gov/geo/ ), namely the GSE126044 dataset.The tumor immune microenvironment (TIME) characteristics contained immunomodulators, tumor purity, infiltration levels of tumor-infiltrating immune cells (TIICs), and the expression of inhibitory immune checkpoint molecules. 16 , 17 First, we analyzed the relevance of immunomodulators to GBP5 in the TIME of NSCLC, comprising chemokines, major histocompatibility complex (MHC) molecules, receptors, immunoinhibitors, and immunostimulators. We also assessed the level of TIICs in NSCLC with two different methods: TIMER 18 and EPIC 19 algorithms.…”
Background
Immunotherapy drugs, immune checkpoint inhibitors (ICIs), have been approved for first- and second-line treatment of non-small cell lung cancer (NSCLC), but only a portion of patients respond to ICIs. It is crucial to screen the beneficiaries of immunotherapy through biomarkers accurately.
Methods
Several datasets were used to explore the predictive value for immunotherapy and immune relevance of guanylate binding protein 5 (GBP5) in NSCLC, including the GSE126044 dataset, The Cancer Genome Atlas (TCGA) dataset, Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset, the Kaplan–Meier plotter dataset, the HLuA150CS02 cohort, and the HLugS120CS01 cohort.
Results
GBP5 was upregulated in tumor tissues but associated with a good prognosis in NSCLC. Moreover, our findings demonstrated that GBP5 was strongly correlated with the expression of many immune-related genes, TIIC levels, and PD-L1 expression based on RNA-seq data onto online databases and validation of the NSCLC tissue microarray using IHC staining. Moreover, pan-cancer analysis has shown that GBP5 was a factor in identifying immuno-hot tumors, except for a few tumor types.
Conclusion
In summary, our current research suggests that GBP5 expression is a potential biomarker for predicting the outcome of NSCLC patients treated with ICIs. More research with large-scale samples is needed to determine their value as biomarkers of ICIs benefit.
Tuberculosis (TB) is a prevalent and severe infectious disease that poses a significant threat to human health. However, it is frequently disregarded as there are not enough quick and accurate ways to diagnose tuberculosis. Here, we develop a strategy for tuberculosis detection to address the challenges, including an experimental strategy, namely, Double Adapter Directional Capture sequencing (DADCSeq), an easily operated and low-cost whole transcriptome sequencing method, and a computational method to identify hub differentially expressed genes as well as the diagnosis of TB based on whole transcriptome data using DADCSeq on peripheral blood mononuclear cells (PBMCs) from active TB and latent TB or healthy control. Applying our approach to create a robust and stable TB multi-mRNA risk probability model (TBMMRP) that can accurately distinguish active and latent TB patients, including active TB and healthy controls in clinical cohorts, this diagnostic biomarker was successfully validated by several independent cross-platform cohorts with favorable performance in differentiating active TB from latent TB or active TB from healthy controls and further demonstrated superior or similar diagnostic accuracy compared to previous diagnostic markers. Overall, we develop a low-cost and effective strategy for tuberculosis diagnosis; as the clinical cohort increases, we can expand to different disease kinds and learn new features through our disease diagnosis strategy.
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