Huili Chen’s research while affiliated with Bengbu Medical College and other places

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Publications (10)


Flowchart of the study’s workflow.
Single-cell transcriptome analysis of nucleotide metabolism. (A) Annotation results of single-cell subpopulations. (B) Heat map of marker genes in cellular subpopulations. (C) Volcano maps for differential analysis of subgroups. (D) Nucleotide metabolism levels in each cell type. (E) CopyKAT inference results of malignant cells in epithelial cells. (F) Distribution of nucleotide metabolism levels in normal and malignant cells. (G) Differences between the levels of nucleotide metabolism in normal and malignant cells. (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001).
Modeling prognosis related to nucleotide metabolism. (A) Prognosis-related nucleotide metabolism genes and their interactions in LUAD. (B) Lasso regression analysis and cross-validation. (C) Seven genes were selected for modeling through Stepcox regression analysis. (D) KM curve and timeROC curve for the TCGA-LUAD queue. (E) KM curve and timeROC curve for the GSE31210 queue. (F) KM curve and timeROC curve for the GSE50081 queue. (G) KM curve and timeROC curve for the GSE72094 queue. (H–K) Distribution of OS status, OS, risk scores in the TCGA and GEO cohorts, and heatmap of mRNA expression of the seven genes between the high NMBRS and low NMBRS groups.
Model comparison and clinicopathological analysis. (A–D) NMBRS compared with the C index of 11 LUAD-related studies in the TCGA-LUAD cohorts, the GSE31210 cohorts, the GSE50081 cohorts, and the GSE72094 cohorts. (E) Correlation between NMBRS and various clinicopathological factors. (F) Predictive performance of NMBRS across different clinicopathological factors. (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001).
NMBRS-related genetic alterations between the low and high NMBRS groups. (A) A waterfall map of the mutation status of somatic cells in the high-NMBRS group. (B) A waterfall map of the mutation status of somatic cells in the low-NMBRS group. (C) The Violin plot demonstrates differences in TMB scores for the high NMBRS and low NMBRS groups. (D) The correlation of NMBRS with TMB. (E) Combined TMB score and NMBRS risk score for Kaplan-Meier curve analysis of OS. (F) The Violin plot demonstrates differences in MATH scores for the high NMBRS and low NMBRS groups. (G) The correlation of NMBRS with MATH. (H) Combined MATH score and NMBRS risk score for Kaplan-Meier curve analysis of OS. (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001).

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Integration of single-cell transcriptomics and bulk transcriptomics to explore prognostic and immunotherapeutic characteristics of nucleotide metabolism in lung adenocarcinoma
  • Article
  • Full-text available

January 2025

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3 Reads

Kai Zhang

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Luyao Wang

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Huili Chen

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[...]

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Jing Zhang

Background Lung adenocarcinoma (LUAD) is a highly aggressive tumor with one of the highest morbidity and mortality rates in the world. Nucleotide metabolic processes are critical for cancer development, progression, and alteration of the tumor microenvironment. However, the effect of nucleotide metabolism on LUAD remains to be thoroughly investigated. Methods Transcriptomic and clinical data of LUAD were downloaded and organized from TCGA and GEO databases. Genes related to nucleotide metabolism were downloaded from the Msigdb database. Genes associated with LUAD prognosis were identified using univariate COX analysis, and a prognostic risk model was constructed using the machine learning combination of Lasso + Stepcox. The model’s predictive validity was evaluated using KM survival and timeROC curves. Based on the prognostic model, LUAD patients were classified into different nucleotide metabolism subtypes, and the differences between patients of different subtypes were explored in terms of genomic mutations, functional enrichment, tumor immune characteristics, and immunotherapy responses. Finally, the key gene SNRPA was screened, and a series of in vitro experiments were performed on LUAD cell lines to explore the role of SNRPA in LUAD. Result LUAD patients could be accurately categorized into subtypes based on the nucleotide metabolism-related prognostic risk score (NMBRS). There were significant differences in prognosis between patients of different subtypes, and the NMBRS showed high accuracy in predicting the prognosis of LUAD patients. In addition, patients of different subtypes showed significant differences in genomic mutation and functional enrichment and exhibited different anti-tumor immune profiles. Importantly, NMBRS can be used to predict the responsiveness of LUAD patients to immunotherapy. The results of in vitro cellular experiments indicate that SNRPA plays an important role in the development and progression of lung adenocarcinoma. Conclusion This study comprehensively reveals the prognostic value and clinical application of nucleotide metabolism in LUAD. A prognostic signature constructed based on genes related to nucleotide metabolism accurately predicted the prognosis of LUAD patients, and this signature can be used as a guide for LUAD immunotherapy.

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Fig. 1 WGCNA algorithm to identify centrosome-related genes. A Correlation analysis of module and centrosome scores. B Scatter plot between blue gene significance (GS) and module membership (MM). C Venn diagram of DEGs and blue module genes. D GO enrichment analysis of centrosome-related genes. E KEGG enrichment analysis of centrosome-related genes
Fig. 5 Mutational landscape of centrosome-related prognostic signature. A Differences in CRS scores and prognostic performance in different cancers. B Pathway enrichment analysis between tumor tissues of high CRS and low CRS groups. C Box-and-line graph plot demonstrating TMB differences between high and low risk groups. D Survival curves of LUAD patients between high and low TMB groups. E Waterfall plot demonstrating the mutational landscape between high and low risk groups. F Survival curve analysis of OS by combining TMB score and risk score. G Drug sensitivity analysis between high and low risk groups
Development of a novel centrosome-related risk signature to predict prognosis and treatment response in lung adenocarcinoma

November 2024

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5 Reads

Discover Oncology

Abnormalities of centrosomes, the major microtubular organizing centers of animal cells and regulators of cell cycle progression, usually accelerate tumor progression, but their prognostic value in lung adenocarcinoma (LUAD) remains insufficiently explored. We collected centrosome genes from the literature and identified LUAD-specific centrosome-related genes (CRGs) using the single-sample gene set enrichment analysis (ssGSEA) algorithm and weighted gene co-expression network analysis (WGCNA). Univariate Cox was performed to screen prognostic CRGs. Consistent clustering was performed to classify LUAD patients into two subgroups, and centrosome-related risk score signatures were constructed by Lasso and multivariate Cox regression to predict overall survival (OS). We further explored the correlation between CRS and patient prognosis, clinical manifestations, mutation status, tumor microenvironment, and response to different treatments. We constructed centrosome-associated prognostic features and verified that CRS could effectively predict 1-, 3-, and 5-year survival in LUAD patients. In addition, patients in the high-risk group exhibited elevated tumor mutational loads and reduced levels of immune infiltration, particularly of T and B cells. Patients in the high-risk group were resistant to immunotherapy and sensitive to 5-fluoropyrimidine and gefitinib. The key gene spermine synthase (SRM) is highly expressed at the mRNA and protein levels in LUAD. Our work develops a novel centrosome-related prognostic signature that accurately predicts OS in LUAD and can assist in clinical diagnosis and treatment.


Integration of the bulk transcriptome and single-cell transcriptome reveals efferocytosis features in lung adenocarcinoma prognosis and immunotherapy by combining deep learning

November 2024

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2 Reads

Cancer Cell International

Background Efferocytosis (ER) refers to the process of phagocytic clearance of programmed dead cells, and studies have shown that it is closely related to tumor immune escape. Methods This study was based on a comprehensive analysis of TCGA, GEO and CTRP databases. ER-related genes were collected from previous literature, univariate Cox regression was performed and consistent clustering was performed to categorize lung adenocarcinoma (LUAD) patients into two subgroups. Lasso regression and multivariate Cox regression analyses were used to construct ER-related prognostic features, and multiple immune infiltration algorithms were used to assess the correlation between the extracellular burial-related risk score (ERGRS) and tumor microenvironment (TME). And the key gene HAVCR1 was identified by deep learning, etc. Finally, pan-cancer analysis of the key genes was performed and in vitro experiments were conducted to verify the promotional effect of HAVCR1 on LUAD progression. Results A total of 33 ER-related genes associated with the prognosis of LUAD were identified, and the prognostic signature of ERGRS was successfully constructed to predict the overall survival (OS) and treatment response of LUAD patients. The high-risk group was highly enriched in some oncogenic pathways, while the low-ERGRS group was highly enriched in some immune-related pathways. In addition, the high ERGRS group had higher TMB, TNB and TIDE scores and lower immune scores. The low-risk group had better immunotherapeutic response and less likelihood of immune escape. Drug sensitivity analysis revealed that BRD-K92856060, monensin and hexaminolevulinate may be potential therapeutic agents for the high-risk group. And ERGRS was validated in several cohorts. In addition, HAVCR1 is one of the key genes, and knockdown of HAVCR1 in vitro significantly reduced the proliferation, migration and invasion ability of lung adenocarcinoma cells. Conclusion Our study developed a novel prognostic signature of efferocytosis-related genes. This prognostic signature accurately predicted survival prognosis as well as treatment outcome in LUAD patients and explored the role of HAVCR1 in lung adenocarcinoma progression.


Roburic acid inhibits lung cancer metastasis and triggers autophagy as verified by network pharmacology, molecular docking techniques and experiments

October 2024

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32 Reads

Background Roburic acid (ROB) is a newly discovered tetracyclic triterpene acid extracted from oak galls, which has anti-inflammatory effects, but the mechanism of its anticancer effect is not clear. Our study focuses on exploring the potential mechanism of action of ROB in the treatment of lung cancer using a combination of network pharmacological prediction, molecular docking technique and experimental validation. Methods A network pharmacology approach was used to screen the protein targets of ROB and lung cancer, and PPI network analysis and enrichment analysis were performed on the intersecting genes. The tissue and organ distribution of the targets was also evaluated based on the BioGPS database. To ensure the reliability of the network pharmacology prediction results, we proceeded to use molecular docking technique to determine the relationship between drugs and targets. Finally, in vitro experiments with cell lines were performed to further reveal the potential mechanism of ROB for the treatment of lung cancer. Results A total of 83 potential targets of ROB in lung cancer were collected and further screened by using Cytoscape software, and 7 targets of PTGS2, CYP19A1, PTGS1, AR, CYP17A1, PTGES and SRD5A1 were obtained as hub genes and 7 hub targets had good binding energy with ROB. GO and KEGG analysis showed that ROB treatment of lung cancer mainly involves Arachidonic acid metabolism, Notch signaling pathway, cancer pathway and PPAR signaling pathway. The results of in vitro experiments indicated that ROB may inhibit the proliferation and metastasis of lung cancer cells and activate the PPARγ signaling pathway, as well as induce cellular autophagy. Conclusions The results of this study comprehensively elucidated the potential targets and molecular mechanisms of ROB for the treatment of lung cancer, providing new ideas for further lung cancer therapy.


Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort

September 2024

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27 Reads

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1 Citation

Introduction The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies are needed to investigate the prognostic significance of the interaction between immune-related genes and cell death in LUAD. Methods In this study, 10 clustering algorithms were applied to perform molecular typing based on cell death-related genes, immune-related genes, methylation data and somatic mutation data. And a powerful computational framework was used to investigate the relationship between immune genes and cell death patterns in LUAD patients. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations, and we constructed an immune-associated programmed cell death model (PIGRS) using the machine learning model that exhibited the best performance. Finally, based on a series of in vitro experiments used to explore the role of PSME3 in LUAD. Results We used 10 clustering algorithms and multi-omics data to categorize TCGA-LUAD patients into three subtypes. patients with the CS3 subtype had the best prognosis, whereas patients with the CS1 and CS2 subtypes had a poorer prognosis. PIGRS, a combination of 15 high-impact genes, showed strong prognostic performance for LUAD patients. PIGRS has a very strong prognostic efficacy compared to our collection. In conclusion, we found that PSME3 has been little studied in lung adenocarcinoma and may be a novel prognostic factor in lung adenocarcinoma. Discussion Three LUAD subtypes with different molecular features and clinical significance were successfully identified by bioinformatic analysis, and PIGRS was constructed using a powerful machine learning framework. and investigated PSME3, which may affect apoptosis in lung adenocarcinoma cells through the PI3K/AKT/Bcl-2 signaling pathway.


Identification of a novel immunogenic death-associated model for predicting the immune microenvironment in lung adenocarcinoma from single-cell and Bulk transcriptomes

August 2024

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5 Reads

Journal of Cancer

Background: Studies on immunogenic death (ICD) in lung adenocarcinoma are limited, and this study aimed to determine the function of ICD in LUAD and to construct a novel ICD-based prognostic model to improve immune efficacy in lung adenocarcinoma patients. Methods: The data for lung adenocarcinoma were obtained from the Cancer Genome Atlas (TCGA) database and the National Center for Biotechnology Information (GEO). The single-cell data were obtained from Bischoff P et al. To identify subpopulations, we performed descending clustering using TSNE. We collected sets of genes related to immunogenic death from the literature and identified ICD-related genes through gene set analysis of variance (GSVA) and weighted gene correlation network analysis (WGCNA). Lung adenocarcinoma patients were classified into two types using consistency clustering. The difference between the two types was analyzed to obtain differential genes. An immunogenic death model (ICDRS) was established using LASSO-Cox analysis and compared with lung adenocarcinoma models of other individuals. External validation was performed in the GSE31210 and GSE50081 cohorts. The efficacy of immunotherapy was assessed using the TIDE algorithm and the IMvigor210, GSE78220, and TCIA cohorts. Furthermore, differences in mutational profiles and immune microenvironment between different risk groups were investigated. Subsequently, ROC diagnostic curves and KM survival curves were used to screen ICDRS key regulatory genes. Finally, RT-qPCR was used to verify the differential expression of these genes. Results: Eight ICD genes were found to be highly predictive of LUAD prognosis and significantly correlated with it. Multivariate analysis showed that patients in the low-risk group had a higher overall survival rate than those in the high-risk group, indicating that the model was an independent predictor of LUAD. Additionally, ICDRS demonstrated better predictive ability compared to 11 previously published models. Furthermore, significant differences in biological function and immune cell infiltration were observed in the tumor microenvironment between the high-risk and low-risk groups. It is noteworthy that immunotherapy was also significant in both groups. These findings suggest that the model has good predictive efficacy. Conclusions: The ICD model demonstrated good predictive performance, revealing the tumor microenvironment and providing a new method for evaluating the efficacy of pre-immunization. This offers a new strategy for future treatment of lung adenocarcinoma.


Tuberculosis to lung cancer: application of tuberculosis signatures in identification of lung adenocarcinoma subtypes and marker screening

August 2024

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12 Reads

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1 Citation

Journal of Cancer

Background: There is an association between LUAD and TB, and TB increases the risk of lung adenocarcinogenesis. However, the role of TB in the development of lung adenocarcinoma has not been clarified. Methods: DEGs from TB and LUAD lung samples were obtained to identify TB-LUAD-shared DEGs. Consensus Clustering was performed on the TCGA cohort to characterize unique changes in TB transcriptome-derived lung adenocarcinoma subtypes. Prognostic models were constructed based on TB signatures to explore the characterization of subgroups. Finally, experimental validation and single-cell analysis of potential markers were performed. Results: We characterized three molecular subtypes with unique clinical features, cellular infiltration, and pathway change manifestations. We constructed and validated TB-related Signature in six cohorts. TB-related Signature has characteristic alterations, and can be used as an effective predictor of immunotherapy response. Prognostically relevant novel markers KRT80, C1QTNF6, and TRPA1 were validated by RT-qPCR. The association between KRT80 and lung adenocarcinoma disease progression was verified in Bulk transcriptome and single-cell transcriptome. Conclusion: For the first time, a comprehensive bioinformatics analysis of tuberculosis signatures was used to identify subtypes of lung adenocarcinoma. The TB-related Signature predicted prognosis and identified potential markers. This result reveals a potential pathogenic association of tuberculosis in the progression of lung adenocarcinoma.


Identification and validation of tryptophan-related gene signatures to predict prognosis and immunotherapy response in lung adenocarcinoma reveals a critical role for PTTG1

July 2024

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6 Reads

Introduction Tryptophan metabolism is strongly associated with immunosuppression and may influence lung adenocarcinoma prognosis as well as tumor microenvironment alterations. Methods Sequencing datasets were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Two different clusters were identified by consensus clustering, and prognostic models were established based on differentially expressed genes (DEGs) in the two clusters. We investigated differences in mutational landscapes, enrichment pathways, immune cell infiltration, and immunotherapy between high- and low-risk scoring groups. Single-cell sequencing data from Bischoff et al. were used to identify and quantify tryptophan metabolism, and model genes were comprehensively analyzed. Finally, PTTG1 was analyzed at the pan-cancer level by the pan-TCGA cohort. Results Risk score was defined as an independent prognostic factor for lung adenocarcinoma and was effective in predicting immunotherapy response in patients with lung adenocarcinoma. PTTG1 is one of the key genes, and knockdown of PTTG1 in vitro decreases lung adenocarcinoma cell proliferation and migration and promotes apoptosis and down-regulation of tryptophan metabolism regulators in lung adenocarcinoma cells. Discussion Our study revealed the pattern and molecular features of tryptophan metabolism in lung adenocarcinoma patients, established a model of tryptophan metabolism-associated lung adenocarcinoma prognosis, and explored the roles of PTTG1 in lung adenocarcinoma progression, EMT process, and tryptophan metabolism.


Identification of T-cell exhaustion-related genes and prediction of their immunotherapeutic role in lung adenocarcinoma

February 2024

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6 Reads

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3 Citations

Journal of Cancer

Background: Lung adenocarcinoma ranks as the second most widespread form of cancer globally, accompanied by a significant mortality rate. Several studies have shown that T cell exhaustion is associated with immunotherapy of tumours. Consequently, it is essential to comprehend the possible impact of T cell exhaustion on the tumor microenvironment. The purpose of this research was to create a TEX-based model that would use single-cell RNA-seq (scRNA-seq) and bulk-RNA sequencing to explore new possibilities for assessing the prognosis and immunotherapeutic response of LUAD patients. Methods: RNA-seq data from LUAD patients was downloaded from the Cancer Genome Atlas (TCGA) database and the National Center for Biotechnology Information (GEO). 10X scRNA sequencing data, as reported by Bischoff P et al., was utilized for down-sampling clustering and subgroup identification using TSNE. TEX-associated genes were identified through gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). We utilized LASSO-Cox analysis to establish predicted TEX features. External validation was conducted in GSE31210 and GSE30219 cohorts. Immunotherapeutic response was assessed in IMvigor210, GSE78220, GSE35640 and GSE100797 cohorts. Furthermore, we investigated differences in mutational profiles and immune microenvironment between various risk groups. We then screened TEXRS key regulatory genes using ROC diagnostic curves and KM survival curves. Finally, we verified the differential expression of key regulatory genes through RT-qPCR. Results: Nine TEX genes were identified as highly predictive of LUAD prognosis and strongly correlated with disease outcome. Univariate and multivariate analysis revealed that patients in the low-risk group had significantly better overall survival rates compared with those in the high-risk group, highlighting the model's ability to independently predict LUAD prognosis. Our analysis revealed significant variation in the biological function, mutational landscape, and immune cell infiltration within the tumor microenvironment of both high-risk and low-risk groups. Additionally, immunotherapy was found to have a significant impact on both groups, indicating strong predictive efficacy of the model. Conclusions: The TEX model showed good predictive performance and provided a new perspective for evaluating the efficacy of preimmunization, which provides a new strategy for the future treatment of lung adenocarcinoma.


Construction of a prognostic model for lung adenocarcinoma tumor endothelial cells and prediction of immunotherapy based on single-cell transcriptome and Bulk transcriptome

January 2024

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18 Reads

Background: Lung adenocarcinoma (LUAD) is a common histologic subtype of lung cancer with high morbidity and mortality. Tumor endothelial cells (TEC) are associated with tumor progression and metastasis. In this study, we explored the effect of TEC on prognosis and immunotherapy of LUAD based on single-cell transcriptome and Bulk transcriptome. To help lung adenocarcinoma patients obtain accurate clinical treatment strategies. Methods: We identified TEC marker genes by single-cell transcriptome in this study. LUAD data were downloaded from The Cancer Genome Atlas(TCGA) and Gene Expression Omnibus(GEO) databases, and prognostic models of TEC marker genes were constructed using Lasso-Cox analysis in the TCGA cohort and externally validated in the GEO cohort. Differences in the immune microenvironment between high and low-risk groups were analyzed using the ESTIMATE and six immune cell infiltration algorithms. Using the TIDE algorithm, the IMvigor210, GSE78220, and Whijae Roh et al. cohorts were used to predict the outcome of immunotherapy in patients in different risk groups. In addition, differences in functional enrichment analysis and genomic mutations between high and low-risk groups were investigated. Finally, core genes were screened using differential and survival analyses, and RT-qPCR verified their expression. Results: The results showed that the prognostic model constructed based on TEC marker genes could categorize LUAD patients into two groups, and there was a significant difference in survival time between the two groups. In addition, we found significant differences between the high- and low-risk groups in terms of biological functions, genomic mutations, immune cell infiltration, immune characteristics, and chemotherapeutic drug sensitivity. Notably, patients in the low-risk group showed better immunotherapy response. Finally, the results of RT-qPCR experiments were consistent with the bioinformatics analysis. Conclusion: In this study, we developed a new TEC marker gene-based signature that effectively stratifies LUAD patients and has a strong efficacy in predicting the prognosis of LUAD patients and immunotherapy.

Citations (3)


... In this study, 10 machine learning algorithms, including both individual and ensemble approaches, were chosen according to their performance to build the prediction model. Numerous studies have proved the feasibility and broad use of these methods [41][42][43][44] . For example, Xie et al. identified 15 high-impact genes using 10 clustering algorithms, namely SVM, Lasso, GBM, RSF, Enet, Stepwise Cox, Ridge, CoxBoost, SuperPC, and PplsRcox. ...

Reference:

Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma
Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort

... In lung cancer, TRPA1 expression positively correlates with higher cancer stages and metastases and indicates higher risk. These results align with a previous study showing that TRPA1 could be a novel marker for tuberculosis-related lung adenocarcinoma [20]. The analysis of tumour infiltration reveals that TRPA1 is highly expressed in the tumour environment, and higher expression in B cells improves overall survival. ...

Tuberculosis to lung cancer: application of tuberculosis signatures in identification of lung adenocarcinoma subtypes and marker screening

Journal of Cancer

... Given these circumstances, generating prognostic models based on key biomarkers that can predict patient survival, immune microenvironmental status, gene mutations, and potential drug sensitivities has great potential for improving the accuracy of LUAD therapy. For example, Xie.et.al studied G protein-coupled receptor-related models [53] and Lian.et.al identified T cell exhaustion related models [54]. Each has provided new insights into the treatment of LUAD patients. ...

Identification of T-cell exhaustion-related genes and prediction of their immunotherapeutic role in lung adenocarcinoma

Journal of Cancer