Tumor mutation burden (TMB)-associated signature constructed to predict survival of lung squamous cell carcinoma patients

Duan, Yan; Chen, Yi

doi:10.1038/s41598-021-88694-7

Cited by 17 publications

(12 citation statements)

References 43 publications

(31 reference statements)

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“…FAM107A (Family with sequence similarity 107, member A) was a candidate tumor suppressor gene. It was low expressed in laryngeal squamous cell carcinoma and participated in the occurrence of lung cancer ( Zhou et al, 2020 ; Yan and Chen, 2021 ). MAP4K1 (Mitogen-Activated Protein Kinase Kinase Kinase Kinase 1) was involved in promoting T-cell failure in multiple cancers, while it was associated with favorable prognosis of muscle-invasive bladder cancer ( van der Heijden et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

Immune Infiltration Characteristics and a Gene Prognostic Signature Associated With the Immune Infiltration in Head and Neck Squamous Cell Carcinoma

Zhu

Yang

et al. 2022

Front. Genet.

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Background: Immunotherapy has become the new standard of care for recurrent and metastatic head and neck squamous cell carcinoma (HNSCC), and PD-L1 is a widely used biomarker for immunotherapeutic response. However, PD-L1 expression in most cancer patients is low, and alternative biomarkers used to screen the population benefiting from immunotherapy are still being explored. Tumor microenvironment (TME), especially tumor immune-infiltrating cells, regulates the body’s immunity, affects the tumor growth, and is expected to be a promising biomarker for immunotherapy.Purpose: This article mainly discussed how the immune-infiltrating cell patterns impacted immunity, thereby affecting HNSCC patients’ prognosis.Method: The immune-infiltrating cell profile was generated by the CIBERSORT algorithm based on the transcriptomic data of HNSCC. Consensus clustering was used to divide groups with different immune cell infiltration patterns. Differentially expressed genes (DEGs) obtained from the high and low immune cell infiltration (ICI) groups were subjected to Kaplan–Meier and univariate Cox analysis. Significant prognosis-related DEGs were involved in the construction of a prognostic signature using multivariate Cox analysis.Results: In our study, 408 DEGs were obtained from high- and low-ICI groups, and 59 of them were significantly associated with overall survival (OS). Stepwise multivariate Cox analysis developed a 16-gene prognostic signature, which could distinguish favorable and poor prognosis of HNSCC patients. An ROC curve and nomogram verified the sensitivity and accuracy of the prognostic signature. The AUC values for 1 year, 2 years, and 3 years were 0.712, 0.703, and 0.700, respectively. TCGA-HNSCC cohort, GSE65858 cohort, and an independent GSE41613 cohort proved a similar prognostic significance. Notably, the prognostic signature distinguished the expression of promising immune inhibitory receptors (IRs) well and could predict the response to immunotherapy.Conclusion: We established a tumor immune cell infiltration (TICI)-based 16-gene signature, which could distinguish patients with different prognosis and help predict the response to immunotherapy.

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Section: Discussionmentioning

confidence: 99%

Immune Infiltration Characteristics and a Gene Prognostic Signature Associated With the Immune Infiltration in Head and Neck Squamous Cell Carcinoma

Zhu

Yang

et al. 2022

Front. Genet.

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“…It is a new genetic marker that can reflect the overall status of tumor gene mutations. TMB has been widely used in screening the benefit population from immune checkpoint inhibitor therapy and efficacy prediction for NSCLC patients [ 41 ]. It has also been studied in EGFR-mutated NSCLC patients [ 42 ].…”

Section: Pathological Featuresmentioning

confidence: 99%

Clinicopathologic Features and Molecular Biomarkers as Predictors of Epidermal Growth Factor Receptor Gene Mutation in Non-Small Cell Lung Cancer Patients

Liu

Xiong

2021

Current Oncology

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Lung cancer ranks first in the incidence and mortality of cancer in the world, of which more than 80% are non-small cell lung cancer (NSCLC). The majority of NSCLC patients are in stage IIIB~IV when they are admitted to hospital and have no opportunity for surgery. Compared with traditional chemotherapy, specific targeted therapy has a higher selectivity and fewer adverse reactions, providing a new treatment direction for advanced NSCLC patients. Tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR-TKIs) are the widely used targeted therapy for NSCLC patients. Their efficacy and prognosis are closely related to the mutation status of the EGFR gene. Clinically, detecting EGFR gene mutation is often limited by difficulty obtaining tissue specimens, limited detecting technology, and economic conditions, so it is of great clinical significance to find indicators to predict EGFR gene mutation status. Clinicopathological characteristics, tumor markers, liquid biopsy, and other predictors are less invasive, economical, and easier to obtain. They can be monitored in real-time, which is supposed to predict EGFR mutation status and provide guidance for the accurate, individualized diagnosis and therapy of NSCLC patients. This article reviewed the correlation between the clinical indicators and EGFR gene mutation status in NSCLC patients.

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“…The correlations between SPINK1 and the immune, stromal, and ESTIMATE scores were determined using the “estimate” package in R. Similarly, the expressions of several prominent immune checkpoints (ICPs) were determined as these important molecules are likely involved in the induction of ICB response ( 23 ). Tumor mutation burden (TMB), the number of mutations per million bases in tumor tissue, was used for assessing vulnerability to ICB immunotherapy, as TMB likely affects immune response ( 24 ).. The mutation data of patients with HCC were downloaded from TCGA, and TMB analysis was accomplished using the R package “maftools.” ( 25 ) The correlations among SPINK1 , ICPs, and TMB were investigated using statistical t -test.…”

Section: Methodsmentioning

confidence: 99%

Serine Protease Inhibitor Kazal Type 1, A Potential Biomarker for the Early Detection, Targeting, and Prediction of Response to Immune Checkpoint Blockade Therapies in Hepatocellular Carcinoma

Jia

Liu

et al. 2022

Front. Immunol.

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BackgroundWe aimed to characterize serine protease inhibitor Kazal type 1 (SPINK1) as a gene signature for the early diagnosis, molecular targeting, and prediction of immune checkpoint blockade (ICB) treatment response of hepatocellular carcinoma (HCC).MethodsThe transcriptomics, proteomics, and phenotypic analyses were performed separately or in combination.ResultsWe obtained the following findings on SPINK1. Firstly, in the transcriptomic training dataset, which included 279 stage I and II tumor samples (out of 1,884 stage I–IV HCC specimens) and 259 normal samples, significantly higher area under curve (AUC) values and increased integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were demonstrated for HCC discrimination in SPINK1-associated models compared with those of alpha-fetoprotein (AFP). The calibration of both SPINK1-related curves fitted significantly better than that of AFP. In the two independent transcriptomic validation datasets, which included 201, 103 stage I-II tumor and 192, 169 paired non-tumor specimens, respectively, the obtained results were consistent with the above-described findings. In the proteomic training dataset, which included 98 stage I and II tumor and 165 normal tissue samples, the analyses also revealed better AUCs and increased IDI and NRI in the aforementioned SPINK1-associated settings. A moderate calibration was shown for both SPINK1-associated models relative to the poor results of AFP. Secondly, in the in vitro and/or in vivo murine models, the wet-lab experiments demonstrated that SPINK1 promoted the proliferation, clonal formation, migration, chemoresistance, anti-apoptosis, tumorigenesis, and metastasis of HCC cells, while the anti-SPINK1 antibody inhibited the growth of the cells, suggesting that SPINK1 has “tumor marker” and “targetable” characteristics in the management of HCC. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that SPINK1 was engaged in immunity-related pathways, including T-cell activation. Thirdly, in the transcriptomic analyses of the 368 HCC specimens from The Cancer Genome Atlas (TCGA) cohort, the high abundance of SPINK1 was positively correlated with the high levels of activated tumor-infiltrating CD4+ and CD8+ T lymphocytes and dendritic and natural killer cells, while there were also positive correlations between SPINK1 and immune checkpoints, including PD-1, LAG-3, TIM-3, TIGIT, HAVCR2, and CTLA-4. The ESTIMATE algorithm calculated positive correlations between SPINK1 and the immune and ESTIMATE scores, suggesting a close correlation between SPINK1 and the immunogenic microenvironment within HCC tissues, which may possibly help in predicting the response of patients to ICB therapy.ConclusionsSPINK1 could be a potential biomarker for the early detection, targeted therapy, and prediction of ICB treatment response in the management of HCC.

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Tumor mutation burden (TMB)-associated signature constructed to predict survival of lung squamous cell carcinoma patients

Cited by 17 publications

References 43 publications

Immune Infiltration Characteristics and a Gene Prognostic Signature Associated With the Immune Infiltration in Head and Neck Squamous Cell Carcinoma

Immune Infiltration Characteristics and a Gene Prognostic Signature Associated With the Immune Infiltration in Head and Neck Squamous Cell Carcinoma

Clinicopathologic Features and Molecular Biomarkers as Predictors of Epidermal Growth Factor Receptor Gene Mutation in Non-Small Cell Lung Cancer Patients

Serine Protease Inhibitor Kazal Type 1, A Potential Biomarker for the Early Detection, Targeting, and Prediction of Response to Immune Checkpoint Blockade Therapies in Hepatocellular Carcinoma

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