Identification of an Individualized Immune-Related Prognostic Risk Score in Lung Squamous Cell Cancer

Zhuang, Yuan; Li, Sihan; Liu, Chang; Li, Guang

doi:10.3389/fonc.2021.546455

Cited by 11 publications

(8 citation statements)

References 42 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, the gene signal based on abnormal mRNA has been used to predict the prognosis of hepatocellular carcinoma, which has great potential ( 19 , 20 ). Gene signatures based on immune-related genes have been reported in a variety of cancers, such as lung squamous cell cancer, non-small cell lung cancer, esophageal carcinoma, etc ( 13 , 21 , 22 ). In this study, the high-throughput expression profiles of immune-related genes in TCGA-LIHC cohort were analyzed, and the differentially expressed immune genes were screened by differential analysis.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, due to the maturity of high-throughput sequencing technology, a series of changes in tumor tissue genome and normal tissue genome have been excavated. Several studies in different types of cancer, including ovarian cancer, cervical cancer and lung squamous cell cancer (11)(12)(13), immune-related genes were used to establish tumor prognostic models, which further reflects the potential of immune-related genes to become tumor prognostic markers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification, Verification and Pathway Enrichment Analysis of Prognosis-Related Immune Genes in Patients With Hepatocellular Carcinoma

Zhu

Song

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

Hepatocellular carcinoma is a common malignant tumor with poor prognosis, poor treatment effect, and lack of effective biomarkers. In this study, bioinformatics analysis of immune-related genes of hepatocellular carcinoma was used to construct a multi-gene combined marker that can predict the prognosis of patients. The RNA expression data of hepatocellular carcinoma were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the IMMPORT database. Differential analysis was performed by Wilcox test to obtain differentially expressed genes. Univariate Cox regression analysis, lasso regression analysis and multivariate Cox regression analysis were performed to establish a prognostic model of immune genes, a total of 5 genes (HDAC1, BIRC5, SPP1, STC2, NR6A1) were identified to construct the models. The expression levels of 5 genes in HCC tissues were significantly different from those in paracancerous tissues. The Kaplan-Meier survival curve showed that the risk score calculated according to the prognostic model was significantly related to the overall survival (OS) of HCC. The receiver operating characteristic (ROC) curve confirmed that the prognostic model had high accuracy. Independent prognostic analysis was performed to prove that the risk value can be used as an independent prognostic factor. Then, the gene expression data of hepatocellular carcinoma in the ICGC database was used as a validation data set for the verification of the above steps. In addition, we used the CIBERSORT software and TIMER database to conduct immune infiltration research, and the results showed that the five genes of the model and the risk score have a certain correlation with the content of immune cells. Moreover, through Gene Set Enrichment Analysis (GSEA) and the construction of protein interaction networks, we found that the p53-mediated signal transduction pathway is a potentially important signal pathway for hepatocellular carcinoma and is positively regulated by certain genes in the prognostic model. In conclusion, this study provides potential targets for predicting the prognosis and treatment of hepatocellular carcinoma patients, and also provides new ideas about the correlation between immune genes and potential pathways of hepatocellular carcinoma.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification, Verification and Pathway Enrichment Analysis of Prognosis-Related Immune Genes in Patients With Hepatocellular Carcinoma

Zhu

Song

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…Moreover, AMH is an immune-related prognostic gene in LUSC and has been used to construct a prognostic model in LUSC [ 31 ]. Zhuang et al also found that AMH is a LUSC-related immune gene and is not associated with distant metastasis [ 32 ]. TWIK-related acid-sensitive potassium channel 1 (TASK1), encoded by KCNK3, is associated with pulmonary circulation and controls pulmonary arterial tone, which may contribute to poor prognosis in lung cancer patients [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

Characterization of a prognostic model for lung squamous cell carcinoma based on eight stemness index-related genes

Jiang

Xie

2022

BMC Pulm Med

View full text Add to dashboard Cite

Background Cancer stem cells (CSCs) are implicated in cancer progression, chemoresistance, and poor prognosis; thus, they may be promising therapeutic targets. In this study, we aimed to investigate the prognostic application of differentially expressed CSC-related genes in lung squamous cell carcinoma (LUSC). Methods The mRNA stemness index (mRNAsi)-related differentially expressed genes (DEGs) in tumors were identified and further categorized by LASSO Cox regression analysis and 1,000-fold cross-validation, followed by the construction of a prognostic score model for risk stratification. The fractions of tumor-infiltrating immune cells and immune checkpoint genes were analyzed in different risk groups. Results We found 404 mRNAsi-related DEGs in LUSC, 77 of which were significantly associated with overall survival. An eight-gene prognostic signature (PPP1R27, TLX2, ANKLE1, TIGD3, AMH, KCNK3, FLRT3, and PPBP) was identified and used to construct a risk score model. The TCGA set was dichotomized into two risk groups that differed significantly (p = 0.00057) in terms of overall survival time (1, 3, 5-year AUC = 0.830, 0.749, and 0.749, respectively). The model performed well in two independent GEO datasets (p = 0.029, 0.033; 1-year AUC = 0747, 0.783; 3-year AUC = 0.746, 0.737; 5-year AUC = 0.706, 0.723). Low-risk patients had markedly increased numbers of CD8+ T cells and M1 macrophages and downregulated immune checkpoint genes compared to the corresponding values in high-risk patients (p < 0.05). Conclusion A stemness-related prognostic model based on eight prognostic genes in LUSC was developed and validated. The results of this study would have prognostic and therapeutic implications.

show abstract

“…developed TIL-associated IncRNA signature for predicting 3-year OS and yielded an AUC of 0.646 ( 22 ), and Zhuang et al. developed immune gene risk index for predicting 3-year OS with an AUC of 0.666 ( 23 ). However, no individual prognostic models in those study were developed to predict the individualized survival outcomes of NSCLC patients.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of an Immune-Based Prognostic Risk Score for Patients With Resected Non-Small Cell Lung Cancer

et al. 2022

View full text Add to dashboard Cite

BackgroundDespite the well-known role of immunoscore, as a prognostic tool, that appeared to be superior to tumor–node–metastasis (TNM) staging system, no prognostic scoring system based on immunohistochemistry (IHC) staining digital image analysis has been established in non-small cell lung cancer (NSCLC). Hence, we aimed to develop and validate an immune-based prognostic risk score (IMPRS) that could markedly improve individualized prediction of postsurgical survival in patients with resected NSCLC.MethodsIn this retrospective study, complete resection of NSCLC (stage I–IIIA) was performed for two independent patient cohorts (discovery cohort, n=168; validation cohort, n=115). Initially, paraffin-embedded resected specimens were stained by immunohistochemistry (IHC) of three immune cell types (CD3+, CD4+, and CD8+ T cells), and a total of 5,580 IHC-immune features were extracted from IHC digital images for each patient by using fully automated pipeline. Then, an IHC-immune signature was constructed with selected features using the LASSO Cox analysis, and the association of signature with patients’ overall survival (OS) was analyzed by Kaplan–Meier method. Finally, IMPRS was established by incorporating IHC-immune signature and independent clinicopathological variables in multivariable Cox regression analysis. Furthermore, an external validation cohort was included to validate this prognostic risk score.ResultsEight key IHC-immune features were selected for the construction of IHC-immune signature, which showed significant associations with OS in all cohorts [discovery: hazard ratio (HR)=11.518, 95%CI, 5.444–24.368; validation: HR=2.664, 95%CI, 1.029–6.896]. Multivariate analyses revealed IHC-immune signature as an independent prognostic factor, and age, T stage, and N stage were also identified and entered into IMPRS (all p<0.001). IMPRS had good discrimination ability for predicting OS (C-index, 0.869; 95%CI, 0.861–0.877), confirmed using external validation cohort (0.731, 0.717–0.745). Interestingly, IMPRS had better prognostic value than clinicopathological-based model and TNM staging system termed as C-index (clinicopathological-based model: 0.674; TNM staging: 0.646, all p<0.05). More importantly, decision curve analysis showed that IMPRS had adequate performance for predicting OS in resected NSCLC patients.ConclusionsOur findings indicate that the IMPRS that we constructed can provide more accurate prognosis for individual prediction of OS for patients with resected NSCLC, which can help in guiding personalized therapy and improving outcomes for patients.

show abstract

Identification of an Individualized Immune-Related Prognostic Risk Score in Lung Squamous Cell Cancer

Cited by 11 publications

References 42 publications

Identification, Verification and Pathway Enrichment Analysis of Prognosis-Related Immune Genes in Patients With Hepatocellular Carcinoma

Identification, Verification and Pathway Enrichment Analysis of Prognosis-Related Immune Genes in Patients With Hepatocellular Carcinoma

Characterization of a prognostic model for lung squamous cell carcinoma based on eight stemness index-related genes

Development and Validation of an Immune-Based Prognostic Risk Score for Patients With Resected Non-Small Cell Lung Cancer

Contact Info

Product

Resources

About