Development and validation of an individualized immune prognostic model in stage I-III lung squamous cell carcinoma.
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ABSTRACT:
SUBMITTER: Yang QF
PROVIDER: S-EPMC8209222 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Importance:The prevalence of early-stage non-small cell lung cancer (NSCLC) is expected to increase with recent implementation of annual screening programs. Reliable prognostic biomarkers are needed to identify patients at a high risk for recurrence to guide adjuvant therapy. Objective:To develop a robust, individualized immune signature that can estimate prognosis in patients with early-stage nonsquamous NSCLC. Design, Setting, and Participants:This retrospective study analyzed the gene expression profiles of frozen tumor tissue samples from 19 public NSCLC cohorts, including 18 microarray data sets and 1 RNA-Seq data set for The Cancer Genome Atlas (TCGA) lung adenocarcinoma cohort. Only patients with nonsquamous NSCLC with clinical annotation were included. Samples were from 2414 patients with nonsquamous NSCLC, divided into a meta-training cohort (729 patients), meta-testing cohort (716 patients), and 3 independent validation cohorts (439, 323, and 207 patients). All patients underwent surgery with a negative surgical margin, received no adjuvant or neoadjuvant therapy, and had publicly available gene expression data and survival information. Data were collected from July 22 through September 8, 2016. Main Outcomes and Measures:Overall survival. Results:Of 2414 patients (1205 men [50%], 1111 women [46%], and 98 of unknown sex [4%]; median age [range], 64 [15-90] years), a prognostic immune signature of 25 gene pairs consisting of 40 unique genes was constructed using the meta-training data set. In the meta-testing and validation cohorts, the immune signature significantly stratified patients into high- vs low-risk groups in terms of overall survival across and within subpopulations with stage I, IA, IB, or II disease and remained as an independent prognostic factor in multivariate analyses (hazard ratio range, 1.72 [95% CI, 1.26-2.33; P?<?.001] to 2.36 [95% CI, 1.47-3.79; P?<?.001]) after adjusting for clinical and pathologic factors. Several biological processes, including chemotaxis, were enriched among genes in the immune signature. The percentage of neutrophil infiltration (5.6% vs 1.8%) and necrosis (4.6% vs 1.5%) was significantly higher in the high-risk immune group compared with the low-risk groups in TCGA data set (P?<?.003). The immune signature achieved a higher accuracy (mean concordance index [C-index], 0.64) than 2 commercialized multigene signatures (mean C-index, 0.53 and 0.61) for estimation of survival in comparable validation cohorts. When integrated with clinical characteristics such as age and stage, the composite clinical and immune signature showed improved prognostic accuracy in all validation data sets relative to molecular signatures alone (mean C-index, 0.70 vs 0.63) and another commercialized clinical-molecular signature (mean C-index, 0.68 vs 0.65). Conclusions and Relevance:The proposed clinical-immune signature is a promising biomarker for estimating overall survival in nonsquamous NSCLC, including early-stage disease. Prospective studies are needed to test the clinical utility of the biomarker in individualized management of nonsquamous NSCLC.
| S-EPMC5710196 | biostudies-literature
Project description:Background: The immune system plays an important role in the development of lung squamous cell carcinoma (LUSC). Therefore, immune-related genes (IRGs) expression may be an important predictor of LUSC prognosis. However, a prognostic model based on IRGs that can systematically assess the prognosis of LUSC patients is still lacking. This study aimed to construct a LUSC immune-related prognostic model by using IRGs. Methods: Gene expression data about LUSC were obtained from The Cancer Genome Atlas (TCGA). Differential expression analysis and univariate Cox regression analysis were performed to identify prognostic differentially expressed IRGs. A prognostic model was constructed using the Lasso and multivariate Cox regression analyses. Then we validated the performance of the prognostic model in training and test cohorts. Furthermore, associations with clinical variables and immune infiltration were also analyzed. Results: 593 differentially expressed IRGs were identified, and 8 of them were related to prognosis. Then a transcription factor regulatory network was established. A prognostic model consisted of 4 immune-related genes was constructed by using Lasso and multivariate Cox regression analyses. The prognostic value of this model was successfully validated in training and test cohorts. Further analysis showed that the prognostic model could be used independently to predict the prognosis of LUSC patients. The relationships between the risk score and immune cell infiltration indicated that the model could reflect the status of the tumor immune microenvironment. Conclusions: We constructed a risk model using four PDIRGs that can accurately predict the prognosis of LUSC patients. The risk score generated by this model can be used as an independent prognostic indicator. Moreover, the model can predict the infiltration of immune cells in patients, which is conducive to the prediction of patient sensitivity to immunotherapy.
| S-EPMC7330657 | biostudies-literature
Project description:Lung Squamous Cell Carcinoma (LUSC) is an aggressive malignancy with limited therapeutic options. The response to immune therapy is a determining factor for the prognosis of LUSC patients. This study aimed to develop a reliable immune-related prognostic signature in LUSC. We extracted gene expression and clinical data of LUSC from The Cancer Genome Atlas (TCGA). A total of 502 patients enrolled and were divided into respond and non-responder groups by the TIDE algorithm. The CIBERSORT algorithm and the LM22 gene signature were used to analyze the distribution of immune cells in LUSC. Efficacy and response strength of immunotherapy are calculated by the tumor mutation burden (TMB) and ESTIMATE Score. Differentially expressed genes (DEGs) between the two groups were analyzed. The differential expression genes related to overall survival were pointed as hub DEGs, and a prognostic signature was constructed with lasso regression analysis. LUSC patients were divided into responder and non-responder groups based on the response to immunotherapy. The distribution of immune cells was significantly different between the two groups. Forty-four DGEs were considered as overall survival-related genes. A prognostic signature was constructed, consisting of 11 hub-DGEs, including MMP20, C18orf26, CASP14, FAM71E2, OPN4, CGB5, DIRC1, C9orf11, SPATA8, C9orf144B, and ZCCHC5. The signature can accurately distinguish LUSC patients into high and low-risk groups. Moreover, the high-risk group had a shorter survival time than the low-risk group. The area under the ROC curve was 0.67. The multivariate Cox regression showed that the risk score calculated by the constructed signature was an independent prognostic predictor for LUSC patients. In short, we established a novel immune-related prognostic signature in LUCS, which has significant sensitivity and accuracy in predicting the prognosis of patients. Our research can guide the evaluation of the prognosis of LUSC patients in clinical, and the discovered immune-related genes can provide a theoretical basis for the discovery of new therapeutic targets.
| S-EPMC9715950 | biostudies-literature
Project description:ObjectivesTo establish a pre-therapy prognostic index model (PIM) of the first-line chemotherapy aiming to achieve accurate prediction of time to progression (TTP) and overall survival among the patients diagnosed with locally advanced (stage III) or distant metastasis (stage IV) lung squamous cell carcinoma (LSCC).MethodsNinety-six LSCC patients treated with first-line chemotherapy were retrospectively enrolled to build the model. Fourteen epidermal growth factor receptor (EGFR)-mutant LSCC patients treated with first-line EGFR-tyrosine kinase inhibitor (TKI) therapy were enrolled for validation dataset. From CT images, 56,000 phenotype features were initially computed. PIM was constructed by integrating a CT phenotype signature selected by the least absolute shrinkage and selection operator and the significant blood-based biomarkers selected by multivariate Cox regression. PIM was then compared with other four prognostic models constructed by the CT phenotype signature, clinical factors, post-therapy tumor response, and Glasgow Prognostic Score.ResultsThe signature includes eight optimal features extracted from co-occurrence, run length, and Gabor features. By using PIM, chemotherapy efficacy of patients categorized in the low-risk, intermediate-risk, and high-risk progression subgroups (median TTP = 7.2 months, 3.4 months, and 1.8 months, respectively) was significantly different (p?<?0.0001, log-rank test). Chemotherapy efficacy of the low-risk progression subgroup was comparable with EGFR-TKI therapy (p?=?0.835, log-rank test). Prognostic prediction of chemotherapy efficacy by PIM was significantly higher than other models (p?<?0.05, z test).ConclusionThe study demonstrated that the PIM yielded significantly higher performance to identify individual stage III-IV LSCC patients who can potentially benefit most from first-line chemotherapy, and predict the risk of failure from chemotherapy for individual patients.Key points• TTP and OS of first-line chemotherapy in individual stage III-IV LSCC patients could be predicted by pre-therapy blood-based biomarkers and image-based signatures. • Risk status of pre-therapy indicators affected the efficacy of first-line chemotherapy in stage III-IV LSCC patients. • Those stage III-IV LSCC patients who were able to achieve similar efficacy to EGFR-TKI therapy through chemotherapy were identified.
| S-EPMC6443600 | biostudies-literature
Project description:BackgroundLung squamous cell carcinoma (LUSC), one of the main pathological types of lung cancer, has led to consequential socioeconomic burden. Ferroptosis is an iron-dependent form of cell death process with potentials for therapeutic target in various kinds of tumors. However, whether ferroptosis-related genes (FRGs) are associated with the prognosis of LUSC patients is still unclear. The aim of this study was to establish a FRGs-based signature which could stratify patients with LUSC.MethodsThe RNA sequencing profiles and corresponding clinical data of LUSC patients were retrieved from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) dataset. A FRG-based signature was developed using the TCGA-LUSC cohort and validated in the GEO cohort. Gene set enrichment analysis (GSEA) and analysis of immune cell characteristics were conducted to assess the relationship between FRGs and biological function or immune status. A nomogram based on selected clinical factors and the risk scores which were generated from the FRG-based signature was developed using the TCGA cohort and validated in the GEO cohort.ResultsA set of 16 FRGs, significantly associated with overall survival (OS) in the TCGA cohort, was identified and could classify LUSC patients into two risk groups. Kaplan-Meier analysis illustrated that the survival rate of the high-risk group was significantly lower than the low-risk group. Assessment and external validation of the signature showed that the survival predictive performance of this signature was adequate. Additionally, multiple pathways and functions were enriched through GSEA and the analysis of immune cell characteristics showed significantly different abundances of immune cells among the two risk groups. Finally, a nomogram integrating the FRG-based signature and selected clinical factors was also developed and assessed in both the TCGA and GEO cohort.ConclusionThis study indicated the association between the FRGs and prognosis of patients with LUSC. Targeting ferroptosis may serve as a novel potential therapeutic alternative for LUSC.
| S-EPMC8636213 | biostudies-literature
Project description:Papillary renal carcinoma (PRCC) is one of the important subtypes of kidney cancer, with a high degree of heterogeneity. At present, there is still a lack of robust and accurate biomarkers for the diagnosis, prognosis and treatment selection of PRCC. Considering the important role of tumor immunity in PRCC, we aim to construct a signature based on immune-related gene pairs (IRGPs) to estimate the prognostic of patients with PRCC. We obtained gene expression profiling and clinical information of patients with PRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), which were divided into discovery (n = 287) and validation (n = 28) cohorts, respectively. By univariate analysis, multivariate Cox analysis, and least absolute shrinkage and selection operator (Lasso) analysis, we selected 14 IRGPs with a panel of 22 unique genes to construct the prognostic signature. According to the signature, we stratified patients into high-risk group and low-risk group. In both discovery and validation cohorts, the results of Kaplan-Meier analysis showed that there were significant differences in OS between the two groups (p < 0.001). Combined with multiple clinical and pathological factors, the results of multivariate analyses confirmed that this signature was an independent predictor of OS (HR, 3.548; 95%CI, 2.096-6.006; p < 0.001). The results of immune infiltration analysis demonstrated that the abundance of multiple tumor-infiltration lymphocytes such as CD8 + T cells, Tregs, and T follicular cell helper were significantly higher in the high-risk group. Functional analysis showed that multiple immune-related signaling pathways were enriched in the high-risk group. In conclusion, we successfully established an individualized prognostic IRGPs signature, which can accurately assess and predict the OS of patients with PRCC.
| S-EPMC7680997 | biostudies-literature
Project description:BackgroundEarly-stage lung squamous cell carcinoma (LUSC) progression is accompanied by changes in immune microenvironments and the expression of immune-related genes (IRGs). Identifying innate IRGs associated with prognosis may improve treatment and reveal new immunotherapeutic targets.MethodsGene expression profiles and clinical data of early-stage LUSC patients were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases and IRGs from the InnateDB database. Univariate and multivariate Cox regression and LASSO regression analyses were performed to identify an innate IRG signature model prognostic in patients with early-stage LUSC. The predictive ability of this model was assessed by time-dependent receiver operator characteristic curve analysis, with the independence of the model-determined risk score assessed by univariate and multivariate Cox regression analyses. Overall survival (OS) in early-stage LUSC patients was assessed using a nomogram and decision curve analysis (DCA). Functional and biological pathways were determined by gene set enrichment analysis, and differences in biological functions and immune microenvironments between the high- and low-risk groups were assessed by ESTIMATE and the CIBERSORT algorithm.ResultsA signature involving six IRGs (SREBF2, GP2, BMX, NR1H4, DDX41, and GOPC) was prognostic of OS. Samples were divided into high- and low-risk groups based on median risk scores. OS was significantly shorter in the high-risk than in the low-risk group in the training (P < 0.001), GEO validation (P = 0.00021) and TCGA validation (P = 0.034) cohorts. Multivariate Cox regression analysis showed that risk score was an independent risk factor for OS, with the combination of risk score and T stage being optimally predictive of clinical benefit. GSEA, ESTIMATE, and the CIBERSORT algorithm showed that immune cell infiltration was higher and immune-related pathways were more strongly expressed in the low-risk group.ConclusionA signature that includes these six innate IRGs may predict prognosis in patients with early-stage LUSC.
| S-EPMC8643179 | biostudies-literature
Project description:BackgroundThe incidence of stage I and stage II lung adenocarcinoma (LUAD) is likely to increase with the introduction of annual screening programs for high-risk individuals. We aimed to identify a reliable prognostic signature with immune-related genes that can predict prognosis and help making individualized management for patients with early-stage LUAD.MethodsThe public LUAD cohorts were obtained from the large-scale databases including 4 microarray data sets from the Gene Expression Omnibus (GEO) and 1 RNA-seq data set from The Cancer Genome Atlas (TCGA) LUAD cohort. Only early-stage patients with clinical information were included. Cox proportional hazards regression model was performed to identify the candidate prognostic genes in GSE30219, GSE31210 and GSE50081 (training set). The prognostic signature was developed using the overlapped prognostic genes based on a risk score method. Kaplan-Meier curve with log-rank test and time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic value and performance of this signature, respectively. Furthermore, the robustness of this prognostic signature was further validated in TCGA-LUAD and GSE72094 cohorts.ResultsA prognostic immune signature consisting of 21 immune-related genes was constructed using the training set. The prognostic signature significantly stratified patients into high- and low-risk groups in terms of overall survival (OS) in training data set, including GSE30219 (HR = 4.31, 95% CI 2.29-8.11; P = 6.16E-06), GSE31210 (HR = 11.91, 95% CI 4.15-34.19; P = 4.10E-06), GSE50081 (HR = 3.63, 95% CI 1.90-6.95; P = 9.95E-05), the combined data set (HR = 3.15, 95% CI 1.98-5.02; P = 1.26E-06) and the validation data set, including TCGA-LUAD (HR = 2.16, 95% CI 1.49-3.13; P = 4.54E-05) and GSE72094 (HR = 2.95, 95% CI 1.86-4.70; P = 4.79E-06). Multivariate cox regression analysis demonstrated that the 21-gene signature could serve as an independent prognostic factor for OS after adjusting for other clinical factors. ROC curves revealed that the immune signature achieved good performance in predicting OS for early-stage LUAD. Several biological processes, including regulation of immune effector process, were enriched in the immune signature. Moreover, the combination of the signature with tumor stage showed more precise classification for prognosis prediction and treatment design.ConclusionsOur study proposed a robust immune-related prognostic signature for estimating overall survival in early-stage LUAD, which may be contributed to make more accurate survival risk stratification and individualized clinical management for patients with early-stage LUAD.
| S-EPMC7542703 | biostudies-literature
Project description:Head and neck squamous cell carcinoma (HNSCC) is among the most destructive of tumors, leading to considerable morbidity and mortality. Abnormal immune microenvironment is closely associated with tumor progression. This study aimed to construct a robust immune prognostic model for HNSCC. The RNA-seq transcriptome data and clinical information of HNSCC were downloaded from The Cancer Genome Atlas (TCGA) database. The key pathways and transcriptional factors (TFs) that are correlated with significantly altered immune related genes were identified. A robust immune prognostic model was constructed and further validated using a discovery-validation cohort design. An immune prognostic signature-based nomogram model was also developed. We have identified 400 significantly changed immune related genes in HNSCC. In addition, functional analysis of the altered immune related genes revealed many biological functions and pathways that might affect the tumor immune microenvironment. FOXP3, SNAI2, and STAT1 were identified as the hub TFs for regulating immunological changes in HNSCC. Moreover, an immune related gene-based prognostic signature significantly associated with the overall survival (OS) of HNSCC was constructed in the discovery cohort, and successfully validated in the validation cohort. Finally, a nomogram model based on immune prognostic signature was built and exhibited good performance for predicting the OS of HNSCC. In conclusion, the immune prognostic model is robust for predicting the prognosis of HNSCC and may evolve as a promising tool for risk evaluation and therapeutic selection.
| S-EPMC7667274 | biostudies-literature
Project description:BackgroundTumor immune infiltration is closely associated with clinical outcome in lung cancer. We aimed to develop an immune signature to improve the prognostic predictions of lung adenocarcinoma (LUAD).MethodsWe applied "Cell type Identification by Estimating Relative Subsets of RNA Transcripts" method to quantify the fraction of 22 leukocyte cells from six public microarray datasets. Four datasets from GPL570 were treated as the training cohort and two datasets from GPL96 and GPL10379 as the validation cohorts. An immune risk score (IRS) based on leukocyte cell fraction was established by least absolute shrinkage and selection operator cox regression model.ResultsIRS consisting of 6 types of leukocytes was constructed in the training dataset. In the training cohort (520 patients), the IRS stratified patients into high-IRS group (215 patients) and low-IRS group (305 patients) with significant differences in overall survival (OS) (HR: 2.77, 95% CI 2.08-3.06). Multivariate analysis including age, gender, stage, IRS and tumor purity revealed the IRS to be an independent prognostic factor in all datasets (training: HR: 10.71, 95% CI 5.72-20.07; validation-1: HR 2.68, 95% CI 1.15-6.27; validation-2: HR 3.71, 95% CI 1.33-10.33); all p < 0.05). IRS was significantly positively correlated to the expression levels of PD1, PDL1, CTLA and LAG3 (all p < 0.001). When integrated with clinical characteristics including stage and age, the composite immune and clinical signature presented with improved prognostic accuracy than IRS (mean C-index 0.66 vs. 0.60).ConclusionsThe proposed immune-clinical signature could predict OS in patients with LUAD effectively.
| S-EPMC7206750 | biostudies-literature