Ideal prognostic model in lung squamous cell carcinoma.
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ABSTRACT:
SUBMITTER: Kinoshita T
PROVIDER: S-EPMC7354127 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
Publications
Ideal prognostic model in lung squamous cell carcinoma.
Translational lung cancer research 20200601 3
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Project description:Lung squamous cell carcinoma (LUSCC), as the major type of lung cancer, has high morbidity and mortality rates. The prognostic markers for LUSCC are much fewer than lung adenocarcinoma. Besides, protein biomarkers have advantages of economy, accuracy and stability. The aim of this study was to construct a protein prognostic model for LUSCC. The protein expression data of LUSCC were downloaded from The Cancer Protein Atlas (TCPA) database. Clinical data of LUSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 237 proteins were identified from 325 cases of LUSCC patients based on the TCPA and TCGA database. According to Kaplan-Meier survival analysis, univariate and multivariate Cox analysis, a prognostic prediction model was established which was consisted of 6 proteins (CHK1_pS345, CHK2, IRS1, PAXILLIN, BRCA2 and BRAF_pS445). After calculating the risk values of each patient according to the coefficient of each protein in the risk model, the LUSCC patients were divided into high risk group and low risk group. The survival analysis demonstrated that there was significant difference between these two groups (p= 4.877e-05). The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was 0.699, which suggesting that the prognostic risk model could effectively predict the survival of LUSCC patients. Univariate and multivariate analysis indicated that this prognostic model could be used as independent prognosis factors for LUSCC patients. Proteins co-expression analysis showed that there were 21 proteins co-expressed with the proteins in the risk model. In conclusion, our study constructed a protein prognostic model, which could effectively predict the prognosis of LUSCC patients.
| S-EPMC7645351 | 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:BackgroundLung cancer is one of the top causes of cancer-related death worldwide. Cellular senescence is a characteristic of cell cycle arrest that plays a role in carcinogenesis and immune microenvironment modulation. Despite this, the clinical and immune cell infiltration features of senescence in lung squamous cell carcinoma (LUSC) are unknown.MethodsThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were used to get RNA-seq data and clinical information for LUSC. The least absolute shrinkage and selection operator (LASSO)-Cox regression, receiver operating characteristic (ROC), and Kaplan-Meier analysis were used to evaluate a risk model for predicting overall survival based on six differentially expressed genes. The tumor microenvironment (TME) and immunotherapy response were also studied.ResultsTo discriminate LUSC into high- and low-risk subgroups, a risk model comprised of six cellular senescence-related genes (CDKN1A, CEBPB, MDH1, SIX1, SNAI1, and SOX5) was developed. The model could stratify patients into high-risk and low-risk groups, according to ROC and Kaplan-Meier analysis. In the TCGA-LUSC and GSE73403 cohorts, the high-risk group had a worse prognosis (P<0.05), and was associated with immune cell inactivation and being insensitive to immunotherapy in IMvigor210.ConclusionsWe discovered a new LUSC classification based on six cellular senescence-related genes, which will aid in identifying patients who will benefit from anti-PD-1 treatment. Targeting senescence-related genes appears to be another option for improving clinical therapy for LUSC.
| S-EPMC9712184 | biostudies-literature
Project description:BackgroundEsophageal squamous cell carcinoma (ESCC) has the highest mortality rates in China. The 5-year survival rate of ESCC remains dismal despite improvements in treatments such as surgical resection and adjuvant chemoradiation, and current clinical staging approaches are limited in their ability to effectively stratify patients for treatment options. The aim of the present study, therefore, was to develop an immunohistochemistry-based prognostic model to improve clinical risk assessment for patients with ESCC.MethodsWe developed a molecular prognostic model based on the combined expression of axis of epidermal growth factor receptor (EGFR), phosphorylated Specificity protein 1 (p-Sp1), and Fascin proteins. The presence of this prognostic model and associated clinical outcomes were analyzed for 130 formalin-fixed, paraffin-embedded esophageal curative resection specimens (generation dataset) and validated using an independent cohort of 185 specimens (validation dataset).ResultsThe expression of these three genes at the protein level was used to build a molecular prognostic model that was highly predictive of ESCC survival in both generation and validation datasets (P = 0.001). Regression analysis showed that this molecular prognostic model was strongly and independently predictive of overall survival (hazard ratio = 2.358 [95% CI, 1.391-3.996], P = 0.001 in generation dataset; hazard ratio = 1.990 [95% CI, 1.256-3.154], P = 0.003 in validation dataset). Furthermore, the predictive ability of these 3 biomarkers in combination was more robust than that of each individual biomarker.ConclusionsThis technically simple immunohistochemistry-based molecular model accurately predicts ESCC patient survival and thus could serve as a complement to current clinical risk stratification approaches.
| S-EPMC4143329 | biostudies-literature
Project description:BackgroundLung squamous cell carcinoma (LUSC), as the second frequent subtype of lung cancer, causes lots of mortalities primarily due to a lack of precise prognostic markers and timely treatment intervention. Previous studies have constructed several risk prognostic models based on DNA methylation sites in multiple tumors, whereas, DNA methylation signature of LUSC remains to be built, and its predictive value need to be evaluated.MethodsThe genome-wide DNA methylation data of LUSC samples was obtained from The Cancer Genome Atlas dataset. Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) were implemented to identify DNA methylation sites related to overall survival of LUSC patients. Thus, we performed multivariate Cox regression to establish a DNA methylation signature. The Kaplan-Meier (K-M) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted to estimate the prognostic power of the signature. Comparison with other known prognostic biomarkers, our DNA methylation signature showed higher predictive specificity and sensitivity. In addition, multivariate Cox regression screened out independent prognostic factors and constructed a nomogram.ResultsSeveral statistical methods were performed to construct an 11-DNA methylation signature. LUSC patients were divided into low- and high-risk group based on risk score, and high-risk group had a shorter survival time. According to the results of K-M and ROC analyses, the 11-DNA methylation signature showed significant sensitivity and specificity in predicting the LUSC patients' overall survival. Finally, we integrated some independent prognostic factors (risk score, metastasis stage, and tobacco smoking history) to construct a nomogram, which has excellent prognostic power and may provide guidance for the therapeutic strategies.ConclusionsWe constructed the first risk prognosis model based on DNA methylation site in LUSC, which showed better predictive ability. In addition, a nomogram integrating the DNA methylation signature, metastasis stage, and tobacco smoking history was developed.
| S-EPMC7330303 | biostudies-literature
Project description:Lung squamous cell carcinoma (LUSC) possesses a poor prognosis even for stages I-III resected patients. Reliable prognostic biomarkers that can stratify and predict clinical outcomes for stage I-III resected LUSC patients are urgently needed. Based on gene expression of LUSC tissue samples from five public datasets, consisting of 687 cases, we developed an immune-related prognostic model (IPM) according to immune genes from ImmPort database. Then, we comprehensively analyzed the immune microenvironment and mutation burden that are significantly associated with this model. According to the IPM, patients were stratified into high- and low-risk groups with markedly distinct survival benefits. We found that patients with high immune risk possessed a higher proportion of immunosuppressive cells such as macrophages M0, and presented higher expression of CD47, CD73, SIRPA, and TIM-3. Moreover, When further stratified based on the tumor mutation burden (TMB) and risk score, patients with high TMB and low immune risk had a remarkable prolonged overall survival compared to patients with low TMB and high immune risk. Finally, a nomogram combing the IPM with clinical factors was established to provide a more precise evaluation of prognosis. The proposed immune relevant model is a promising biomarker for predicting overall survival in stage I-III LUSC. Thus, it may shed light on identifying patient subset at high risk of adverse prognosis from an immunological perspective.
| S-EPMC8209222 | biostudies-literature
Project description:BackgroundCancer 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).MethodsThe 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.ResultsWe 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).ConclusionA 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.
| S-EPMC9178800 | biostudies-literature
Project description:BackgroundFor metachronous second pulmonary squamous cell carcinoma (msPSC) in patients with resected PSC, the method to distinguish tumour clonality has not yet been well established, which makes it difficult to determine accurate staging and predict prognosis.MethodsPatients who underwent surgery for first PSC and encountered msPSC were recruited from the Surveillance, Epidemiology, and End Results (SEER) database. We extracted overall survival 1 (OS1) for the first PSC, overall survival 2 (OS2) for msPSC, and interval survival for the time interval between the first and second PSC. The nomogram was calibrated for OS2, and recursive partitioning analysis (RPA) was performed for risk stratification.ResultsA total of 617 patients were identified. Several independent prognostic factors were identified and integrated into the nomogram for OS2, including gender, age (2nd), nodal status (1st), node metastasis (2nd), and extrapulmonary metastasis (2nd). The calibration curves showed optimal agreement between the predictions and actual observations, and the c-index was 0.678. Surgery was associated with longer survival for msPSC patients. The prognosis of sublobectomy was comparable and inferior to that of lobectomy in the low- and moderate-risk groups, respectively. Radiotherapy was associated with better outcomes in patients who did not undergo surgery.ConclusionsThe RPA-based clinical nomogram appears to be suitable for the prognostic prediction and risk stratification of OS2 in msPSC. This practical system may help clinicians make decisions and design clinical studies.
| S-EPMC8828508 | biostudies-literature
Project description:Background: Lung squamous cell carcinoma (LUSC) has been a highly malignant tumor with very poor prognosis. It is confirmed that pyroptosis refers to the deaths of cells in a programmed and inflammatory manner. Nevertheless, the correlation between expression of genes related with pyroptosis and their prognosis remains uncertain in LUSC. Methods: Utilization of The Cancer Genome Atlas (TCGA) cohort has been done for evaluating the prognostics of pyroptosis-related genes for survival and constructing a signature with multiple genes. The least absolute shrinkage and selection operator (LASSO) Cox regression was performed for establishing such pyroptosis-related gene signature. Results: Eventually, identification of 28 genes in relation to pyroptosis was made in LUSC and healthy lung tissues. Upon the basis of these differentially-expressed genes (DEGs), the patients of LUSC can be divided into two subtypes. Nine gene signatures were established using LASSO. The surviving rate for low-risk group was apparently greater in contrast with the high-risk group (p < .001). According to our finding, risk score worked as an independent predictive factor of OS among LUSC sufferers in combination with clinical characteristics. In line with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, the enrichment of immunity-related genes and decreasing immunity status among the high-risk group. Conclusion: Genes in relation with pyroptosis played an essential role in tumor immunity, which is capable of predicting the prognosis for LUSCs.
| S-EPMC8829032 | biostudies-literature
Project description:BackgroundRecurrence is a risk factor for the prognosis of lung squamous carcinoma (LUSC). DNA methylation levels of RNAs are also associated with LUSC prognosis. This study aimed to construct a prognostic model with high performance in predicting LUSC prognosis using the methylation levels of lncRNAs and genes.MethodsThe differentially expressed RNAs (DERs) and differentially methylated RNAs (DMRs) between the recurrent and non-recurrent LUSC tissues in The Cancer Genome Atlas (TCGA; training dataset) were identified. Weighted correlation network analysis was performed to identify co-methylation networks. Differentially methylated genes and lncRNAs with opposite expression-methylation levels were used for the screening of prognosis-associated RNAs. The prognostic model was constructed and its performance was validated in the GSE39279 dataset.ResultsA total of 664 DERs and 981 DMRs (including 972 genes) in recurrent LUSC tissues were identified. Three co-methylation modules, including 226 differentially methylated genes, were significantly associated with LUSC. Among prognosis-associated RNAs, 18 DERs/DMRs with opposite methylation-expression levels were included in the methylation prognostic risk model. LUSC patients with high risk scores had a poor prognosis compared with patients who had low risk scores (TCGA: HR = 3.856, 95% CI [2.297-6.471]; GSE39279: HR = 3.040, 95% CI [1.435-6.437]). This model had a high accuracy in predicting the prognosis (AUC = 0.903 and 0.800, respectively), equivalent to the nomogram model inclusive of clinical variables.ConclusionsReferring to the methylation levels of the 16-RNAs might help to predict the survival outcomes in LUSC.
| S-EPMC8958968 | biostudies-literature