Similar Datasets
Project description:Single nuclei RNA analysis of adjacent normal tissue to predict recurrence in lung cancer patients
2023-09-08 | GSE229253 | GEO
Project description:Tumor-adjacent lung transcriptome as a predictor of recurrence in early-stage lung adenocarcinoma [bulk RNA-seq]
| PRJNA955664 | ENA
Project description:Tumor-adjacent lung transcriptome as a predictor of recurrence in early-stage lung adenocarcinoma [snRNA-seq]
| PRJNA953640 | ENA
Project description:PurposeRecent researches showed the vital role of BACH1 in promoting the metastasis of lung cancer. We aimed to explore the value of BACH1 in predicting the overall survival (OS) of early-stage (stages I-II) lung adenocarcinoma. Patients and Methods. Lung adenocarcinoma cases were screened from the Cancer Genome Atlas (TCGA) database. Functional enrichment analysis was performed to obtain the biological mechanisms of BACH1. Gene set enrichment analysis (GSEA) was performed to identify the difference of biological pathways between high- and low-BACH1 groups. Univariate and multivariate COX regression analysis had been used to screen prognostic factors, which were used to establish the BACH1 expression-based prognostic model in the TCGA dataset. The C-index and time-dependent AUC curve were used to evaluate predictive power of the model. External validation of prognostic value was performed in two independent datasets from Gene Expression Omnibus (GEO). Decision analysis curve was finally used to evaluate clinical usefulness of the BACH1-based model beyond pathologic stage alone.ResultsBACH1 was an independent prognostic factor for lung adenocarcinoma. High-expression BACH1 cases had worse OS. BACH1-based prognostic model showed an ideal C-index and t-AUC and validated by two GEO datasets, independently. More importantly, the BACH1-based model indicated positive clinical applicability by DCA curves.ConclusionOur research confirmed that BACH1 was an important predictor of prognosis in early-stage lung adenocarcinoma. The higher the expression of BACH1, the worse OS of the patients.
| S-EPMC8758312 | biostudies-literature
Project description:BackgroundDespite surgical resection, early lung adenocarcinoma has a recurrence rate of 20-50%. No clear predictive markers for recurrence of early lung adenocarcinoma are available. Targeted next-generation sequencing (NGS) is rarely used to identify recurrence-related genes. We aimed to identify genetic alterations that can predict recurrence, by comparing the molecular profiles of patient groups with and without recurrence.MethodsTissues from 230 patients with resected stage I-II lung adenocarcinoma (median follow-up: 49 months) were analyzed via targeted NGS for 207 cancer-related genes. The recurrence-free survival according to the number and type of mutation was estimated using the Kaplan-Meier method. Independent predictive biomarkers related to recurrence were identified using the Cox proportional hazards model.ResultsRecurrence was observed in 64 patients (27.8%). In multivariate analysis adjusted for age, sex, smoking history, stage, surgical mode, and visceral pleural invasion, the CTNNB1 mutation and fusion genes (ALK, ROS1, RET) were negative prognostic factors for recurrence in early-stage lung adenocarcinoma (HR 4.47, p = 0.001; HR 2.73, p = 0.009). EGFR mutation was a favorable factor (HR 0.51, p = 0.016), but the CTNNB1/EGFR co-mutations were negative predictors (HR 19.2, p < 0.001). TP53 mutation was a negative predictor compared with EGFR mutation for recurrence (HR 5.24, p = 0.02).ConclusionsTargeted NGS can provide valuable information to predict recurrence and identify patients at high recurrence risk, facilitating selection of the treatment strategy among close monitoring and adjuvant-targeted therapy. Larger datasets are required to validate these findings.
| S-EPMC8184531 | biostudies-literature
Project description:ImportanceRecommendations for adjuvant therapy after surgical resection of lung adenocarcinoma (LUAD) are based solely on TNM classification but are agnostic to genomic and high-risk clinicopathologic factors. Creation of a prediction model that integrates tumor genomic and clinicopathologic factors may better identify patients at risk for recurrence.ObjectiveTo identify tumor genomic factors independently associated with recurrence, even in the presence of aggressive, high-risk clinicopathologic variables, in patients with completely resected stages I to III LUAD, and to develop a computational machine-learning prediction model (PRecur) to determine whether the integration of genomic and clinicopathologic features could better predict risk of recurrence, compared with the TNM system.Design, setting, and participantsThis prospective cohort study included 426 patients treated from January 1, 2008, to December 31, 2017, at a single large cancer center and selected in consecutive samples. Eligibility criteria included complete surgical resection of stages I to III LUAD, broad-panel next-generation sequencing data with matched clinicopathologic data, and no neoadjuvant therapy. External validation of the PRecur prediction model was performed using The Cancer Genome Atlas (TCGA). Data were analyzed from 2014 to 2018.Main outcomes and measuresThe study end point consisted of relapse-free survival (RFS), estimated using the Kaplan-Meier approach. Associations among clinicopathologic factors, genomic alterations, and RFS were established using Cox proportional hazards regression. The PRecur prediction model integrated genomic and clinicopathologic factors using gradient-boosting survival regression for risk group generation and prediction of RFS. A concordance probability estimate (CPE) was used to assess the predictive ability of the PRecur model.ResultsOf the 426 patients included in the analysis (286 women [67%]; median age at surgery, 69 [interquartile range, 62-75] years), 318 (75%) had stage I cancer. Association analysis showed that alterations in SMARCA4 (clinicopathologic-adjusted hazard ratio [HR], 2.44; 95% CI, 1.03-5.77; P = .042) and TP53 (clinicopathologic-adjusted HR, 1.73; 95% CI, 1.09-2.73; P = .02) and the fraction of genome altered (clinicopathologic-adjusted HR, 1.03; 95% CI, 1.10-1.04; P = .005) were independently associated with RFS. The PRecur prediction model outperformed the TNM-based model (CPE, 0.73 vs 0.61; difference, 0.12 [95% CI, 0.05-0.19]; P < .001) for prediction of RFS. To validate the prediction model, PRecur was applied to the TCGA LUAD data set (n = 360), and a clear separation of risk groups was noted (log-rank statistic, 7.5; P = .02), confirming external validation.Conclusions and relevanceThe findings suggest that integration of tumor genomics and clinicopathologic features improves risk stratification and prediction of recurrence after surgical resection of early-stage LUAD. Improved identification of patients at risk for recurrence could enrich and enhance accrual to adjuvant therapy clinical trials.
| S-EPMC7758824 | biostudies-literature
Project description:Background: Lung cancer is associated with the highest mortality rate of all cancer types, and the most common histologic subtype of lung cancer is adenocarcinoma. To apply more effective therapeutic treatment, molecular markers that are able to predict the recurrence risk of patients with adenocarcinoma are critically needed. Mutations in TP53 tumor suppressor gene have been found in approximately 50% of lung adenocarcinoma cases, but the presence of a TP53 mutation does not always associate with increased mortality.Methods: The Cancer Genome Atlas RNA sequencing data of lung adenocarcinoma were used to define a novel gene signature for P53 deficiency. This signature was then used to calculate a sample-specific P53 deficiency score based on a patient's transcriptomic profile and tested in four independent lung adenocarcinoma microarray datasets.Results: In all datasets, P53 deficiency score was a significant predictor for recurrence-free survival where high P53 deficiency score was associated with poor survival. The score was prognostic even after adjusting for several key clinical variables including age, tumor stage, smoking status, and P53 mutation status. Furthermore, the score was able to predict recurrence-free survival in patients with stage I adenocarcinoma and was also associated with smoking status.Conclusions: The P53 deficiency score was a better predictor of recurrence-free survival compared with P53 mutation status and provided additional prognostic values to established clinical factors.Impact: The P53 deficiency score can be used to stratify early-stage patients into subgroups based on their risk of recurrence for aiding physicians to decide personalized therapeutic treatment. Cancer Epidemiol Biomarkers Prev; 27(1); 86-95. ©2017 AACR.
| S-EPMC5839302 | biostudies-literature
Project description:To determine the circRNA expression profile in early stage lung adenocarcinoma and matched non-tumor tissues, we used circRNA microArray analysis form Arraystar to examine the expression of circRNAs Lung adenocarcinoma, a form of NSCLC with high lethality at advanced stage, is becoming more popular in women, non- or never-smokers, and even young adult. However, there are no effective early diagnosis methods at present for patients to cure timely. Circular RNAs (circRNAs) as a special novel, stable, and conserved non-coding RNA in mammalian cells have been reported to be widely involved in the processes of cancer disease. Yet, it is still a puzzle which specific circRNAs are involved in the development of early stage lung adenocarcinoma. Here, tumour samples and paired adjacent normal tissues from 4 patients with early stage lung adenocarcinoma were selected for investigating the expression profile of circRNAs by using the high-throughput circRNA microarray. Bioinformatic analyses were conducted to screen those differentially expressed circRNAs. This work illustrates that clusters of circRNAs are aberrantly expressed in early stage lung adenocarcinoma, which might be able to provide potential targets for the early diagnosis of this disease and new genetic insights into lung cancer.
2019-06-26 | GSE101684 | GEO
Project description:The incidence of lung adenocarcinoma (LUAD) is increasing worldwide with different prognosis even in early-stage patients. We aimed to identify a prognostic panel with multiple DNA methylation biomarkers to predict survival in early-stage LUAD patients of different racial groups.The methylation array, pyrosequencing methylation assay, Cox regression and Kaplan-Meier analyses were conducted to build the risk score equations of selected probes in a training cohort of 69 Asian LUAD patients. The risk score model was verified in another cohort of 299 Caucasian LUAD patients in The Cancer Genome Atlas (TCGA) database.We performed a Cox regression analysis, in which the regression coefficients were obtained for eight probes corresponding to eight genes (AGTRL1, ALDH1A3, BDKRB1, CTSE, EFNA2, NFAM1, SEMA4A and TMEM129). The risk score was derived from sum of each methylated probes multiplied by its corresponding coefficient. Patients with the risk score greater than the median value showed poorer overall survival compared with other patients (p = 0.007). Such a risk score significantly predicted patients showing poor survival in TCGA cohort (p = 0.036). A multivariate analysis was further performed to demonstrate that the eight-probe panel association with poor outcome in early-stage LUAD patients remained significant even after adjusting for different clinical variables including staging parameters (hazard ratio, 2.03; p = 0.039).We established a proof-of-concept prognostic panel consisting of eight-probe signature to predict survival of early-stage LUAD patients of Asian and Caucasian populations.
| S-EPMC4969679 | biostudies-other
Project description:ObjectivesTo evaluate whether combining stability and discriminability criteria in building radiomic classifiers will improve the prognosis of cancer recurrence in early stage non-small cell lung cancer on non-contrast computer tomography (CT).Materials and methodsCT scans of 610 patients with early stage (IA, IB, IIA) NSCLC from four independent cohorts were evaluated. A total of 350 patients from Cleveland Clinic Foundation and University of Pennsylvania were divided into two equal sets for training (D1) and validation set (D2). 80 patients from The Cancer Genome Atlas Lung Adenocarcinoma and Squamous Cell Carcinoma and 195 patients from The Cancer Imaging Archive, were used as independent second (D3) and third (D4) validation sets. A linear discriminant analysis (LDA) classifier was built based on the most stable and discriminate features. In addition, a radiomic risk score (RRS) was generated by using least absolute shrinkage and selection operator, Cox regression model to predict time to progression (TTP) following surgery.ResultsA feature selection strategy focusing on both feature discriminability and stability resulted in the classifier having a higher discriminability on validation datasets compared to the discriminability alone criteria in discriminating cancer recurrence (D2, AUC of 0.75 vs. 0.65; D3, 0.74 vs. 0.62; D4, 0.76 vs. 0.63). The RRS generated by most stable-discriminating features was significantly associated with TTP compared to discriminating alone criteria (HR = 1.66, C-index of 0.72 vs. HR = 1.04, C-index of 0.62).ConclusionAccounting for both stability and discriminability yielded a more generalizable classifier for predicting cancer recurrence and TTP in early stage NSCLC.
| S-EPMC7141152 | biostudies-literature