Background: The aim of this paper is to identify the differentially expressed lncRNAs (DELs) that could serve as markers for the prognosis of early-stage (stage I-II) lung squamous cell carcinoma (SCC).Methods: lncRNAs expression data and corresponding clinical information for 395 patients with stage I-II lung SCC were obtained from The Cancer Genome Atlas (TCGA). Univariate Cox regression analysis and LASSO regression were used to screen key lncRNAs, which were then were subjected to a multivariate Cox regression analysis. Furthermore, based on the results of multivariate analysis, lncRNAs with statistical significance were utilized to establish a risk assessment model. Also, a prognostic nomogram based on the risk assessment model was built. These two tools were evaluated by receiver operating characteristic (ROC) curve. Additionally, Kaplan-Meier (KM) survival curves for potential prognostic lncRNAs and clinical factors were performed.Results: A total of 5 key lncRNAs (AC015712.4, LINC02301, AGAP11, AC099850.3, and AC008915.1) were screened to construct the risk assessment model, and the area under the ROC curves (AUC) showed the model had a general performance. The risk level of the model was identified as an independent prognostic factor for stage I-II lung SCC. A nomogram combining the lncRNA-based risk assessment model, age, and T stage was constructed to predict 3-and 5-year overall survival (OS) in patients with stage I-II lung SCC.The results of ROC and calibration curves demonstrated that the nomogram was reliable in predicting OS rate. Besides, KM survival curves showed OS time was significantly corrected with the expression of AC015712.4, age, and T stage.
Conclusions:In the present study, a risk assessment model and a nomogram based on five lncRNAs were constructed to predict OS time for early-stage lung SCC, which may contribute to the management of lung SCC.
The role of immune cell infiltration in the prognosis of clear cell renal cell carcinoma (ccRCC) has received increasing attention. However, immune scores have not yet been introduced into routine clinical practice of ccRCC patients. The principal objective of our research was to study the correlation between immune scores and overall survival (OS) of ccRCC.
In this study, Cox regression analyses were used to identify risk factors associated with OS of ccRCC based on the Cancer Genome Atlas datasets. Furthermore, an integrated nomogram combining immune scores and clinicopathologic factors was built for predicting 3- and 5-year OS of ccRCC patients. The receiver operating characteristic curve, concordance index, and calibration curves were used for the evaluation of our nomogram. Also, Kaplan–Meier (KM) survival analysis of immune scores, stromal scores, and different clinicopathological factors was performed.
A total of 514 patients were divided into the low- or high-immune scores group. KM and multivariate Cox regression analyses demonstrated that ccRCC patients with high-immune scores had significantly poor OS compared with those with low-immune scores. Calibration curves showed good consistency between the predicted OS and the actual OS probability. Areas under the receiver operating characteristic curves for 3- and 5-year OS were 0.816 and 0.769, and the concordance index was 0.775, indicating that our nomogram had good accuracy for predicting OS of ccRCC patients. Additionally, KM analysis showed that older age, later T stage, distant metastasis, advanced tumor lymph node metastasis stage, higher tumor grade, left site, and low stromal scores were associated with worse OS in ccRCC patients.
High-immune scores show a significant correlation with unsatisfactory prognosis in ccRCC patients. Furthermore, the immune scores-based nomogram may be helpful in predicting ccRCC prognosis.
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