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BackgroundMAPK-RAP1A signaling, which is involved in cancer progression, remains to be defined. Upregulation of MAPK-RAP1A signaling accounts for most cancers that harbor high incident rate, such as non-small cell lung cancer (NSCLC) and pancreatic cancer, especially in hepatocellular carcinoma (HCC). MAPK-RAP1A signaling plays an important function as clinical diagnosis and prognostic value in cancers, and the role of MAPK-RAP1A signaling related with immune infiltration for HCC should be elucidated.MethodsMicroarray data and patient cohort information from The Cancer Genome Atlas (TCGA; n = 425) and International Cancer Genome Consortium (ICGC; n = 405) were selected for validation. The Cox regression and least absolute shrinkage and selection operator (LASSO) were used to construct a clinical prognostic model in this analysis and validation study. We also tested the area under the curve (AUC) of the risk signature that could reflect the status of predictive power by determining model. MAPK-RAP1A signaling is also associated with tumor-infiltrating immune cells (TICs) as well as clinical parameters in HCC. The GSEA and CIBERSORT were used to calculate the proportion of TICs, which should be beneficial for the clinical characteristics (clinical stage, distant metastasis) and positively correlated with the survival of HCC patients.ResultsHCC patients with enrichment of MAPK-RAP1A signaling were associated with clinical characteristics and favorable T cell gamma delta (Vδ T cells), and STMN1, RAP1A, FLT3, HSPA8, ANGPT2, and PGF were used as candidate biomarkers for risk scores of HCC. To determine the molecular mechanism of this signature gene association, Gene Set Enrichment Analysis (GSEA) was proposed. Cytokine–cytokine receptor interaction, TGF-β signaling pathway, and Intestinal immune network for IgA production gene sets were closely related in MAPK-RAP1A gene sets. Thus, we established a novel prognostic prediction of HCC to deepen learning of MAPK-RAP1A signaling pathways.ConclusionOur findings demonstrated that HCC patients with enrichment of MAPK-RAP1A signaling were associated with clinical characteristics and favorable T cell gamma delta (Vδ T cells), which may be a novel prognostic prediction of HCC.

Objectives: To identify a computed tomography (CT)-based radiomic signature for predicting progression-free survival (PFS) in stage IV anaplastic lymphoma kinase (ALK)-positive non-small-cell lung cancer (NSCLC) patients treated with tyrosine kinase inhibitor (TKI) crizotinib.Materials and Methods: This retrospective proof-of-concept study included a cohort of 63 stage IV ALK-positive NSCLC patients who had received TKI crizotinib therapy for model construction and validation. Another independent cohort including 105 stage IV EGFR-positive NSCLC patients was also used for external validation in EGFR-TKI treatment. We initially extracted 481 quantitative three-dimensional features derived from manually segmented tumor volumes of interest. Pearson's correlation analysis along with the least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression was successively performed to select critical radiomic features. A CT-based radiomic signature for PFS prediction was obtained using multivariate Cox regression. The performance evaluation of the radiomic signature was conducted using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) analysis, and Kaplan–Meier survival analysis.Results: A radiomic signature containing three features showed significant prognostic performance for ALK-positive NSCLC patients in both the training cohort (C-index, 0.744; time-dependent AUC, 0.895) and the validation cohort (C-index, 0.717; time-dependent AUC, 0.824). The radiomic signature could significantly risk-stratify ALK-positive NSCLC patients (hazard ratio, 2.181; P < 0.001) and outperformed other prognostic factors. However, no significant association with PFS was captured for the radiomic signature in the EGFR-positive NSCLC cohort (log-rank tests, P = 0.41).Conclusions: The CT-based radiomic features can capture valuable information regarding the tumor phenotype. The proposed radiomic signature was found to be an effective prognostic factor in stage IV ALK mutated nonsynchronous nodules in NSCLC patients treated with a TKI.

Objective: To develop and evaluate a diffusion-weighted imaging (DWI)-based radiomic nomogram for lymph node metastasis (LNM) prediction in advanced gastric cancer (AGC) patients.Overall Study: This retrospective study was conducted with 146 consecutively included pathologically confirmed AGC patients from two centers. All patients underwent preoperative 3.0 T magnetic resonance imaging (MRI) examination. The dataset was allocated to a training cohort (n = 71) and an internal validation cohort (n = 47) from one center along with an external validation cohort (n = 28) from another. A summary of 1,305 radiomic features were extracted per patient. The least absolute shrinkage and selection operator (LASSO) logistic regression and learning vector quantization (LVQ) methods with cross-validations were adopted to select significant features in a radiomic signature. Combining the radiomic signature and independent clinical factors, a radiomic nomogram was established. The MRI-reported N staging and the MRI-derived model were built for comparison. Model performance was evaluated considering receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).Results: A two-feature radiomic signature was found significantly associated with LNM (p < 0.01, training and internal validation cohorts). A radiomic nomogram was established by incorporating the clinical minimum apparent diffusion coefficient (ADC) and MRI-reported N staging. The radiomic nomogram showed a favorable classification ability with an area under ROC curve of 0.850 [95% confidence interval (CI), 0.758–0.942] in the training cohort, which was then confirmed with an AUC of 0.857 (95% CI, 0.714–1.000) in internal validation cohort and 0.878 (95% CI, 0.696–1.000) in external validation cohort. Meanwhile, the specificity, sensitivity, and accuracy were 0.846, 0.853, and 0.851 in internal validation cohort, and 0.714, 0.952, and 0.893 in external validation cohort, compensating for the MRI-reported N staging and MRI-derived model. DCA demonstrated good clinical use of radiomic nomogram.Conclusions: This study put forward a DWI-based radiomic nomogram incorporating the radiomic signature, minimum ADC, and MRI-reported N staging for individualized preoperative detection of LNM in patients with AGC.

Introduction: Assessment of lymph node metastasis (LNM) is crucial for treatment decision and prognosis prediction for endometrial cancer (EC). However, the sensitivity of the routinely used magnetic resonance imaging (MRI) is low in assessing normal-sized LNM (diameter, 0–0.8 cm). We aimed to develop a predictive model based on magnetic resonance (MR) images and clinical parameters to predict LNM in normal-sized lymph nodes (LNs).Materials and Methods: A total of 200 retrospective patients were enrolled and divided into a training cohort (n = 140) and a test cohort (n = 60). All patients underwent preoperative MRI and had pathological result of LNM status. In total, 4,179 radiomic features were extracted. Four models including a clinical model, a radiomic model, and two combined models were built. Area under the receiver operating characteristic (ROC) curves (AUC) and calibration curves were used to assess these models. Subgroup analysis was performed according to LN size. All patients underwent surgical staging and had pathological results.Results: All of the four models showed predictive ability in LNM. One of the combined models, ModelCR1, consisting of radiomic features, LN size, and cancer antigen 125, showed the best discrimination ability on the training cohort [AUC, 0.892; 95% confidence interval [CI], 0.834–0.951] and test cohort (AUC, 0.883; 95% CI, 0.786–0.980). The subgroup analysis showed that this model also indicated good predictive ability in normal-sized LNs (0.3–0.8 cm group, accuracy = 0.846;