Search

BackgroundThis study aimed to explore the potential effects of long non-coding RNAs (lncRNAs) in multiple myeloma (MM) patients using two detection methods: high-throughput sequencing and microarray.MethodsIn this study, lncRNAs were detected in 20 newly diagnosed MM patients, with 10 patients analyzed by whole transcriptome-specific RNA sequencing and 10 patients analyzed by microarray (Affymetrix Human Clariom D). The expression levels of lncRNAs, microRNAs, and messenger RNAs (mRNAs) were analyzed, and the differentially expressed lncRNAs identified by both methods were selected. The significant differentially expressed lncRNAs were further validated using PCR.ResultsThis study established the aberrant expression of certain lncRNAs involved in the occurrence of MM, with AC007278.2 and FAM157C showing the most significant differences. The top 5 common pathways identified by the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were the chemokine signaling pathway, inflammatory mediator regulation, Th17 cell differentiation, apoptosis, and NF-kappa B signaling pathway. Furthermore, three microRNAs (miRNAs) (miR-4772-3p, miR-617, and miR-618) were found to constitute competing endogenous RNA (ceRNA) networks in both sequencing and microarray analyses.ConclusionsBy the combination analysis, our understanding of lncRNAs in MM will be increased significantly. More overlapping differentially expressed lncRNAs were found to predict therapeutic targets precisely.

ObjectivePrecise risk stratification is increasingly essential in the management of multiple myeloma (MM) as some standard-risk (SR) patients still exhibit similar poor outcomes as genetically high-risk (GHR) patients in the era of novel agents. It has recently been demonstrated that functional high-risk (FHR) patients, those with suboptimal response to first-line induction therapy or early relapse within 12 months, have identifiable molecular characteristics from the SR group in the CoMMpass dataset. However, these findings lack practical validation in the real world.MethodsMM cells purified by CD138 microbeads from newly diagnosed MM (NDMM) patients received fluorescence in situ hybridization and sequencing with a 92-gene Panel. Cytogenetic abnormalities defined GHR patients with t(4;14) or t(14;16) or complete loss of functional P53 or 1q21 gain and International Staging System (ISS) stage 3. SR group was patients who did not fulfill any criteria for GHR or FHR.ResultsThere were 145 patients with NDMM, 78 in the SR group, 56 in the GHR group, and 11 in the FHR group. In the FHR group, eight patients were suboptimal responses to induction therapy, and three relapsed within 12 months. We found that male patients, patients with extra-medullary plasmacytoma (EMD), circulating clonal plasma cells (CPC) ≥0.05%, and P53 mono-allelic inactivation were significantly higher in the FHR group compared to the SR group. After a median follow-up of 21.0 months, the median progression-free survival (PFS) and overall survival (OS) were 5.0 months, 19.1 months and 36.6 months in the FHR, GHR, and SR groups, respectively. Compared to the SR group, FHR patients had a higher frequency of mutations in MKI67, ERN1, and EML4. GO analysis showed that mutations in FHR were enriched for oxidative stress, chromosomal segregation, and hypoxia tolerance.ConclusionThe FHR found in the SR NDMM patient group has unique clinical features, including being male, with EMD and CPC, and genetic characteristics of mutations affecting oxidative stress, chromosome segregation, and hypoxia tolerance. In contrast to previous reports, our data suggested that patients with P53 mono-allelic inactivation should be classified in the GHR group rather than the FHR group.

ObjectiveTo use machine learning methods to explore overall survival (OS)-related prognostic factors in elderly multiple myeloma (MM) patients.MethodsData were cleaned and imputed using simple imputation methods. Two data resampling methods were implemented to facilitate model building and cross validation. Four algorithms including the cox proportional hazards model (CPH); DeepSurv; DeepHit; and the random survival forest (RSF) were applied to incorporate 30 parameters, such as baseline data, genetic abnormalities and treatment options, to construct a prognostic model for OS prediction in 338 elderly MM patients (>65 years old) from four hospitals in Beijing. The C-index and the integrated Brier score (IBwere used to evaluate model performances.ResultsThe 30 variables incorporated in the models comprised MM baseline data, induction treatment data and maintenance therapy data. The variable importance test showed that the OS predictions were largely affected by the maintenance schema variable. Visualizing the survival curves by maintenance schema, we realized that the immunomodulator group had the best survival rate. C-indexes of 0.769, 0.780, 0.785, 0.798 and IBS score of 0.142, 0.112, 0.108, 0.099 were obtained from the CPH model, DeepSurv, DeepHit, and the RSF model respectively. The RSF model yield best scores from the fivefold cross-validation, and the results showed that different data resampling methods did affect our model results.ConclusionWe established an OS model for elderly MM patients without genomic data based on 30 characteristics and treatment data by machine learning.

PurposeTo study the impact of dose distribution on volume-effect parameter and predictive ability of equivalent uniform dose (EUD) model, and to explore the improvements.Methods and MaterialsThe brains of 103 nasopharyngeal carcinoma patients treated with IMRT were segmented according to dose distribution (brain and left/right half-brain for similar distributions but different sizes; VD with different D for different distributions). Predictive ability of EUDVD (EUD of VD) for radiation-induced brain injury was assessed by receiver operating characteristics curve (ROC) and area under the curve (AUC). The optimal volume-effect parameter a of EUD was selected when AUC was maximal (mAUC). Correlations between mAUC, a and D were analyzed by Pearson correlation analysis. Both mAUC and a in brain and half-brain were compared by using paired samples t-tests. The optimal DV and VD points were selected for a simple comparison.ResultsThe mAUC of brain/half-brain EUD was 0.819/0.821 and the optimal a value was 21.5/22. When D increased, mAUC of EUDVD increased, while a decreased. The mAUC reached the maximum value when D was 50–55 Gy, and a was always 1 when D ≥55 Gy. The difference of mAUC/a between brain and half-brain was not significant. If a was in range of 1 to 22, AUC of brain/half-brain EUDV55 Gy (0.857–0.830/0.845–0.830) was always larger than that of brain/half-brain EUD (0.681–0.819/0.691–0.821). The AUCs of optimal dose/volume points were 0.801 (brain D2.5 cc), 0.823 (brain V70 Gy), 0.818 (half-brain D1 cc), and 0.827 (half-brain V69 Gy), respectively. Mean dose (equal to EUDVD with a = 1) of high-dose volume (V50 Gy–V60 Gy) was superior to traditional EUD and dose/volume points.ConclusionVolume-effect parameter of EUD is variable and related to dose distribution. EUD with large low-dose volume may not be better than simple dose/volume points. Critical-dose-volume EUD could improve the predictive ability and has an invariant volume-effect parameter. Mean dose may be the case in which critical-dose-volume EUD has the best predictive ability.

To estimate whether adjuvant radiotherapy is necessary for patients with stage IA1-IIA1 cervical cancer after laparoscopic hysterectomy, 221 patients were retrospectively analyzed. Sixty-two of them were treated with laparoscopic hysterectomy and adjuvant radiotherapy (group A), 115 underwent open surgery (group B) and 44 received laparoscopic hysterectomy alone (group C). Results showed that the 3-year local recurrence-free survival (LRFS) rates of group A, B and C were 98.4%, 97.4% and 86.4%, respectively. The LRFS rates of group A and B surpassed C (A vs. B, p=0.634; A vs. C, p=0.011; B vs. C, p=0.006). The inter-group differences of 3-year overall survival (OS) and distant metastasis free survival (DMFS) were not statistically significant. In subgroup analysis of stage IB disease, the 3-year LRFS rates of group A, B and C were 100%, 98.8% and 83.1%, the 3-year OS rates of group A, B and C were 100%, 98.9% and 91.5%, respectively. The 3-year LRFS and OS rates of group A and B were significantly superior to group C (p

PurposeTo investigate the role of half-brain delineation in the prediction of radiation-induced temporal lobe injury (TLI) in nasopharyngeal carcinoma (NPC) receiving intensity-modulated radiotherapy (IMRT).Methods and MaterialsA total of 220 NPC cases treated with IMRT and concurrent platinum-based chemotherapy were retrospectively analyzed. Dosimetric parameters of temporal lobes, half-brains, and brains included maximum dose (Dmax), doses covering certain volume (DV) from 0.03 to 20 cc and absolute volumes receiving specific dose (VD) from 40 to 80 Gy. Inter-structure variability was assessed by coefficients of variation (CV) and paired samples t-tests. Receiver operating characteristic curve (ROC) and Youden index were used for screening dosimetric parameters to predict TLI. Dose/volume response curve was calculated using the logistic dose/volume response model.ResultsCVs of brains, left/right half-brains, and left/right temporal lobes were 9.72%, 9.96%, 9.77%, 27.85%, and 28.34%, respectively. Each DV in temporal lobe was significantly smaller than that in half-brain (P < 0.001), and the reduction ranged from 3.10% to 45.98%. The area under the curve (AUC) of DV and VD showed an “increase-maximum-decline” behavior with a peak as the volume or dose increased. The maximal AUCs of DVs in brain, half-brain and temporal lobe were 0.808 (D2cc), 0.828 (D1.2cc) and 0.806 (D0.6cc), respectively, and the maximal AUCs of VDs were 0.818 (D75Gy), 0.834 (V72Gy) and 0.814 (V70Gy), respectively. The cutoffs of V70Gy (0.86 cc), V71Gy (0.72 cc), V72Gy (0.60 cc), and V73Gy (0.45 cc) in half-brain had better Youden index. TD5/5 and TD50/5 of D1.2cc were 58.7 and 80.0 Gy, respectively. The probability of TLI was higher than >13% when V72Gy>0 cc, and equal to 50% when V72Gy = 7.66 cc.ConclusionHalf-brain delineation is a convenient and stable method which could reduce contouring variation and could be used in NPC patients. D1.2cc and V72Gy of half-brain are feasible for TLI prediction model. The dose below 70 Gy may be relatively safe for half-brain. The cutoff points of V70–73Gy could be considered when the high dose is inevitable.