Objective. The study is aimed at analyzing the predictive value of serum Ig A, Ig G, and TNF-α in the recurrence of multiple myeloma (MM). Methods. 136 patients with MM treated in our hospital from January 2010 to January 2017 were followed up for 5 years. Finally, 100 patients who met the inclusion and exclusion criteria and had the complete follow-up visit were selected as the study subjects, with the recurrence of MM as endpoint event, and the observation was taken until the occurrence of endpoint event in patients or the termination of this study. They were divided into the recurrence group (RG) and the nonrecurrence group (NRG) according to whether the endpoint event occurred. The venous blood of patients was collected at the first diagnosis and subsequent visit (at the time of recurrence or termination of the study) to measure the Ig A and Ig G using a full automatic special protein analyzer and the TNF-α level by enzyme-linked immunosorbent assay. The data obtained in this study were analyzed by univariate analysis to choose the factors with difference in statistical significance to draw the ROC curve, and the areas under the curve (AUC) were recorded to analyze the potential mechanism of Ig A, Ig G, and TNF-α in predicting the recurrence of MM. Results. After follow-up visit, there were 62 patients with recurrence (62.0%) and 38 patients without recurrence (38.0%), with no obvious difference in gender, age, body weight, and immune classification between the two groups ( P > 0.05 ). Compared with the NRG, the levels of soluble interleukin-2 receptor (sIL-2R) and β2-microglobulin (β2-MG) in the RG at the first diagnosis were distinctly higher ( P < 0.001 ); the levels of Ig A, Ig G, and TNF-α in the RG at the first diagnosis were visibly higher ( P < 0.05 ); and the levels of Ig A, Ig G, and TNF-α in the RG at the subsequent visit were clearly higher ( P < 0.05 ). There was a correlation between Ig G, Ig A, and TNF-α and β2-MG at the first diagnosis and the subsequent visit ( P < 0.05 ); there was a correlation between Ig G and TNF-α, and sIL-2R at the first diagnosis and the subsequent visit ( P < 0.05 ); and there was a correlation between Ig A and sIL-2R at the subsequent visit ( P < 0.05 ). The AUC of Ig G, Ig A, and TNF-α in predicting the MM at the first diagnosis were 0.772, 0.776, and 0.778, respectively. Conclusion. The serum Ig A, Ig G, and TNF-α had a predictive value in the recurrence of MM, and TNF-α was correlated with sIL-2R and β2-MG, with the highest AUC and the best predictive value.

Background Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, might serve as a promising therapeutic target for cancer treatment and clinical outcome prediction. Nevertheless, the role of cuproptosis-related lncRNAs in AML is not fully understood. Methods The RNA sequencing data and demographic characteristics of AML patients were downloaded from The Cancer Genome Atlas database. Pearson correlation analysis, the least absolute shrinkage and selection operator algorithm, and univariable and multivariable Cox regression analyses were applied to identify the cuproptosis-related lncRNA signature and determine its feasibility for AML prognosis prediction. The performance of the proposed signature was evaluated via Kaplan–Meier survival analysis, receiver operating characteristic curves, and principal component analysis. Functional analysis was implemented to uncover the potential prognostic mechanisms. Additionally, quantitative real-time PCR (qRT-PCR) was employed to validate the expression of the prognostic lncRNAs in AML samples. Results A signature consisting of seven cuproptosis-related lncRNAs (namely NFE4, LINC00989, LINC02062, AC006460.2, AL353796.1, PSMB8-AS1, and AC000120.1) was proposed. Multivariable cox regression analysis revealed that the proposed signature was an independent prognostic factor for AML. Notably, the nomogram based on this signature showed excellent accuracy in predicting the 1-, 3-, and 5-year survival (area under curve = 0.846, 0.801, and 0.895, respectively). Functional analysis results suggested the existence of a significant association between the prognostic signature and immune-related pathways. The expression pattern of the lncRNAs was validated in AML samples. Conclusion Collectively, we constructed a prediction model based on seven cuproptosis-related lncRNAs for AML prognosis. The obtained risk score may reveal the immunotherapy response in patients with this disease.

Background Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, might be a promising therapeutic target for cancer treatment. Nevertheless, the role of cuproptosis-related lncRNAs in AML remains unexplored. Methods AML RNA sequencing data and demographical characteristics were downloaded from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis, the least absolute shrinkage and selection operator (LASSO) regression algorithm, and univariable and multivariable Cox regression analyses were applied to identify the cuproptosis-related lncRNA signature and determine its feasibility it for AML prognosis prediction. The performance of the proposed signature was measured via Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and principal component analysis (PCA). Functional analysis was implemented to uncover the potential prognostic mechanisms. Moreover, quantitative real-time PCR (qRT-PCR) was used to validate the expression of the prognostic lncRNAs in clinical samples. Results A signature consisting of seven cuproptosis-related lncRNA (NFE4, LINC00989, LINC02062, AC006460.2, AL353796.1, PSMB8-AS1, and AC000120.1) was identified. Multivariable cox regression analysis revealed that the proposed lncRNA signature was an independent prognostic factor for AML, the nomogram based on this signature showed excellent accuracy in predicting 1-, 3-, and 5-year survival [Area Under Curve (AUC) = 0.846, 0.801, and 0.895, respectively]. Functional analysis suggested a significant association between the prognostic signature and the immune-related pathways. The expression pattern of the lncRNAs was validated in AML samples, which suggested the robustness of these findings. Conclusion In this study, we constructed a prediction model based on seven cuproptosis-related lncRNAs for AML prognosis. The obtained risk score may be connected with tumor immunity.