We analyzed different patient subgroups to determine optimal maintenance therapy in newly diagnosed multiple myeloma (NDMM) patients. A total of 226 NDMM patients in our center were included in the study. The characteristics, survival, and adverse reactions were compared among patients who received maintenance therapy or not, and patients who received proteasome inhibitors (PIs) or immunomodulators (IMiDs) maintenance. The survival of different maintenance durations of bortezomib-based regimens was also analyzed. The maintenance therapy not only upgraded more patient responses (34.3 13.3%, = 0.006), but also significantly prolonged their progression-free survival (PFS) (median PFS 41.1 10.5 months, < 0.001) and overall survival (OS) (median OS not reached 38.6 months, < 0.001). Compared with IMiDs, the PFS (median PFS 43.7 38.5 months, = 0.034) and OS (median OS not reached 78.5 months, = 0.041) were both enhanced by PIs maintenance. Patients younger than 65 years whomaintenance with an increased OS. A bortezomib-based maintenance therapy duration of 12 to 24 months after induction and consolidation therapy produced satisfactory OS.PIs maintenance was superior to IMiDs in overall PFS and OS. The beneficial effect was most evident in patients achieving PR after induction and consolidation therapy, and in high-risk patients. Moreover, younger patients also benefited from PIs maintenance with an increased OS. A bortezomib-based maintenance therapy duration of 12 to 24 months after induction and consolidation therapy produced satisfactory OS.Species-specific lncRNAs significantly determine species-specific functions through various ways, such as epigenetic regulation. However, there has been no study focusing on the role of species-specific lncRNAs in other species yet. Here, we found that siRNAs targeting mouse-specific lncRNA AA388235 could significantly induce death of human tumor cells, although it has no effect on mouse tumor cells and normal human cells. The mechanism studies showed that these siRNAs could activate the response of human tumor cells to exogenous nucleic acids, induce pyroptosis and apoptosis in the presence of GSDME, but induce apoptosis in the absence of GSDME. They also significantly inhibited the growth of human tumor cells in vivo. 17 siRNAs were designed for seven more mouse-specific lncRNAs selected randomly, among which 12 siRNAs targeting five lncRNAs induced death in human tumor cell. Our study not only demonstrates that the siRNAs designed for knocking down mouse-specific lncRNA AA388235 can be potential tumor therapeutic drugs, but also suggests that non-human species-specific lncRNAs are a huge potential library that can be used to design siRNAs for tumor treatment. Large-scale screening based on this is promising.Breast cancer (BC) is the primary threat to women's health, and early diagnosis of breast cancer is imperative. Although there are many ways to diagnose breast cancer, the gold standard is still pathological examination. In this paper, a low dimensional three-channel features based breast cancer histopathological images recognition method is proposed to achieve fast and accurate breast cancer benign and malignant recognition. Three-channel features of 10 descriptors were extracted, which are gray level co-occurrence matrix on one direction (GLCM1), gray level co-occurrence matrix on four directions (GLCM4), average pixel value of each channel (APVEC), Hu invariant moment (HIM), wavelet features, Tamura, completed local binary pattern (CLBP), local binary pattern (LBP), Gabor, histogram of oriented gradient (Hog), respectively. Then support vector machine (SVM) was used to assess their performance. Experiments on BreaKHis dataset show that GLCM1, GLCM4 and APVEC achieved the recognition accuracy of 90.2%-94.97% at the image level and 89.18%-94.24% at the patient level, which is better than many state-of-the-art methods, including many deep learning frameworks. The experimental results show that the breast cancer recognition based on high dimensional features will increase the recognition time, but the recognition accuracy is not greatly improved. Three-channel features will enhance the recognizability of the image, so as to achieve higher recognition accuracy than gray-level features.RNA methylation is a novel epigenetic modification that can be used to evaluate tumor prognosis. However, the underlying mechanisms are unclear. This study aimed to investigate the genetic characteristics of 5-methylcytosine (m5C) and N1-methyladenosine (m1A) regulators in lung squamous cell carcinoma (LUSC) and the prognostic value and immune-related effects of m5C regulators. To this end, we selected the public LUSC dataset from the Cancer Genome Atlas and Gene Expression Omnibus. The least absolute shrinkage and selection operator regression model was used to identify prognostic risk signatures. find more We used the UALCAN and Human Protein Atlas databases to study the expression of target gene mRNA/protein expression. Furthermore, the Tumor Immune Single Cell Hub and the Tumor Immune Estimation Resource were used to evaluate the degree of immune cell infiltration. Most of the m5C and m1A regulators showed significantly different expression between LUSC and normal samples. The m5C regulators were associated with poor prognosis. In addition, a prognostic risk signature was developed based on two m5C regulators, NOP2/Sun RNA methyltransferase 3 (NSUN3), and NOP2/Sun RNA methyltransferase 4 (NSUN4). Compared with normal lung tissues, the expression of NSUN3 and NSUN4 in the LUSC TCGA dataset was increased, which was related to clinicopathological characteristics and survival. NSUN3 and NSUN4 were related to the infiltration of six major immune cells; especially NSUN3, which was closely related to CD8+ T cells, while NSUN4 was closely related to neutrophils. Our findings suggest that m5C regulators can predict the clinical prognosis risk and regulate the tumor immune microenvironment in LUSC. The present study was designed to explore the prognostic value of preoperative inflammatory and nutritional biomarkers in stage III gastric cancer (GC) patients with adjuvant chemotherapy and to develop a novel scoring system called the inflammatory-nutritional prognostic score (INPS). A total of 513 patients with pathological stage III GC undergoing radical gastrectomy followed by adjuvant chemotherapy from 2010 to 2017 were enrolled in the study. Clinicopathological characteristics and blood test parameters of individual patients were collected. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used for feature selection to construct INPS. Survival curves were generated using the Kaplan-Meier method with log-rank tests. The nomogram was generated based on the result of the multivariate analysis using Cox's proportional hazards model. The model was assessed by the concordance index (C-index) and was internally validated by bootstraps. According to the results of Lasso Cox regression and K-M survival curves, INPS was determined as follows a low body mass index (BMI) (<23 kg/m ), a low prealbumin (<180 mg/L), a high neutrophil-lymphocyte ratio (NLR) (≥2.