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Designing appropriate similarity metrics (distance) and estimating the optimal number of clusters have been two important issues in cluster analysis. This study proposed an improved k-means clustering algorithm involving a Weighted Distance and a novel Internal Validation index (WeDIV). The weighted distance, EP_dis, was designed by considering the relative contribution between Euclidean and Pearson distances with a weighted strategy. This strategy can effectively capture information reflecting the globally spatial correlation and locally variable trend simultaneously in high-dimensional space. The new internal validation index,RCH, inspired by the Calinski-Harabasz (CH) index and the analysis of variance, was developed to automatically estimate the optimal number of clusters. The EP_dis was proved reliable in mathematics and was validated on two simulated datasets. Four simulated datasets representing different properties were used to validate the effectiveness of RCH. Furthermore, We compared the clustering performance of WeDIV with 12 prevailing clustering algorithms on 16 UCI datasets. The results demonstrated that WeDIV outperforms the others regardless of specifying the number of clusters or not.

Introduction: Cancer is the leading cause of death among children. Objectives: We report on the latest estimates of the burden of cancer among children at the global, regional, and national levels from 1990 to 2019. Methods: Based on the Global Burden of Disease Study 2019, children’s cancer data were analyzed by sex, age, year, and location. Age-standardized rates were used to compare the burdens among regions and nations. Joinpoint analysis was applied to assess the temporal trend of the global childhood cancer burden. Results: In 2019, 291,319 (95% uncertainty interval [UI], 254,239 to 331,993) new cases and 98,834 (86,124 to 113,581) deaths from childhood cancer were documented globally. Further, 8,302,464 (7,230,447 to 9,555,118) DALYs and 1,806,630 (1,567,808 to 2,089,668) prevalent cases were recorded in the same year. Age-standardized incidence and prevalence rates of childhood cancer were greatest in higher SDI settings and increased most significantly in Australasia and Southern Latin America over the last 30 years. However, although age-standardized death and DALY rates of childhood cancer have remarkably decreased in all regions since 1990, countries with a lower SDI showed the highest rates in 2019, particularly in countries in Eastern Sub-Saharan Africa. Among all cancers, leukemia has shown the largest decrease in burden since 1990. Despite this, leukemia was still the most common cancer and the leading cause of death among children in 2019, followed by brain and central nervous system cancer. Conclusions: On a global scale, the childhood cancer burden has significantly fallen over the last 30 years, but is still higher in lower SDI countries. Effective interventions and collaborations among nations should be facilitated to improve healthcare among children with cancer in countries with lower SDI.

The roles of ubiquitin-related genes in hepatocellular carcinoma (HCC) have not been thoroughly investigated. This study aimed to systematically examine ubiquitin-related genes and identify subtypes and stratify prognosis of HCC by using ubiquitin-related signatures. Survival, biological processes, tumor microenvironment (TME), and genomic alterations of the HCC subtypes were investigated. Patients with HCC were classified into two subtypes (clusters 1 and 2) with distinct survival outcomes, pathways, and genomic alterations. Cluster 2 had better prognosis than did cluster 1. Hepatitis B, hepatitis C, Janus tyrosine kinase (JAK)-signal transducer and activator of transcription (STAT) pathway, and natural killer cell-mediated cytotoxicity were enriched in cluster 1. Moreover, cluster 2 had a higher immune score and immune cell infiltrations, whereas cluster 1 had a lower immune score and immune infiltrations. Additionally, mutations, amplifications, and deletions among the phosphatidylinositol 3-kinase (PI3K)-AKT, p53, and receptor tyrosine kinase (RTK)-RAS pathways more frequently occurred in cluster 1, while those among the Hippo, MYC, and Notch signaling pathways were found in cluster 2. Finally, a prognostic signature, consisting of eight ubiquitin-related genes, was established and validated. In brief, our study established a new classification and developed a prognostic signature for HCC.

Summary: Background: Accumulative evidences have shown that dysregulation of biological pathways contributed to the initiation and progression of malignant tumours. Several methods for pathway activity measurement have been proposed, but they are restricted to making comparisons between groups or sensitive to experimental batch effects. Methods: We introduced a novel method for individualized pathway activity measurement (IPAM) that is based on the ranking of gene expression levels in individual sample. Taking advantage of IPAM, we calculated the pathway activity of 318 pathways from KEGG database in the 10528 tumour/normal samples of 33 cancer types from TCGA to identify characteristic dysregulated pathways among different cancer types. Findings: IPAM precisely quantified the level of activity of each pathway in pan-cancer analysis and exhibited better performance in cancer classification and prognosis prediction over five widely used tools. The average ROC-AUC of cancer diagnostic model using tumour-educated platelets (TEPs) reached 92.84%, suggesting the potential of our algorithm in early diagnosis of cancer. We identified several pathways significantly deregulated and associated with patient survival in a large fraction of cancer types, such as tyrosine metabolism, fatty acid degradation, cell cycle, p53 signalling pathway and DNA replication. We also confirmed the dominant role of metabolic pathways in cancer pathway dysregulation and identified the driving factors of specific pathway dysregulation, such as PPARA for branched-chain amino acid metabolism and NR1I2, NR1I3 for fatty acid metabolism. Interpretation: Our study will provide novel clues for understanding the pathological mechanisms of cancer, ultimately paving the way for personalized medicine of cancer. Funding: A full list of funding can be found in the Acknowledgements section.

Previous studies of correlations of microRNA (miR)-499 rs3746444 and miR-196a-2 rs11614913 polymorphisms with glioma risk have yielded inconsistent results. In this study, relationships between these two polymorphisms and glioma risk and survival were evaluated. In total, 605 patients and 1,300 controls were genotyped. rs3746444 increased glioma risk in five genetic models (GA versus AA, odds ratio [OR], 95% confidence interval [CI] = 1.31 [1.05–1.66], p = 0.02; GG versus AA, OR [95% CI] = 10.70 [6.13–18.69], p < 0.0001; GA + GG versus AA, OR [95% CI] = 1.82 [1.47–2.24], p < 0.0001; GG versus AA + GA, OR [95% CI] = 9.99 [5.74–17.40], p < 0.0001; G versus A, OR [95% CI] = 2.18 [1.82–2.60], p < 0.0001). rs11614913 decreased glioma risk in a recessive model (OR [95% CI] = 0.79 [0.64–0.97], p = 0.03). No relationships between either SNP and survival were found. rs3746444 in the miR-499 seed region could affect target recognition. Bioinformatics analyses indicated that miR-499 rs3746444 is involved in various biological processes and pathways, including “cell adhesion molecule binding,” “positive regulation of catabolic process,” “NF-kappa B pathway,” and “PI3K-Akt pathway,” by targeting mRNAs. Our results suggested that miR-499 rs3746444 and miR-196a-2 rs11614913 have crucial roles in glioma susceptibility.

Glioma is the most common tumor of the central nervous system; variation in susceptibility and prognosis worldwide suggests that there are molecular and genetic differences among individuals. The H19 gene plays a dual role in carcinogenesis. In this study, associations between H19 polymorphisms and susceptibility as well as prognosis in glioma were evaluated. In total, 605 patients with glioma and 1,300 cancer-free subjects were enrolled in the study. Individuals with the rs3741219 A>G allele were less likely to develop glioma (relative risk [RR] = 0.54, 95% confidence interval [95% CI] = 0.45–0.63, p < 0.001), whereas rs217727 G>A and rs2839698 G>A genotypes were not associated with glioma risk. The associations between H19 polymorphisms and prognosis were assessed, including overall survival and progression-free survival. Three focused H19 polymorphisms did not show a significant effect on survival. Further analysis based on false-positive report probability validated these significant results. In the haplotype analysis, individuals with the Grs217727Ars2839698Grs3741219 haplotype were less likely to develop glioma (odds ratio [OR] = 0.33, 95% CI = 0.23–0.46, p = 0.02). Overall, carriers of the rs3741219 AG or GG genotype of H19 have a decreased susceptibility to glioma, but polymorphisms in this gene are not related to prognosis.

Although genome-wide association studies (GWASs) have successfully identified thousands of risk variants for human complex diseases, understanding the biological function and molecular mechanisms of the associated SNPs involved in complex diseases is challenging. Here we developed a framework named integrative multi-omics network-based approach (IMNA), aiming to identify potential key genes in regulatory networks by integrating molecular interactions across multiple biological scales, including GWAS signals, gene expression-based signatures, chromatin interactions and protein interactions from the network topology. We applied this approach to breast cancer, and prioritized key genes involved in regulatory networks. We also developed an abnormal gene expression score (AGES) signature based on the gene expression deviation of the top 20 rank-ordered genes in breast cancer. The AGES values are associated with genetic variants, tumor properties and patient survival outcomes. Among the top 20 genes, RNASEH2A was identified as a new candidate gene for breast cancer. Thus, our integrative network-based approach provides a genetic-driven framework to unveil tissue-specific interactions from multiple biological scales and reveal potential key regulatory genes for breast cancer. This approach can also be applied in other complex diseases such as ovarian cancer to unravel underlying mechanisms and help for developing therapeutic targets.

MicroRNAs bind to the 3′ untranslated regions of mRNAs, affecting translation, tumorigenesis, and apoptosis. This study evaluated the role of TYMS (rs1059394, C > T, and rs2847153, G > A), RYR3 (rs1044129, G > A), KIAA0423 (rs1053667, T > C), and GOLGA7 (rs11337, G > T) polymorphisms for assessment of glioma risk and prognosis among the Chinese Han population. Five single-nucleotide polymorphisms were assessed in 605 glioma patients and 1,300 controls. We found a significant correlation between rs1059394 and glioma susceptibility in the homozygote and dominant genetic models (TT versus CC, odds ratio [OR] = 0.71, 95% confidence interval [CI] = 0.52–0.97, p = 0.03; CT+TT versus CC, OR = 0.74, 95% CI = 0.55–0.99, p = 0.04). The results of the Kaplan-Meier and log rank tests revealed that the rs11337 GG genotype correlated with better overall survival of glioma patients (p = 0.017) than the GT genotype. Multivariate Cox regression analysis results also showed that the rs11337 GT genotype correlated with worse overall survival (p = 0.017, hazard ratio [HR] = 1.25, 95% CI = 1.04–1.5) than the GG genotype. These results suggest that GOLGA7 (rs11337) polymorphism may play a role in the prognosis of glioma patients and that TYMS (rs1059394) is associated with glioma risk. Keywords: GOLGA7, rs11337, polymorphism, cancer risk, prognosis, microRNA-binding site

Dysregulation of multiple long non-coding RNAs (lncRNAs) was reported to play major roles in breast cancer (BC). Here we aimed to collect most of the relevant literature to assess the prognostic value of lncRNAs in BC. To this end, we systematically searched PubMed, Embase, Web of Science, Chinese National Knowledge Infrastructure (CNKI), and Wanfang to identify published articles on the associations of lncRNAs with clinicopathology and/or survival of BC. Via this searching, we identified 70 articles involving 9,307 BC patients and regarding 48 lncRNAs. The expression of 41 lncRNAs was related to one or more clinicopathological parameters of BC, including tumor size; lymph node metastasis; histological grade; TNM stage; and estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) statuses (p < 0.05). Dysregulation of 28 lncRNAs was associated with overall survival, and abnormal expression of 9 lncRNAs was linked to disease-free survival. Furthermore, the expression level of 3 lncRNAs was correlated with metastasis-free survival, 3 lncRNAs with relapse-free survival, and 3 lncRNAs with progression-free survival. Our analysis showed that multiple lncRNAs were significantly associated with BC clinicopathology and survival. A large-scale study is needed to verify the prognostic value of these lncRNAs in BC. Keywords: long non-coding RNA, breast cancer, clinicopathology, survival, prognosis