Global, regional, and national time trends in incidence for tuberculosis, 1990-2019: An age-period-cohort analysis for the Global Burden of Disease 2019 study.

BACKGROUND: Tuberculosis (TB) represents a significant global health concern, being the leading cause of mortality from a single infectious agent worldwide. The investigation of TB incidence and epidemiological trends is critical for evaluating the effectiveness of control strategies and identifying ongoing challenges. OBJECTIVES: This study presents the trend in TB incidence across 204 countries and regions over a 30-year period. METHODS: The study utilises data sourced from the Global Burden of Disease (GBD) database. The age cohort model and gender subgroup analysis were employed to estimate the net drift (overall annual percentage change), local drift (age annual percentage change), longitudinal age curve (expected age ratio), and cycle and cohort effect (relative risk of cycle and birth cohort) of TB incidence from 1990 to 2019. This approach facilitates the examination and differentiation of age, period, and cohort effects in TB incidence trends, potentially identifying disparities in TB prevention across different countries. RESULTS: Over the past three decades, a general downward trend in TB incidence has been observed in most countries. However, in 15 of the 204 countries, the overall incidence rate is still on the rise (net drift ≥0.0 %) or stagnant decline (≥-0.5 %). From 1990 to 2019, the net drift of tuberculosis mortality ranged from -2.2 % [95 % confidence interval (CI): -2.33, -2.05] in high Socio-demographic Index (SDI) countries to -1.7 % [95 % CI: -1.81, -1.62] in low SDI countries. In some below-average SDI countries,men in the birth cohort are at a disadvantage and at risk of deterioration, necessitating comprehensive TB prevention and treatment. CONCLUSIONS: While the global incidence of TB has declined, adverse period and cohort effects have been identified in numerous countries, raising questions about the adequacy of TB healthcare provision across all age groups. Furthermore, this study reveals gender disparities in TB incidence.

Machine learning algorithm to construct cuproptosis- and immune-related prognosis prediction model for colon cancer.

BACKGROUND: Over the past few years, research into the pathogenesis of colon cancer has progressed rapidly, and cuproptosis is an emerging mode of cellular apoptosis. Exploring the relationship between colon cancer and cuproptosis benefits in identifying novel biomarkers and even improving the outcome of the disease. AIM: To look at the prognostic relationship between colon cancer and the genes associated with cuproptosis and the immune system in patients. The main purpose was to assess whether reasonable induction of these biomarkers reduces mortality among patients with colon cancers. METHOD: Data obtained from The Cancer Genome Atlas and Gene Expression Omnibus and the Genotype-Tissue Expression were used in differential analysis to explore differential expression genes associated with cuproptosis and immune activation. The least absolute shrinkage and selection operator and Cox regression algorithm was applied to build a cuproptosis- and immune-related combination model, and the model was utilized for principal component analysis and survival analysis to observe the survival and prognosis of the patients. A series of statistically meaningful transcriptional analysis results demonstrated an intrinsic relationship between cuproptosis and the micro-environment of colon cancer. RESULTS: Once prognostic characteristics were obtained, the CDKN2A and DLAT genes related to cuproptosis were strongly linked to colon cancer: The first was a risk factor, whereas the second was a protective factor. The finding of the validation analysis showed that the comprehensive model associated with cuproptosis and immunity was statistically significant. Within the component expressions, the expressions of HSPA1A, CDKN2A, and UCN3 differed markedly. Transcription analysis primarily reflects the differential activation of related immune cells and pathways. Furthermore, genes linked to immune checkpoint inhibitors were expressed differently between the subgroups, which may reveal the mechanism of worse prognosis and the different sensitivities of chemotherapy. CONCLUSION: The prognosis of the high-risk group evaluated in the combined model was poorer, and cuproptosis was highly correlated with the prognosis of colon cancer. It is possible that we may be able to improve patients' prognosis by regulating the gene expression to intervene the risk score.

A somatic mutation-derived LncRNA signatures of genomic instability predicts the prognosis and tumor microenvironment immune characters in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is an aggressive carcinoma with genome instability. Long non-coding RNAs (LncRNAs) have been functionally associated with genomic instability in cancers. However, the identification and prognostic value of lncRNAs related to genome instability have not been explored in hepatocellular carcinoma. In this study, we aim to identify a genomic instability-related lncRNA signature for predicting prognosis and the efficacy of immunotherapy in HCC patients. METHODS: According to the somatic mutation and transcript data of 364 patients with HCC, we determined differentially expressed genome instability-related lncRNAs (GInLncRNAs). Gene ontology (GO) enrichment analyses and Kyoto Encyclopedia of genes and genomes enrichment analyses revealed the potential functions of genes co-expressed with those lncRNAs involved in cancer development and immune function. We further determined a genome instability-related lncRNA signature (GInLncSig) through Cox regression analysis and LASSO regression analysis. Thereafter, we performed correlation analyses with mutations, clinical stratification analyses, and survival analyses to evaluate GInLncSig predictive function. Subsequently, we construct a nomogram model for prognostic assessments of patients with HCC. Finally, we performed Immunocytes infiltration analysis, gene set enrichment analysis (ssGSEA) of immunity circle-associated pathways, and T cell-inflamed score to explore GInLncSig's potential value in guiding immunotherapy. RESULTS: We identified 11 independent prognosis-associated GInLncRNAs (AC002511.2, LINC00501, LINC02055, LINC02714, LINC01508, LOC105371967, RP11_96A15.1, RP11_305F18.1, RP11_342M1.3, RP11_432J24.3, U95743.1) to construct a GInLncSig. According to the risk score calculated by GInLncSig, the high-risk group was characterized by a higher somatic mutation count, significantly poorer clinical prognosis, higher T cell-inflamed score, and specific tumor immune infiltration status compared to the low-risk group. Furthermore, we constructed a nomogram model to improve the reliability and clinical utility of predicting the prognosis of patients with HCC. CONCLUSION: Our study established a reliable prognostic prediction signature that could be a tool for prognosis prediction and a promising predictive biomarker of immunotherapy in hepatocellular carcinoma.