Lung cancer remains a leading cause of cancer-related mortality in urban China, underscoring the urgency of effective screening programs. Despite the proven efficacy of low-dose computed tomography (LDCT) in reducing lung cancer mortality, participation rates among high-risk populations remain suboptimal. This study aspired to determine factors influencing LDCT screening adherence among high-risk individuals in Nanjing, China, as part of the China Urban Cancer Screening Project (CanSPUC). A cross-sectional study was conducted from 2019 to 2023, involving 52,910 participants aged 45–74 years. High-risk individuals were identified using a modified Harvard Risk Index adjusted for Chinese demographics. Socio-demographic and clinical data were collected via questionnaires and LDCT reports, with missing values imputed via random forest models. Logistic regression was used to determine the factors associated with LDCT screening adherence. Among 16,020 high-risk participants, only 45.4% (7,280/16,020) underwent LDCT. Key factors influencing participation included gender (OR = 1.36, 95% CI:1.24–1.49, p < 0.001), education level (OR = 1.18, 95%CI:1.10–1.26, p < 0.001), occupational exposure (OR = 1.20, 95%CI: 1.11–1.29, p < 0.001), current smoking (OR = 1.26, 95%CI:1.13–1.40, p < 0.001), family cancer history (OR = 1.37, 95%CI:1.28–1.46, p < 0.001), and tuberculosis history (OR = 0.84, 95%CI:0.72–0.97, p = 0.022). LDCT detected lung cancer in 0.47% (34/7,280), with semi-positive nodules observed in 43.16% (3,142/7,280) of screened individuals. Despite effective risk stratification, participation in LDCT screening remains low in urban China. Targeted interventions to solve gender disparities, health literacy gaps, and risk communication may be very important to improve the intake rate. Perhaps future efforts should give priority to community-based education, enhanced accessibility, and tailored protocols to reduce lung cancer mortality in high-risk populations.

BACKGROUND: Numerous studies reveal the clinical significance of tumor microenvironment (TME) in multiple cancers. The association between TME in oral squamous cell carcinoma (OSCC) and clinical outcomes remains unsolved. OBJECTIVE: This study aims to exhibit the TME of OSCC and identified the prognostic marker. METHODS: Gene expression profile and clinical data OSCC patients were from the TCGA database. The validated stage data was from the Gene Expression Omnibus (GSE65858). Immune/stromal scores of each patient were calculated by ESTIMATE algorithm. Biological functional prediction was conducted. Prognostic genes identified by survival analysis. Nomogram and Receiver operating characteristic curves were employed to test the predicting power. TIMER database was applied to evaluated the immune infiltrates. RESULTS: Lower immune scores were observed in male patients (P= 0.0107) and different primary tumor sites of oral cavity with different stromal scores (P= 0.0328). The Differentially expressed genes (DEGs) were involved in immune related pathways. HGF gene (hepatocyte growth factor) was prognostic related and with a better prognostic performance when combined with clinical features (AUC=TCGA 0.638, AUC=GEO 0.714). HGF was significantly related with B cell, CD4ï⁢¼⁢‹T cell, CD8+T cell, macrophage, neutrophils, and dendritic cell infiltration. CONCLUSION : The current study analyzed the TME and presented immune related prognostic biomarkers for OSCC.

Background: Cervical cancer is a prevalent malignancy of the female reproductive system. Cervical intraepithelial neoplasia (CIN) is a precursor lesion for CC. Various studies have examined circulating microRNAs (miRNAs) as potential early diagnostic markers for CC and CIN. However, the findings have been inconclusive. Therefore, it is necessary to evaluate the diagnostic accuracy and identify potential sources of variability among these studies. Methods: The PubMed, Cochrane Library, Embase, and Web of Science databases were searched to identify relevant literature. Then, Stata 14.0 was utilized to calculate summary estimates for diagnostic parameters, including sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic (ROC). To scrutinize the heterogeneity, the Cochran-Q test and I² statistic were utilized. As significant heterogeneity was observed, the random effects model was chosen. To explore potential sources of the heterogeneity, subgroup and regression analyses were conducted. Results: We analysed 12 articles reporting on 24 studies involving 1817 patients and 1731 healthy controls. The pooled sensitivity was 0.77 (95% CI 0.73–0.81), the specificity was 0.81 (95% CI 0.73–0.86), the PLR was 3.99 (95% CI 2.81–5.65), the NLR was 0.28 (95% CI 0.23–0.35), the DOR was 14.18 (95% CI 8.47–23.73), and the area under the curve (AUC) was 0.85 (95% CI 0.81–0.87). Subgroup analysis revealed that multiple miRNAs can improve diagnostic performance; the pooled sensitivity of multiple miRNAs was 0.78 (95% CI 0.68–0.86), the specificity was 0.85 (95% CI 0.78–0.90), and the AUC was 0.89 (95% CI 0.86–0.91). Conclusion: This study suggested that circulating microRNAs may be biomarkers for early CC diagnosis.

Individuals of African ancestry (AA) have approximately twofold higher risk of multiple myeloma (MM) than individuals of European ancestry, yet the molecular determinants between monoclonal gammopathy of undetermined significance (MGUS) and MM remain poorly defined. We performed whole-exome sequencing (WES) and genome-wide DNA methylation profiling in 28 AA patients with MM and 28 frequency-matched AA patients with MGUS without documented progression. After quality control, the WES analysis included 28 patients with MGUS and 27 with MM, while the DNA methylation analysis included 27 patients with MGUS and 26 with MM. Whole-exome sequencing identified differences in observed germline variant distributions, with qualifying COL19A1 variants observed only in MGUS and qualifying VWA5B2 variants observed only in MM within this cohort. Gene-burden analyses identified nominal exploratory signals in LAMA5 and PIEZO1, although neither remained significant after Bonferroni correction. DNA methylation profiling identified 703 differentially methylated positions and 122 exploratory candidate differentially methylated regions between MM and MGUS. One 395 bp region spanning 16 CpG sites within RNF39 met the FWER significance threshold. Exploratory pathway analyses implicated oncogenic signaling, cellular stress responses, and cell-death pathways. Although epigenetic age acceleration did not differ between groups, lower DNA methylation (DNAm)-estimated telomere length was observed in MM than in MGUS; however, this difference was borderline after adjustment for DNAm-estimated blood-cell composition. Together these exploratory findings identify germline and epigenetic features associated with MM versus MGUS in AA patients and nominate biological pathways for validation in larger, longitudinal studies.