The relationship between household income and prognosis of patients with cervical adenocarcinoma in the United States: A retrospective cohort study based on the SEER database.

OBJECTIVE: To investigate the association between household income and overall survival (OS) of patients with cervical adenocarcinoma. METHODS: We conducted a retrospective cohort study involving participants selected from the Surveillance, Epidemiology, and End Results (SEER) database. Data were collected on various variables, including demographic variables such as median household income and clinicopathological characteristics for all participants. Cox regression analysis was utilized to examine the association between household income and OS. Subgroup analysis, sensitivity analysis, and E-value were used to further confirm the association. RESULTS: A total of 2217 patients were included in the study. Compared with low-income (<$35,000-$54,999), middle-income (55,000-$69,999) or high-income (≥$70,000) was significantly associated with a higher 5-year OS (70.8 %, 58.7 % vs 50 %) in patients with cervical adenocarcinoma. The HR was 0.49, 95 % CI 0.41-0.58, p < 0.001 and 0.66 (0.55-0.78), p < 0.001 respectively, in the unadjusted model. After adjustment for potential confounders, the results were similar (adjusted HR 0.54 (0.45-0.65), p < 0.001) and 0.79 (0.66-0.94), p = 0.01), respectively. This significant association was also present in the various adjusted models. Subgroup and sensitivity analyses suggested that the relationship remained robust and reliable. The E-value analysis indicated robustness to unmeasured confounding. There was evidence of an interaction between age at diagnosis, race, primary site, tumor grade, T, N, M, or Scope Reg LN Sur, and household income on increasing the 5-year OS of cervical adenocarcinoma. CONCLUSIONS: Our study found that middle or high household income was significantly associated with a better 5-year OS compared with low household income in patients with cervical adenocarcinoma.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on drug response genes to predict prognosis and therapeutic response in ovarian cancer.

Ovarian cancer represents a severe gynecological malignancy with a dire prognosis, underscoring the imperative need for dependable biomarkers that can accurately predict drug response and guide therapeutic choices. In this study, we harnessed online single-cell RNA sequencing (scRNAseq) and bulk RNA sequencing (RNAseq) datasets, applying the Scissor algorithm to identify cells responsive to paclitaxel. From these cells, we derived a gene signature, subsequently used to construct a prognostic model that demonstrated high sensitivity and specificity in predicting patient outcomes. Moreover, we conducted pathway and functional enrichment analyses to uncover potential molecular mechanisms driving the prognostic gene signature. This study illustrates the critical role of scRNAseq and bulk RNAseq in developing precise prognostic models for ovarian cancer, potentially transforming clinical decision-making.