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Chinese Clinical Trial Registry (TRC-13003122).

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Background: Apolipoprotein C1 (APOC1) has been proved to play a critical role in gastric, breast, lung, and pancreatic cancer. However, the relationship between APOC1 and urinary tumors remains unclear. This study aimed to assess the diagnostic and prognostic value of APOC1 in urinary tumors. Methods: We performed a pan analysis of APOC1 mRNA expression in urinary cancer using the Gene Expression Profiling Interactive Analysis (GEPIA) database. To further investigate the prognostic value of APOC1 expression in urinary cancers, the Kaplan-Meier plotter database was used. Furthermore, we collected the tumor and adjacent normal samples of 32 ccRCC patients to perform qRT-PCR and western blotting assays. A total of 72 cases with ccRCC were analyzed using tissue microarrays (TMAs). Results: Our results based on Kaplan-Meier plotter database indicated that a high expression of APOC1 may lead to poor overall survival (OS, p = 0.0019) in patients with ccRCC. Furthermore, the cancer stages and tumor grade of ccRCC appeared to be strongly linked with APOC1 expression according to UALCAN database. Hence, we reached a preliminary conclusion that APOC1 may play a key role in the tumorigenesis and progression of ccRCC. Furthermore, the Kaplan-Meier survival curve analyses of 72 clinical patients indicated that high expression of APOC1 was associated with poor progression-free survival (PFS, p = 0.007) and OS (p = 0.022). In addition, univariate Cox regression analysis confirmed the significant relationship between APOC1 expression and survival (p = 0.038). The TMAs analysis in combination with the patients' clinicopathological features was also performed. The expression of APOC1 was found to be significantly correlated with the tumor size (p = 0.018) and histological grade (p = 0.016). Conclusions: In conclusion, the findings of our study suggest that APOC1 may serve as a novel diagnostic and prognostic biomarker for ccRCC. Further evidence on the mechanism of APOC1 promoting tumor progression may transform it to a new therapeutic target for the treatment of ccRCC.

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Background. Recent research found that N5-methylcytosine (m5C) was involved in the development and occurrence of numerous cancers. However, the function and mechanism of m5C RNA methylation regulators in clear cell renal cell carcinoma (ccRCC) remains undiscovered. This study is aimed at investigating the predictive and clinical value of these m5C-related genes in ccRCC. Methods. Based on The Cancer Genome Atlas (TCGA) database, the expression patterns of twelve m5C regulators and matched clinicopathological characteristics were downloaded and analyzed. To reveal the relationships between the expression levels of m5C-related genes and the prognosis value in ccRCC, consensus clustering analysis was carried out. By univariate Cox analysis and last absolute shrinkage and selection operator (LASSO) Cox regression algorithm, a m5C-related risk signature was constructed in the training group and further validated in the testing group and the entire cohort. Then, the predictive ability of survival of this m5C-related risk signature was analyzed by Cox regression analysis and nomogram. Functional annotation and single-sample Gene Set Enrichment Analysis (ssGSEA) were applied to further explore the biological function and potential signaling pathways. Furthermore, we performed qRT-PCR experiments and measured global m5C RNA methylation level to validate this signature in vitro and tissue samples. Results. In the TCGA-KIRC cohort, we found significant differences in the expression of m5C RNA methylation-related genes between ccRCC tissues and normal kidney tissues. Consensus cluster analysis was conducted to separate patients into two m5C RNA methylation subtypes. Significantly better outcomes were observed in ccRCC patients in cluster 1 than in cluster 2. m5C RNA methylation-related risk score was calculated to evaluate the prognosis of ccRCC patients by seven screened m5C RNA methylation regulators (NOP2, NSUN2, NSUN3, NSUN4, NSUN5, TET2, and DNMT3B) in the training cohort. The AUC for the 1-, 2-, and 3-year survival in the training cohort were 0.792, 0.675, and 0.709, respectively, indicating that the risk signature had an excellent prognosis prediction in ccRCC. Additionally, univariate and multivariate Cox regression analyses revealed that the risk signature could be an independent prognostic factor in ccRCC. The results of ssGSEA suggested that the immune cells with different infiltration degrees between the high-risk and low-risk groups were T cells including follicular helper T cells, Th1_cells, Th2_cells, and CD8+_T_cells, and the main differences in immune-related functions between the two groups were the interferon response and T cell costimulation. In addition, qRT-PCR experiments confirmed our results in renal cell lines and tissue samples. Conclusions. According to the seven selected regulatory factors of m5C RNA methylation, a risk signature associated with m5C methylation that can independently predict prognosis in patients with ccRCC was developed and further verified the predictive efficiency.

IMPORTANCERelatively little is known about the persistence of symptoms in patients with COVID-19 for more than 1 year after their acute illness. OBJECTIVE To assess the health outcomes among hospitalized COVID-19 survivors over 2 years and to identify factors associated with increased risk of persistent symptoms. DESIGN, SETTING, AND PARTICIPANTS This was a longitudinal cohort study of patients who survived COVID-19 at 2 COVID-19-designated hospitals in Wuhan, China, from February 12 to April 10, 2020. All patients were interviewed via telephone at 1 year and 2 years after discharge. The 2-year follow-up study was conducted from March 1 to April 6, 2022. Statistical analysis was conducted from April 20 to May 5, 2022. The severity of disease was defined by World Health Organization guideline for COVID-19. EXPOSURES COVID-19. MAIN OUTCOMES AND MEASURESThe main outcome was symptom changes over 2 years after hospital discharge. All patients completed a symptom questionnaire for evaluation of symptoms, along with a chronic obstructive pulmonary disease assessment test (CAT) at 1-year and 2-year followup visits. RESULTSOf 3988 COVID-19 survivors, a total of 1864 patients (median [IQR] age, 58.5 [49.0-68.0] years; 926 male patients [49.7%]) were available for both 1-year and 2-year follow-up visits. The median (IQR) time from discharge to follow-up at 2 years was 730 (719-743) days. At 2 years after hospital discharge, 370 patients (19.8%) still had symptoms, including 224 (12.0%) with persisting symptoms and 146 (7.8%) with new-onset or worsening of symptoms. The most common symptoms were fatigue, chest tightness, anxiety, dyspnea, and myalgia. Most symptoms resolved over time, but the incidence of dyspnea showed no significant change (1-year vs 2-year, 2.6% [49 patients] vs 2.0% [37 patients]). A total of 116 patients (6.2%) had CAT total scores of at least 10 at 2 years after discharge. Patients who had been admitted to the intensive care unit had higher risks of persistent symptoms (odds ratio, 2.69; 95% CI, 1.02-7.06; P = .04) and CAT scores of 10 or higher (odds ratio, 2.83; 95% CI, 1.21-6.66; P = .02). CONCLUSIONS AND RELEVANCEIn this cohort study, 2 years after hospital discharge, COVID-19 survivors had a progressive decrease in their symptom burden, but those with severe disease during hospitalization, especially those who required intensive care unit admission, had higher risks of persistent symptoms. These results are related to the original strain of the virus, and their relevance to infections with the Omicron variant is not known.

To establish an effective nomogram for predicting in-hospital mortality of COVID-19, a retrospective cohort study was conducted in two hospitals in Wuhan, China, with a total of 4,086 hospitalized COVID-19 cases. All patients have reached therapeutic endpoint (death or discharge). First, a total of 3,022 COVID-19 cases in Wuhan Huoshenshan hospital were divided chronologically into two sets, one (1,780 cases, including 47 died) for nomogram modeling and the other (1,242 cases, including 22 died) for internal validation. We then enrolled 1,064 COVID-19 cases (29 died) in Wuhan Taikang-Tongji hospital for external validation. Independent factors included age (HR for per year increment: 1.05), severity at admission (HR for per rank increment: 2.91), dyspnea (HR: 2.18), cardiovascular disease (HR: 3.25), and levels of lactate dehydrogenase (HR: 4.53), total bilirubin (HR: 2.56), blood glucose (HR: 2.56), and urea (HR: 2.14), which were finally selected into the nomogram. The C-index for the internal resampling (0.97, 95% CI: 0.95-0.98), the internal validation (0.96, 95% CI: 0.94-0.98), and the external validation (0.92, 95% CI: 0.86-0.98) demonstrated the fair discrimination ability. The calibration plots showed optimal agreement between nomogram prediction and actual observation. We established and validated a novel prognostic nomogram that could predict in-hospital mortality of COVID-19 patients.