Background The phenomenon of males exhibiting higher lymphoma incidence and mortality rates than females has been widely recognized, and many survival analyses of certain lymphoma subtypes have incorporated sex as a critical prognostic determinant. However, comprehensive research on these sex-based differences in lymphomas remains limited. Methods This study aimed to conduct an integrated population-based analysis of sex-based disparities across various lymphoma subtypes using the Surveillance, Epidemiology, and End Results (SEER) database. We assessed the age-adjusted incidence rates of cases in the SEER22 and incidence-based mortality rates in the SEER12. The rates were stratified according to subtype, sex, race/ethnicity (incidence and mortality), age at diagnosis, and year of diagnosis (incidence only). Furthermore, we compared the risks of all-cause mortality, cancer-specific mortality, and cardiovascular disease-related mortality based on sex in SEER17. Results Our findings revealed that males had a higher likelihood of developing lymphoma and exhibited higher mortality rates than females for most lymphoma subtypes. These sex-based disparities in incidence and mortality were relatively consistent across five racial/ethnic groups. However, sex-based disparities in cancer incidence varied among age groups, and a trend toward narrowing differences with increasing age was observed in nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL), mantle cell lymphoma, and Burkitt lymphoma. Over the past two decades, trends in sex-based disparities of lymphoma incidence have remained stable. Regarding survival outcomes, males faced an increased risk of death compared to females for most lymphoma subtypes, except for NLPHL. Conclusions The results of our study underscore the complex and multifaceted nature of sex-based disparities in lymphoma and may offer valuable guidance for future research to unravel the potential causes of these differences.

Background:Acute myeloid leukemia (AML) with t(8;21) manifests as a diverse hematological malignancy. Although it was categorized into a favorable subtype, 30-40% of patients experience relapse. The objective of this research was to devise a nomogram for the accurate anticipation of both overall survival (OS) and cancer-specific survival (CSS) in t(8;21) AML. Methods:From the Surveillance, Epidemiology, and End Results (SEER) database, individuals diagnosed with t(8;21) AML from 2000 to 2018 were selected. Prognostic factors for t(8;21) AML were identified using Cox regression analysis and Akaike Information Criterion (AIC), forming the basis for constructing prognostic nomograms. Results:Key variables, including first primary tumor, age group, race, and chemotherapy, were identified and integrated into the nomogram. The C-index values for the nomograms predicting OS and CSS were 0.753 (validation: 0.765) and 0.764 (validation: 0.757), respectively. Ultimately, based on nomogram scores, patients were stratified into high-risk and low-risk groups, revealing significant disparities in both OS and CSS between these groups (P < 0.001). Conclusion:This study innovatively crafted nomograms, incorporating clinical and therapeutic variables, to forecast the 1-, 3-, and 5-year survival rates for individuals with t(8;21) AML.

This retrospective study aimed to analyze the treatment effect and prognostic factors of pediatric acute myeloid leukemia (AML) patients with t(8;21). A total of 268 newly diagnosed pediatric AML (pAML) enrolled from 1 January 2005 to 31 December 2022 were retrospectively reviewed, and 50 (18.7%) patients harbored t(8;21) translocation. CR rate, OS, EFS, and RFS were assessed by multivariate Logistic and Cox regression models in these patients. Of the 50 patients, 2 patients abandoned treatment during the first induction course. Of the remaining 48 patients who received double-induction therapy and were included in the final analyses, CR1 and CR2 were 75.0% (36/48) and 95.8% (46/48), respectively. The overall three-year OS, EFS, and RFS were 68.4% (95% CI, 55.0–85.1), 64.2% (95% CI, 50.7–81.4), and 65.5% (95% CI, 51.9–82.8), respectively. The presence of loss of sex chromosome (LOS) at diagnosis (n = 21) was associated with a better 3-year OS [87.5% (95% CI, 72.7–100) vs. 52.7% (95% CI, 35.1–79.3), p = 0.0089], 3-year EFS [81.6% (95% CI, 64.7–100) vs. 49.7% (95% CI, 32.4–76.4), p = 0.023], and 3-year RFS [81.6% (95% CI, 64.7–100) vs. 51.7% (95% CI, 33.9–78.9), p = 0.036] than those without LOS (n = 27), and it was also an independent good prognostic factor of OS (HR, 0.08 [95% CI, 0.01–0.48], p = 0.005), EFS (HR, 0.22 [95% CI, 0.05–0.85], p = 0.029), and RFS (HR, 0.21 [95% CI, 0.05–0.90], p = 0.035). However, extramedullary leukemia (EML) featured the independent risk factors of inferior OS (HR, 10.99 [95% CI, 2.08–58.12], p = 0.005), EFS (HR, 4.75 [95% CI, 1.10–20.61], p = 0.037), and RFS (HR, 6.55 [95% CI, 1.40–30.63], p = 0.017) in pediatric individuals with t(8;21) AML. Further analysis of combining LOS with EML indicated that the EML+LOS− subgroup had significantly inferior OS (92.9%, [95% CI, 80.3–100]), EFS (86.2%, [95% CI, 70.0–100]), and RFS (86.2%, [95% CI, 80.3–100]) compared to the other three subgroups (all p < 0.001). LOS and EML are independent prognostic factors of OS, EFS, and RFS with t(8;21) pAML patients. LOS combined with EML may help improve risk stratification.

Hematopoiesis starts from the embryo and runs through the entire life of a living body, which is a multi-stage and complex dynamic process that is regulated by multiple pathways, involving a variety of cells and hematopoietic anatomical locations. During the development of the mammalian hematopoietic system, the currently known hematopoietic anatomical locations mainly include yolk sac, aorta-gonad-mesonephros, fetal liver, bone marrow, and thymus. The first three are mainly responsible for hematopoiesis during the embryonic and fetal period, while bone marrow is the main place for postnatal hematopoiesis, and thymus is mainly responsible for the differentiation of T lymphocytes. Integrating flow cytometry, in vitro cell culture and in vivo animal transplantation models, early researchers conducted an in-depth analysis of the differentiation pathways of hematopoietic cells. However, due to technical limitations, it is difficult to track the single-cell hematopoietic activity of hematopoietic organs. Transcriptome sequencing at the single-cell level provides researchers with a unique perspective, making it possible to draw the most detailed cell fate transition maps of the hematopoietic development of living organisms, and providing new ideas for the diagnosis and treatment of hematological tumors. In this article, we reviewed the research progress in the use of large-scale single-cell transcriptome sequencing in the field of physiological hematopoiesis in recent years.

Background:Myeloid sarcoma (MS) is a rare hematological tumor that presents with extramedullary tumor masses comprising myeloid blasts. A controversial issue is whether MS involving normal hematopoietic sites (liver, spleen, and lymph nodes) should be excluded in future studies. We aimed to compare MS characteristics and outcomes involving hematopoietic and non-hematopoietic sites and construct a prognostic nomogram exclusively for the latter.Methods:Data from patients diagnosed with MS between 2000 and 2018 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. According to the primary site, patients were classified as having MS involving hematopoietic sites (hMS) or non-hematopoietic sites (eMS). Clinical characteristics and survival outcomes were compared between the two groups using Wilcoxon, chi-square, and log-rank tests. Cox regression analysis was used to identify eMS prognostic factors to establish prognostic nomograms. The models' efficiency and value were assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).Results:In total, 694 patients were enrolled, including 86 with hMS and 608 with eMS. There were no sex, race or marital status distribution differences between the two groups. Patients with eMS had better overall and cancer-specific survival rates than those with hMS. Additionally, prognostic factor effects differed between the two groups. Patients with eMS were randomly divided into the training (number of patiens, n=425) and validation cohorts (n=183). Age, first primary tumor, primary site, and chemotherapy were used to establish nomograms. The C-index values of overall survival (OS) and cancer-specific survival (CSS) nomograms were 0.733 (validation: 0.728) and 0.722 (validation: 0.717), respectively. Moreover, ROC, calibration curves, and DCA confirmed our models' good discrimination and calibration ability and potential clinical utility value.Conclusion:Our study described the differences between patients with eMS and those with hMS. Moreover, we developed novel nomograms based on clinical and therapeutic factors to predict patients with eMS' 1-, 3- and 5-year survival rates.