A case report: Castleman disease treated by the trinity of internal and external treatment of "Fuzheng, phlegm-resolving, and detoxification method".

RATIONALE: Castleman disease (CD) was first included in the CSCO lymphoma diagnosis and treatment guidelines in 2021. Its diagnosis relies on lymph node pathological examination. Observation, surgical resection of the lesion, radiotherapy, chemotherapy, and medical therapy (e.g., rituximab, siltuximab, steroids) can be used. Due to the traumatic, incurable, and recurrent nature of surgical treatment, drug therapy has many side effects and is expensive. Exploring effective traditional Chinese medicine (TCM) comprehensive treatment methods for this disease is important and necessary. PATIENT CONCERNS: The main symptom was recurrent lymphadenopathy, which had been surgically removed 5 times in the past. This time, lymph node enlargement occurred again, and the local hospital recommended surgical resection again. The patient could not tolerate another surgical treatment. Other targeted treatments are not available due to financial constraints. DIAGNOSES: The case was diagnosed as CD by pathological examination, which is an important basis for the diagnosis of this disease. INTERVENTIONS: The patient was treated with surgery in the early stage several times, later came to our hospital for the trinity of TCM integrated treatment program, which combines oral TCM with external application of TCM and intravenous drip of TCM as a syndrome of positive deficiency and phlegm-toxin internalization, and the therapeutic principle. OUTCOMES: After nearly 3 years of comprehensive treatment with TCM, the enlarged lymph nodes could not be touched, and there was no fatigue, fever, or weight loss. During this period, the patient did not undergo surgery, chemotherapy and other western medicine treatment, and lived a normal life. It not only met the patient's expectation but also confirmed that the TCM treatment was indeed effective. LESSONS: This case report confirms that TCM is safe and effective in the treatment of CD, which is worthy of promotion. In clinical practice, the individualized treatment for the patient, the duration of treatment, and the different disease states also affect the treatment outcome.

Identification of Six Diagnostic Biomarkers for Chronic Lymphocytic Leukemia Based on Machine Learning Algorithms.

Background: Chronic lymphocytic leukemia (CLL) is the most common type of leukemia in adults. Thus, novel reliable biomarkers need to be further explored to increase diagnostic, therapeutic, and prognostic effectiveness. Methods: Six datasets containing CLL and control samples were downloaded from the Gene Expression Omnibus database. Differential gene expression analysis, weighted gene coexpression network analysis (WGCNA), and the least absolute shrinkage and selection operator (LASSO) regression were applied to identify potential diagnostic biomarkers for CLL using R software. The diagnostic performance of the hub genes was then measured by the receiver operating characteristic (ROC) curve analysis. Functional analysis was implemented to uncover the underlying mechanisms. Additionally, correlation analysis was performed to assess the relationship between the hub genes and immunity characteristics. Results: A total number of 47 differentially expressed genes (DEGs) and 25 candidate hub genes were extracted through differential gene expression analysis and WGCNA, respectively. Based on the 14 overlapped genes between the DEGs and the candidate hub genes, LASSO regression analysis was used, which identified a final number of six hub genes as potential biomarkers for CLL: ABCA6, CCDC88A, PMEPA1, EBF1, FILIP1L, and TEAD2. The ROC curves of the six genes showed reliable predictive ability in the training and validation cohorts, with all area under the curve (AUC) values over 0.80. Functional analysis revealed an abnormal immune status in the CLL patients. A significant correlation was found between the hub genes and the immune-related pathways, indicating a possible tight connection between the hub genes and tumor immunity in CLL. Conclusion: This study was based on machine learning algorithms, and we identified six genes that could be possible CLL markers, which may be involved in CLL pathogenesis and progression through immune-related signal pathways.