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Fanconi anemia (FA) is a rare autosomal recessive, X-linked or autosomal dominant disease. Few large-scale FA investigations of rare disease cohorts have been conducted in China.We enrolled 148 patients diagnosed with FA according to evidence from the clinical phenotype, family history, and a set of laboratory tests. Next, the clinical manifestations and correlation between the genotype and phenotype of FA pediatric cases were investigated.The most common FA subtype in our cohort was FA-A (51.4 %), followed by FA-D2 and FA-P. Finger (26 %) and skin (25 %) deformities were the most common malformations. Based on family history, blood system diseases (51 %) had the highest incidence rate, followed by digestive system tumours. A set of new or prognosis-related mutation sites was identified. For example, c.2941 T G was a new most common missense mutation site for FANCA. FANCP gene mutation sites were mainly concentrated in exons 12/14/15. The mutations of FANCI/FANCD2 were mainly located at the α helix and β corners of the protein complex. FA-A/D1 patients with splicing or deletion mutations showed more severe disease than those with missense mutations. Chromosome 1/3/7/8 abnormalities were closely linked to the progression of FA to leukemia.Our study investigated the clinical features and genotype/phenotype correlation of 148 Chinese pediatric FA patients, providing new insight into FA.

Background: Severe Aplastic Anemia (SAA) in children is a rare severe disease, and the prognosis after immunosuppressive therapy (IST) is heterogeneous. Few models could accurately predict the long-term outcomes of immunosuppressive therapy for children SAA patients. Previous researches mainly focused on a few pretreatment factors,because SAA patients need to test the bone marrow at 3, 6, 9, and 12 months after IST treatment, and response-based surrogates of outcome have been shown to be highly predictive in other diseases. Based on these, we collected clinical electronic medical records (EMR) from 203 children with SAA, including comprehensive clinical tests from blood routine to bone marrow examination throughout the entire treatment process, to establish a model with more effective prognostic factors to predict the prognosis of patients. Our study will provides a novel and more effective prognosis time node for reducing the times of bone marrow puncture and to guide the next treatment. Methods: Based on the machine learning methods, we established a model with the AUC 0.962 to predict the long-term outcomes in the early stage of SAA patients with IST. Findings: By analyzing the indicators related to long-term efficacy, we found that some of the indicators such as white blood cell count, lymphocyte count and absolute reticulocyte count (ARC) which are consistent with previous studies; but the age is not a suitable predictor for children with SAA, the lymphocyte ratio of bone marrow smear is more effective than lymphocyte count in blood, the C-reactive protein, level of vitamin B12, IL-6 and IL-8 in the early stage of the disease is highly correlated with long-term efficacy (P