The prognostic signature based on glycolysis-immune related genes for acute myeloid leukemia patients

Acute myeloid leukemia (AML) is widely considered an immunoresponsive malignancy. However, potential association between glycolysis-immune related genes and AML patients’ prognosis has been seldom studied. AML-related data was downloaded from TCGA and GEO databases. We grouped patients according to Glycolysis status, Immune Score and combination analysis, basing on which overlapped differentially expressed genes (DEGs) were identified. The Risk Score model was then established. The results showed that totally 142 overlapped genes were probably correlated with glycolysis-immunity in AML patients, among which 6 optimal genes were screened to construct Risk Score. High Risk Score was an independent poor prognostic factor for AML. In conclusion, we established a relatively reliable prognostic signature of AML based on glycolysis-immunity related genes, including METTL7B, HTR7, ITGAX, TNNI2, SIX3 and PURG.     

FEN1 inhibitor SC13 promotes CAR-T cells infiltration into solid tumours through cGAS–STING signalling pathway

It is well known that chimeric antigen receptor T-cell immunotherapy (CAR-T-cell immunotherapy) has excellent therapeutic effect in haematological tumours, but it still faces great challenges in solid tumours, including inefficient T-cell tumour infiltration and poor functional persistence. Flap structure-specific endonuclease 1 (FEN1), highly expressed in a variety of cancer cells, plays an important role in both DNA replication and repair. Previous studies have reported that FEN1 inhibition is an effective strategy for cancer treatment. Therefore, we hypothesized whether FEN1 inhibitors combined with CAR-T-cell immunotherapy would have a stronger killing effect on solid tumours. The results showed that low dose of FEN1 inhibitors SC13 could induce an increase of double-stranded broken DNA (dsDNA) in the cytoplasm. Cytosolic dsDNA can activate the cyclic GMP–AMP synthase–stimulator of interferon gene signalling pathway and increase the secretion of chemokines. In vivo, under the action of FEN1 inhibitor SC13, more chemokines were produced at solid tumour sites, which promoted the infiltration of CAR-T cells and improved anti-tumour immunity. These findings suggest that FEN1 inhibitors could enable CAR-T cells to overcome poor T-cell infiltration and improve the treatment of solid tumours.     

肝癌微波消融术后患者复发的危险因素分析

目的探讨胆囊切除对行肝癌微波消融（microwave ablation,MWA）患者预后的影响。 方法72例接受MWA治疗肝癌的患者,分为胆囊切除组（36例）和非胆囊切除组（36例）,分析总生存率（overall survival,OS）和无进展生存率（progression free survival,PFS）,比较胆囊切除组和非胆囊切除组的预后结果。采用单因素和多因素Cox分析评估总生存率和无进展生存率的潜在危险因素以及比较两组之间的预后。 结果本研究胆囊切除组纳入36例（50.00%）,非胆囊切除组纳入36例（50.00%）。胆囊切除组中位OS为35.55个月（4.20～36.00个月）,非胆囊切除组31.19个月（10.80～36.00个月） （P=0.894）。随访结束前,胆囊切除组和非胆囊切除组的死亡率分别为22.22%和22.22%。胆囊切除组1、2、3年累积总生存率分别为91.67%、79.91%、75.71%,非胆囊切除组分别为97.22%、88.72%和73.81%（P=0.97）。胆囊切除组中位PFS为7.67个月（1.68～32.30个月）,非胆囊切除组为18.25个月（2.24～33.60个月） （P<0.01）。随访结束时,胆囊切除组和非胆囊切除组肝癌复发率分别为69.44%和91.67%,胆囊切除组1、2、3年累积无进展生存率分别为36.11%、16.67%、0.00%,非胆囊切除组分别为77.78%、46.89%和0.00%。非胆囊切除组的累积无进展生存率明显高于胆囊切除组（P<0.01）。多因素分析显示肿瘤数量为3（HR=18.91,95%CI：1.54～232.99,P=0.02）是与OS相关的独立危险因素。多因素分析显示胆囊切除术（HR=3.55,95%CI：1.74～7.26,P<0.01）,肿瘤数量为2和3（HR=2.21,95%CI：1.10～4.42,P=0.02;HR=3.63,95%CI：1.26～10.45,P=0.02）和AFP≥400 ng/mL（HR=0.43,95%CI：0.19～0.98,P<0.05）是与PFS相关的独立危险因素。 结论肝细胞癌患者在MWA后行胆囊切除术后更易发生肝内复发,这可能与γ-GT水平升高有关,且复发率随时间增加而增加。     

Paternity testing and other inference about relationships from DNA mixtures

We present methods for inference about relationships between contributors to a DNA mixture and other individuals of known genotype: a basic example would be testing whether a contributor to a mixture is the father of a child of known genotype. The evidence for such a relationship is evaluated as the likelihood ratio for the specified relationship versus the alternative that there is no relationship. We analyse real casework examples from a criminal case and a disputed paternity case; in both examples part of the evidence was from a DNA mixture. DNA samples are of varying quality and therefore present challenging problems in interpretation. Our methods are based on a recent statistical model for DNA mixtures, in which a Bayesian network (BN) is used as a computational device; the present work builds on that approach, but makes more explicit use of the BN in the modelling. The R code for the analyses presented is freely available as supplementary material.We show how additional information of specific genotypes relevant to the relationship under analysis greatly strengthens the resulting inference. We find that taking full account of the uncertainty inherent in a DNA mixture can yield likelihood ratios very close to what one would obtain if we had a single source DNA profile. Furthermore, the methods can be readily extended to analyse different scenarios as our methods are not limited to the particular genotyping kits used in the examples, to the allele frequency databases used, to the numbers of contributors assumed, to the number of traces analysed simultaneously, nor to the specific hypotheses tested.