As the dominant component of the tumor microenvironment, cancer-associated fibroblasts (CAFs), play a vital role in tumor progression. An increasing number of studies have confirmed that CAFs are involved in almost every aspect of tumors including tumorigenesis, metabolism, invasion, metastasis and drug resistance, and CAFs provide an attractive therapeutic target. This study aimed to explore the feature genes of CAFs for potential therapeutic targets and reliable prediction of prognosis in patients with gastric cancer (GC). Bioinformatic analysis was utilized to identify the feature genes of CAFs in GC by performing an integrated analysis of single-cell and transcriptome RNA sequencing using R software. Based on these feature genes, a CAF-related gene signature was constructed for prognostic prediction by LASSO. Simultaneously, survival analysis and nomogram were performed to validate the prognostic predictive value of this gene signature, and qRT–PCR and immunohistochemical staining verified the expression of the feature genes of CAFs. In addition, small molecular drugs for gene therapy of CAF-related gene signatures in GC patients were identified using the connectivity map (CMAP) database. A combination of nine CAF-related genes was constructed to characterize the prognosis of GC, and the prognostic potential and differential expression of the gene signature were initially validated. Additionally, three small molecular drugs were deduced to have anticancer properties on GC progression. By integrating single-cell and bulk RNA sequencing analyses, a novel gene signature of CAFs was constructed. The results provide a positive impact on future research and clinical studies involving CAFs for GC.

Background Haemolymphangioma arising from the small bowel and its mesentery is extremely rare in the clinical setting. To date, only 8 cases of small bowel haemolymphangioma have been reported, and there have been no previously reported cases of haemolymphangioma in the small bowel mesentery (PubMed). The formation of this tumour is mostly congenital, but the exact mechanism is still unclear. As a benign tumour, the presentation of the disease may vary from a simple well-defined cystic lesion to an aggressive ill-defined lesion mimicking malignancy. However, there are no typical symptoms, and preoperative diagnosis is difficult. Case presentation We present two cases of haemolymphangioma in the small bowel mesentery in a 54-year-old man and a 52-year-old woman. Both of them came to the hospital due to an abdominal mass. In the first case, a cystic teratoma in the left abdominal area was considered after abdominal plain computed tomography (CT) and magnetic resonance imaging (MRI) scans. After taking an enhanced CT scan, a lipoma was considered based on the images. In the second case, cystic masses of the left upper and middle abdomen were observed on abdominal ultrasonography. An abdominal plain CT scan showed an irregular low-density mass in the left upper and middle abdomen. With an enhanced CT scan, haemolymphangioma was considered based on the images. After complete surgical removal, the masses were found to originate from the small bowel mesentery and had not invaded into the peripheral lymphatic tissue. In case 1 in this study, the routine pathology diagnosis was lymphangioma, while in case 2, the diagnosis was haemangioma. The final diagnosis was confirmed to be haemolymphangioma by immunohistochemistry in both cases. No recurrence was evident during 4 months of follow-up. We review the previous case reports of haemolymphangioma in the abdominal cavity and discuss their clinical features, diagnosis, treatment and prognosis. Conclusions The clinical manifestations of abdominal haemolymphangiomas can vary for both location and size. Abdominal CT examination has important clinical value for haemolymphangioma in the abdominal cavity. The final diagnosis of haemolymphangioma depends on a postoperative pathological examination. In addition, postoperative regular follow-up is necessary.

Although many prognostic models have been developed to help determine personalized prognoses and treatments, the predictive efficiency of these prognostic models in hepatocellular carcinoma (HCC), which is a highly heterogeneous malignancy, is less than ideal. Recently, aberrant glycosylation has been demonstrated to universally participate in tumour initiation and progression, suggesting that dysregulation of glycosyltransferases can serve as novel cancer biomarkers. In this study, a total of 568 RNA-sequencing datasets of HCC from the TCGA database and ICGC database were analysed and integrated via bioinformatic methods. LASSO regression analysis was applied to construct a prognostic signature. Kaplan–Meier survival, ROC curve, nomogram, and univariate and multivariate Cox regression analyses were performed to assess the predictive efficiency of the prognostic signature. GSEA and the “CIBERSORT” R package were utilized to further discover the potential biological mechanism of the prognostic signature. Meanwhile, the differential expression of the prognostic signature was verified by western blot, qRT–PCR and immunohistochemical staining derived from the HPA. Ultimately, we constructed a prognostic signature in HCC based on a combination of six glycosyltransferases, whose prognostic value was evaluated and validated successfully in the testing cohort and the validation cohort. The prognostic signature was identified as an independent unfavourable prognostic factor for OS, and a nomogram including the risk score was established and showed the good performance in predicting OS. Further analysis of the underlying mechanism revealed that the prognostic signature may be potentially associated with metabolic disorders and tumour-infiltrating immune cells.