Background: Systemic inflammatory response is a hallmark of cancer and plays a significant role in the development and progression of various malignant tumors. This research aimed to estimate the prognostic function of the C-reactive protein-albumin ratio (CAR) in patients undergoing hepatectomy for hepatocellular carcinoma (HCC) and compare it with other inflammation-based prognostic scores, including the neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, monocyte-lymphocyte ratio, systemic immune inflammation index, prognostic index, Glasgow prognostic score, and modified Glasgow prognostic score. Methods: Retrospective analysis was conducted on data from 1039 HCC cases who underwent curative liver resection. The prognostic performance of CAR was compared with other scores using the area under the time-dependent receiver operating characteristic (t-ROC) curve. Multivariable Cox regression analyses were performed to confirm independent predictors for disease-free survival (DFS) and overall survival (OS). Results: The area under the t-ROC curve for CAR in the evaluation of DFS and OS was significantly greater than that of other scores and alpha-fetoprotein (AFP). Patients were stratified based on the optimal cut-off value of CAR, and the data revealed that both DFS and OS were remarkably worse in the high-CAR set compared to the low-CAR set. Multivariable Cox analysis demonstrated that CAR was an independent prognostic parameters for assessing DFS and OS. Regardless of AFP levels, all patients were subsequently divided into significantly different subgroups of DFS and OS based on CAR risk stratification. Similar results were observed when applying CAR risk stratification to other scoring systems. CAR also showed good clinical applicability in patients with different clinical features. Conclusion: CAR is a more effective inflammation-based prognostic marker than other scores and AFP in predicting DFS as well as OS among patients with HCC after curative hepatectomy.

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous tumor, and the development of accurate predictive models for prognosis and drug sensitivity remains challenging.Methods: We integrated laboratory data and public cohorts to conduct a multi-omics analysis of HCC, which included bulk RNA sequencing, proteomic analysis, single-cell RNA sequencing (scRNA-seq), spatial transcriptomics sequencing (ST-seq), and genome sequencing. We constructed a tumor purity (TP) and tumor microenvironment (TME) prognostic risk model. Proteomic analysis validated the TP-TME-related signatures. Joint analysis of scRNA-seq and ST-seq revealed characteristic clusters associated with TP high-risk subtypes, and immunohistochemistry confirmed the expression of key genes. We conducted functional enrichment analysis, transcription factor activity inference, cell-cell interaction, drug efficacy analysis, and mutation information analysis to identify a novel subtype of HCC.Results: Our analyses constructed a robust HCC prognostic risk prediction model. The patients with TP-TME high-risk subtypes predominantly exhibit hypoxia and activation of the Wnt/beta-catenin, Notch, and TGF-beta signaling pathways. Furthermore, we identified a novel subtype, XPO1+Epithelial. This subtype expresses signatures of the TP risk subtype and aligns with the biological behavior of high-risk patients. Additional analyses revealed that XPO1+Epithelial is influenced primarily by fibroblasts via ligand-receptor interactions, such as FN1-(ITGAV+ITGB1), and constitute a significant component of the TP-TME subtype. Moreover, XPO1+Epithelial interact with monocytes/macrophages, T/NK cells, and endothelial cells through ligand-receptor pairs, including MIF-(CD74+CXCR4), MIF-(CD74+CD44), and VEGFA-VEGFR1R2, respectively, thereby promoting the recruitment of immune-suppressive cells and angiogenesis. The ST-seq cohort treated with Tyrosine Kinase Inhibitors (TKIs) and Programmed Cell Death Protein 1 (PD-1) presented elevated levels of TP and TME risk subtype signature genes, as well as XPO1+Epithelial, T-cell, and endothelial cell infiltration in the treatment response group. Drug sensitivity analyses indicated that TP-TME high-risk subtypes, including sorafenib and pembrolizumab, were associated with sensitivity to multiple drugs. Further exploratory analyses revealed that CTLA4, PDCD1, and the cancer antigens MSLN, MUC1, EPCAM, and PROM1 presented significantly increase expression levels in the high-risk subtype group.Conclusions: This study constructed a robust prognostic model for HCC and identified novel subgroups at the single-cell level, potentially assisting in the assessment of prognostic risk for HCC patients and facilitating personalized drug therapy.

Hepatocellular carcinoma (HCC) is one of the most common malignancies and a leading cause of cancer-related death worldwide. Surgery remains the primary an

Bioinformatics tools were used to identify prognosis-related molecular subtypes and biomarkers of hepatocellular carcinoma (HCC). Differential expression analysis of four datasets identified 3330 overlapping differentially expressed genes (DEGs) in the same direction in all four datasets. Those genes were involved in the cell cycle, FOXO signaling pathway, as well as complement and coagulation cascades. Based on non-negative matrix decomposition, two molecular subtypes of HCC with different prognoses were identified, with subtype C2 showing better overall survival than subtype C1. Cox regression and Kaplan-Meier analysis showed that 217 of the overlapping DEGs were closely associated with HCC prognosis. The subset of those genes showing an area under the curve >0.80 was used to construct random survival forest and least absolute shrinkage and selection operator models, which identified seven feature genes (SORBS2, DHRS1, SLC16A2, RCL1, IGFALS, GNA14, and FANCI) that may be involved in HCC occurrence and prognosis. Based on the feature genes, risk score and recurrence models were constructed, while a univariate Cox model identified FANCI as a key gene involved mainly in the cell cycle, DNA replication, and mismatch repair. Further analysis showed that FANCI had two mutation sites and that its gene may undergo methylation. Single-sample gene set enrichment analysis showed that Th2 and T helper cells are significantly upregulated in HCC patients compared to controls. Our results identify FANCI as a potential prognostic biomarker for HCC.

Low-grade myofibroblastic sarcoma (LGMS) is a rare, poorly differentiated, malignant tumor. The disease mainly occurs in the head and neck and rarely metastasizes at any age. Currently, there is no consensus regarding the treatment of metastatic LGMS. Here, we report the case of a 45-year-old man who was admitted to our hospital with cough for two weeks and abdominal pain for one week. Preoperative computed tomography revealed a large mass (116× 35 mm) in both the lungs and jejunal mass (maximum diameter, 32 mm). The tumor was excised, and immunohistochemical staining was positive for SMA and CD117 and negative for desmin and CD34, indicating a case of LGMS. The patient was effectively treated with apatinib (250 mg/day) after failure of imatinib, liposomal doxorubicin, and ifosfamide. The progression-free survival time was 8.5 months, and the overall survival time was 17 months after treatment with apatinib. No grade 3 or 4 side effects were observed, except hand-foot syndrome. Whole-exome sequencing (WES) and RNA sequencing (RNA-seq) were performed on the patient’s jejunal tumor and para-cancerous tissue samples, and bioinformatics analysis was performed on the results. WES identified five mutations in MKI67, OR2J2, EPPK1, FCGBP, and OR10G4. RNA-seq revealed that 1422 genes were upregulated and 1890 genes were downregulated. The differentially expressed genes were mainly enriched in the phosphatidylinositol 3-kinase (PI3K) signaling pathway, neuroactive ligand-receptor interaction signaling pathway, and cAMP signaling pathway. Our study indicated that apatinib may be a potential novel and effective treatment for LGMS.

Background: Hepatocellular carcinoma (HCC) is a life-threatening human malignancy and the fourth leading cause of cancer-related deaths worldwide. Patients with HCC are often diagnosed at an advanced stage with a poor prognosis. Sorafenib is a multikinase inhibitor used as the first-line treatment for patients with advanced HCC. However, acquired resistance to sorafenib in HCC leads to tumor aggression and limits the drug’s survival benefits; the underlying molecular mechanisms for this resistance remain unclear. Methods: This study aimed to examine the role of the tumor suppressor RBM38 in HCC, and its potential to reverse sorafenib resistance. In addition, the molecular mechanisms underlying the binding of RBM38 and the lncRNA GAS5 were examined. The potential involvement of RBM38 in sorafenib resistance was examined using both in vitro and in vivo models. Functional assays were performed to assess whether RBM38: binds to and promotes the stability of the lncRNA GAS5; reverses the resistance of HCC to sorafenib in vitro; and suppresses the tumorigenicity of sorafenib-resistant HCC cells in vivo. Results: RBM38 expression was lower in HCC cells. The IC₅₀ value of sorafenib was significantly lower in cells with RBM38 overexpression than in control cells. RBM38 overexpression improved sorafenib sensitivity in ectopic transplanted tumors and suppressed the growth rate of tumor cells. RBM38 could bind to and stabilize GAS5 in sorafenib-resistant HCC cells. In addition, functional assays revealed that RBM38 reversed sorafenib resistance both in vivo and in vitro in a GAS5-dependent manner. Conclusions: RBM38 is a novel therapeutic target that can reverse sorafenib resistance in HCC by combining and promoting the lncRNA GAS5.