Long non-coding RNA musculin antisense RNA 1 (lncRNA MSC-AS1) has been recognized as an oncogene in pancreatic cancer, hepatocellular carcinoma, nasopharyngeal carcinoma, and renal cell carcinoma. However, the functional significance of MSC-AS1 and its underlying mechanism in gastric cancer (GC) progression remain unclear. In this study, we demonstrated that the expression of MSC-AS1 in GC tissues was significantly higher than that in non-tumor tissues. Moreover, the elevated level of MSC-AS1 was detected in GC cells (MKN-45, AGS, SGC-7901, and MGC-803) compared to normal GES-1 gastric mucosal cells. The cancer genome atlas (TCGA) data further indicated that the high level of MSC-AS1 was closely correlated with advanced tumor stage and poor prognosis of GC. Next, we revealed that MSC-AS1 knockdown inhibited the proliferation, glucose consumption, lactate production, and pyruvate production of MGC-803 cells. Conversely, MSC-AS1 overexpression enhanced the proliferation and glycolysis of AGC cells. Mechanistically, modulating MSC-AS1 level affected the expression of 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3), but did not impact the levels of hexokinase 2 (HK2) and pyruvate kinase M2 (PKM2) in GC cells. Based on this, we reversed the MSC-AS1 knockdown-induced the inhibition of cell proliferation and glycolysis by restoring PFKFB3 expression in MGC-803 cells. In conclusion, MSC-AS1 facilitated the proliferation and glycolysis of GC cells by maintaining PFKFB3 expression.

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Abstract

Background Breast angiosarcoma is a rare malignancy with poor survival. Due to the paucity of data, the generation of high-quality evidence for its high-risk features and the impact of treatment modalities on survival have been hampered. Objective To examine high-risk features and the impact of treatment modalities on disease-specific survival (DSS) in breast angiosarcoma and differences between breast angiosarcoma cases with and without other prior cancers. Methods In this retrospective study, patients with breast angiosarcoma diagnosed from 1975 to 2016 were identified from the Surveillance, Epidemiology, and End Results database. Cox proportional hazards regression analysis adjusted for age, race, decade at diagnosis, location, pathologic grade, extent of disease, tumor size, and therapy to model DSS outcomes. Propensity score matching analyses were performed to adjust for the differences between breast angiosarcoma cases with and without other prior cancers to compare their DSS values. A Kaplan-Meier curve was used to visualize the cumulative survival probability. Results Of 648 patients with breast angiosarcoma, 55.4% had a prior cancer diagnosis. Older (age ≥ 70) patients were more likely to have breast angiosarcoma with prior cancer than younger patients (64.3% versus 21.8%). Via multivariate analysis, pathologic grade and extent of disease were identified to be significantly associated with DSS in breast angiosarcoma. In matched data, breast angiosarcoma patients with prior cancer had a better DSS than those without prior cancer (HR = 0.60, 95%CI 0.38–0.96, p = 0.0389). In breast angiosarcoma patients without prior cancer, patients with larger tumor size receiving surgery plus radiation or/and chemotherapy might have a better survival than those patients receiving surgery only (HR = 0.38, 95%CI 0.14–0.99, p = 0.0128). DSS is not impacted by the current therapeutic strategies in unselected breast angiosarcoma patients. Conclusions Breast angiosarcoma patients with prior cancer have a better DSS than those without prior cancer. Additionally, some breast angiosarcoma cases with prior cancer may be cutaneous angiosarcomas. Pathologic grade and extent disease are high-risk features. DSS is not impacted by the current therapeutic strategies in unselected breast angiosarcoma patients.

Backgroud:Tumor mutation burden has become a powerful bio-marker to predict prognosis and immunotherapy responsiveness to patients in various cancers, but the role of TMB in colon cancer is still unclear.Methods:The transcriptome profiling data of colon patients and the simple nucleotide variation data of colon cases were downloaded from the Cancer Genome Atlas (TCGA) database. The groups were divided into high TMB and low TMB group according to the median of TMB. Then we explored the relationship between immune checkpoints, immune cells and TMB, respectively. Results: Mutation profiles of 399 colon cancer samples were analyzed in TCGA database. The senior (age＞65) had a strong relationship with higher-TMB level(p=0.001). Low-TMB group correlated with advanced N stage (P＜0.001), M stage (P＜0.001), and pathologic stage(P＜0.001). High-TMB group had significantly higher mRNA level of PD-L1, TIGIT, HAVCR2, and LAG3 than low-TMB group, which indicated high-TMB referred to better immunotherapy responsiveness in colon cancer. And high-TMB level correlated with higher fractions of CD8T cells (p=0.021), higher CD4 memory T cells(p=0.039), follicular helper T cells (p=0.002)and M1 macrophages (p＜0.001), while the low-TMB groups correlated with higher regulator T cells (p=0.002). So high-TMB correlated with stronger immune cell infiltrationConclusions:The high TMB referred to better clinical pathologic features, better immunotherapy responsiveness and stronger immune cells infiltration in colon cancer. Hence TMB may be a very promising bio-marker to predict prognosis and immunotherapy responsiveness to patients in colon cancer.