Somatostatin receptor (SSTR) 2, widely expressed in meningioma, is a G-proteincoupled receptor and can be activated by somatostatin or its synthetic analogs. SSTR2 is therefore extensively studied as a marker and target for the diagnosis and treatment of meningioma. Accumulating studies have revealed the crucial clinical significance of SSTR2 in meningioma. Summarizing the progress of these studies is urgently needed as it may not only provide novel and better management for patients with meningioma but also indicate the direction of future research. Pertinent literature is reviewed to summarize the recent collective knowledge and understanding of SSTR2's clinical significance in meningioma in this review. SSTR2 offers novel ideas and approaches in the diagnosis, treatment, and prognostic prediction for meningioma, but more and further studies are required.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and its dismal prognosis indicates the urgent need to elucidate the potential oncogenic mechanisms. SIRT7 is a classic NAD+-dependent deacetylase that stabilizes the transformed state of cancer cells. However, its functional roles in PDAC are still unclear. Here, we found that SIRT7 expression is upregulated and predicts poor prognosis in PDAC. Then we screened the new interacting proteins of SIRT7 by mass spectrometry and the results showed that SIRT7 can interact with O-GlcNAc transferase (OGT). O-GlcNAcylation stabilizes the SIRT7 protein by inhibiting its interaction with REGγ to prevent degradation, and hyper-O-GlcNAcylation in pancreatic cancer cells leads to hypoacetylation of H3K18 via SIRT7, which promotes transcriptional repression of several tumour suppressor genes. In addition, SIRT7 O-GlcNAcylation at the serine 136 residue (S136) is required to maintain its protein stability and deacetylation ability. In vivo and in vitro experiments showed that blocking SIRT7 O-GlcNAcylation at S136 attenuates tumour progression. Collectively, we demonstrate that O-GlcNAcylation is an important post-translational modification of SIRT7 in pancreatic cancer cells, and elucidating this mechanism of SIRT7 is expected to pave the way for the development of novel therapeutic methods in the future.

Regulated necrosis is an emerging type of cell death independent of caspase. Recently, with increasing findings of regulated necrosis in the field of biochemistry and genetics, the underlying molecular mechanisms and signaling pathways of regulated necrosis are gradually understood. Nowadays, there are several modes of regulated necrosis that are tightly related to cancer initiation and development, including necroptosis, ferroptosis, parthanatos, pyroptosis, and so on. What’s more, accumulating evidence shows that various compounds can exhibit the anti-cancer effect via inducing regulated necrosis in cancer cells, which indicates that caspase-independent regulated necrosis pathways are potential targets in cancer management. In this review, we expand the molecular mechanisms as well as signaling pathways of multiple modes of regulated necrosis. We also elaborate on the roles they play in tumorigenesis and discuss how each of the regulated necrosis pathways could be therapeutically targeted.

Background Surgical resection is the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC) and the survival of patients after radical resection is closely related to relapse. We aimed to develop models to predict the risk of relapse using machine learning methods based on multiple clinical parameters. Methods Data were collected and analysed of 262 PDAC patients who underwent radical resection at 3 institutions between 2013 and 2017, with 183 from one institution as a training set, 79 from the other 2 institution as a validation set. We developed and compared several predictive models to predict 1- and 2-year relapse risk using machine learning approaches. Results Machine learning techniques were superior to conventional regression-based analyses in predicting risk of relapse of PDAC after radical resection. Among them, the random forest (RF) outperformed other methods in the training set. The highest accuracy and area under the receiver operating characteristic curve (AUROC) for predicting 1-year relapse risk with RF were 78.4% and 0.834, respectively, and for 2-year relapse risk were 95.1% and 0.998. However, the support vector machine (SVM) model showed better performance than the others for predicting 1-year relapse risk in the validation set. And the k neighbor algorithm (KNN) model achieved the highest accuracy and AUROC for predicting 2-year relapse risk. Conclusions By machine learning, this study has developed and validated comprehensive models integrating clinicopathological characteristics to predict the relapse risk of PDAC after radical resection which will guide the development of personalized surveillance programs after surgery.