Circular RNAs (circRNAs) have been implicated in cancer progression through largely unknown mechanisms. Herein, we identify an N⁶-methyladenosine (m⁶A) modified circRNA, circNSUN2, frequently upregulated in tumor tissues and serum samples from colorectal carcinoma (CRC) patients with liver metastasis (LM) and predicts poorer patient survival. The upregulated expression of circNSUN2 promotes LM in PDX metastasis models in vivo and accelerates cancer cells invasion in vitro. Importantly, N⁶-methyladenosine modification of circNSUN2 increases export to the cytoplasm. By forming a circNSUN2/IGF2BP2/HMGA2 RNA-protein ternary complex in the cytoplasm, circNSUN2 enhances the stability of HMGA2 mRNA to promote CRC metastasis progression. Clinically, the upregulated expressions of circNSUN2 and HMGA2 are more prevalent in LM tissues than in primary CRC tissues. These findings elucidate that N⁶-methyladenosine modification of circNSUN2 modulates cytoplasmic export and stabilizes HMGA2 to promote CRC LM, and suggest that circNSUN2 could represent a critical prognostic marker and/or therapeutic target for the disease.

Nicotinamide adenine dinucleotide phosphate (NADPH) is an essential electron donor in all organisms, and provides the reducing power for anabolic reactions and redox balance. NADPH homeostasis is regulated by varied signaling pathways and several metabolic enzymes that undergo adaptive alteration in cancer cells. The metabolic reprogramming of NADPH renders cancer cells both highly dependent on this metabolic network for antioxidant capacity and more susceptible to oxidative stress. Modulating the unique NADPH homeostasis of cancer cells might be an effective strategy to eliminate these cells. In this review, we summarize the current existing literatures on NADPH homeostasis, including its biological functions, regulatory mechanisms and the corresponding therapeutic interventions in human cancers, providing insights into therapeutic implications of targeting NADPH metabolism and the associated mechanism for cancer therapy.

Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients. So far, computer-aided diagnosis has not been widely applied in pathological field yet as currently well-addressed tasks are only the tip of the iceberg. Whole slide image (WSI) classification is a quite challenging problem. First, the scarcity of annotations heavily impedes the pace of developing effective approaches. Pixelwise delineated annotations on WSIs are time consuming and tedious, which poses difficulties in building a large-scale training dataset. In addition, a variety of heterogeneous patterns of tumor existing in high magnification field are actually the major obstacle. Furthermore, a gigapixel scale WSI cannot be directly analyzed due to the immeasurable computational cost. How to design the weakly supervised learning methods to maximize the use of available WSI-level labels that can be readily obtained in clinical practice is quite appealing. To overcome these challenges, we present a weakly supervised approach in this article for fast and effective classification on the whole slide lung cancer images. Our method first takes advantage of a patch-based fully convolutional network (FCN) to retrieve discriminative blocks and provides representative deep features with high efficiency. Then, different context-aware block selection and feature aggregation strategies are explored to generate globally holistic WSI descriptor which is ultimately fed into a random forest (RF) classifier for the image-level prediction. To the best of our knowledge, this is the first study to exploit the potential of image-level labels along with some coarse annotations for weakly supervised learning. A large-scale lung cancer WSI dataset is constructed in this article for evaluation, which validates the effectiveness and feasibility of the proposed method. Extensive experiments demonstrate the superior performance of our method that surpasses the state-of-the-art approaches by a significant margin with an accuracy of 97.3% . In addition, our method also achieves the best performance on the public lung cancer WSIs dataset from The Cancer Genome Atlas (TCGA). We highlight that a small number of coarse annotations can contribute to further accuracy improvement. We believe that weakly supervised learning methods have great potential to assist pathologists in histology image diagnosis in the near future.

The 2023 update of the Chinese Society of Clinical Oncology (CSCO) Clinical Guidelines for Gastric Cancer focuses on standardizing cancer diagnosis and treatment in China, reflecting the latest advancements in evidence-based medicine, healthcare resource availability, and precision medicine. These updates address the differences in epidemiological characteristics, clinicopathological features, tumor biology, treatment patterns, and drug selections between Eastern and Western gastric cancer patients. Key revisions include a structured template for imaging diagnosis reports, updated standards for molecular marker testing in pathological diagnosis, and an elevated recommendation for neoadjuvant chemotherapy in stage III gastric cancer. For advanced metastatic gastric cancer, the guidelines introduce new recommendations for immunotherapy, anti-angiogenic therapy and targeted drugs, along with updated management strategies for human epidermal growth factor receptor 2 (HER2)-positive and deficient DNA mismatch repair (dMMR)/microsatellite instability-high (MSI-H) patients. Additionally, the guidelines offer detailed screening recommendations for hereditary gastric cancer and an appendix listing drug treatment regimens for various stages of gastric cancer. The 2023 CSCO Clinical Guidelines for Gastric Cancer updates are based on both Chinese and international clinical research and expert consensus to enhance their applicability and relevance in clinical practice, particularly in the heterogeneous healthcare landscape of China, while maintaining a commitment to scientific rigor, impartiality, and timely revisions.

Substantial evidence suggests that the presence of inflammatory cells plays a critical role in the development and/or progression of human tumors. Neutrophils are the common inflammatory cells in tumors; however, the infiltration of intratumoral neutrophils in colorectal carcinoma (CRC) and its effect on CRC patients' prognosis are poorly understood. In this study, the methods of tissue microarray and immunohistochemistry (IHC) were used to investigate the prognostic significance of intratumoral CD66b+ neutrophil in CRC. According to receiver operating characteristic curve analysis, the cutoff score for high intratumoral CD66b+ neutrophil in CRC was defined when the mean counts were more than 60 per TMA spot. In our study, high intratumoral CD66b+ neutrophil was observed in 104/229 (45.4%) of CRCs and in 29/229 (12.7%) of adjacent mucosal tissues. Further correlation analysis showed that high intratumoral neutrophil was positively correlated with pT status, pM status and clinical stage (P<0.05). In univariate survival analysis, a significant association between high intratumoral neutrophil and shortened patients' survival was found (P<0.0001). In different subsets of CRC patients, intratumoral neutrophil was also a prognostic indicator in patients with stage II, stage III, grade 2, grade 3, pT1, pT2, pN0 and pN1 (P<0.05). Importantly, high intratumoral neutrophil was evaluated as an independent prognostic factor in multivariate analysis (P<0.05). Our results provide evidence that increased intratumoral neutrophil in CRC may be important in the acquisition of a malignant phenotype, indicating that the presence of intratumoral neutrophil is an independent factor for poor prognosis of patients with CRC.