Glycolysis is known as the main pathway for ATP production in cancer cells. However, in cancer cells, glucose deprivation for 24 h does not reduce ATP levels, whereas it does suppress lactate production. In this study, metabolic pathways were blocked to identify the main pathway of ATP production in pancreatic ductal adenocarcinoma (PDAC). Blocking fatty acid oxidation (FAO) decreased ATP production by 40% in cancer cells with no effect on normal cells. The effects of calorie balanced high- or low-fat diets were tested to determine whether cancer growth is modulated by fatty acids instead of calories. A low-fat diet caused a 70% decrease in pancreatic preneoplastic lesions compared with the control, whereas a high-fat diet caused a two-fold increase in preneoplastic lesions accompanied with increase of ATP production in the Kras (G12D)/Pdx1-cre PDAC model. The present results suggest that ATP production in cancer cells is dependent on FAO rather than on glycolysis, which can be a therapeutic approach by targeting cancer energy metabolism.

Background: A standard treatment for unresectable advanced-stage intrahepatic cholangiocarcinoma (IHCC) has not yet been established. Although neoadjuvant concurrent chemoradiotherapy (CCRT) and liver transplantation are associated with long-term survival in select patients, the outcomes of CCRT for advanced-stage unresectable IHCC remain unclear. The aim of our study was to evaluate the outcomes of CCRT in patients with unresectable advanced-stage IHCC. Methods: We retrospectively reviewed the records of all patients with unresectable advanced stage (stage IVa or IVb) IHCC who were pathologically diagnosed and treated at National Cancer Center, Korea, from June 2001 to March 2012. Of the total of 92 patients, 25 (27.1%) received capecitabine plus cisplatin (XP) chemotherapy with external radiotherapy (RT) (XP-CCRT group) and 67 (72.8%) received XP chemotherapy alone (XP group). The clinical characteristics and outcomes of the 2 groups were compared. Results: The 92 patients comprised 72 male and 20 female patients, with a median age of 58 years (range 26–78 years). The baseline clinical characteristics of the 2 groups were similar. Patients in the XP-CCRT group received a mean 44.7 Gy of RT and a mean 5.6 cycles of XP chemotherapy, whereas patients in the XP group received a mean 4.0 cycles. The disease control rate was higher in the XP-CCRT group than in the XP group, but the difference was not statistically significant (56.0% vs. 41.5%, p = 0.217). Although neutropenia was significantly more frequent in the XP-CCRT than in the XP group (48% vs. 9%, p < 0.001), the rates of other toxicities and > grade 3 toxicities did not differ. At a median follow-up of 5.3 months, PFS (4.3 vs. 1.9 months, p = 0.001) and OS (9.3 vs. 6.2 months, p = 0.048) were significantly longer in the XP-CCRT than in the XP group. Conclusions: XP-CCRT was well tolerated and was associated with longer PFS and OS than XP chemotherapy alone in patients with unresectable advanced IHCC. Controlled randomized trials are required to determine whether XP-CCRT is a primary treatment option for patients with unresectable advanced IHCC.

The association between oral microbiota and cancer development has been a topic of intense research in recent years, with compelling evidence suggesting that the oral microbiome may play a significant role in cancer initiation and progression. However, the causal connections between the two remain a subject of debate, and the underlying mechanisms are not fully understood. In this case–control study, we aimed to identify common oral microbiota associated with several cancer types and investigate the potential mechanisms that may trigger immune responses and initiate cancer upon cytokine secretion. Saliva and blood samples were collected from 309 adult cancer patients and 745 healthy controls to analyze the oral microbiome and the mechanisms involved in cancer initiation. Machine learning techniques revealed that six bacterial genera were associated with cancer. The abundance of Leuconostoc, Streptococcus, Abiotrophia, and Prevotella was reduced in the cancer group, while abundance of Haemophilus and Neisseria enhanced. G protein-coupled receptor kinase, H+-transporting ATPase, and futalosine hydrolase were found significantly enriched in the cancer group. Total short-chain fatty acid (SCFAs) concentrations and free fatty acid receptor 2 (FFAR2) expression levels were greater in the control group when compared with the cancer group, while serum tumor necrosis factor alpha induced protein 8 (TNFAIP8), interleukin-6 (IL6), and signal transducer and activator of transcription 3 (STAT3) levels were higher in the cancer group when compared with the control group. These results suggested that the alterations in the composition of oral microbiota can contribute to a reduction in SCFAs and FFAR2 expression that may initiate an inflammatory response through the upregulation of TNFAIP8 and the IL-6/STAT3 pathway, which could ultimately increase the risk of cancer onset.

The aim of this study was to develop a novel deep learning (DL) model without requiring large-annotated training datasets for detecting pancreatic cancer (PC) using computed tomography (CT) images. This retrospective diagnostic study was conducted using CT images collected from 2004 and 2019 from 4287 patients diagnosed with PC. We proposed a self-supervised learning algorithm (pseudo-lesion segmentation (PS)) for PC classification, which was trained with and without PS and validated on randomly divided training and validation sets. We further performed cross-racial external validation using open-access CT images from 361 patients. For internal validation, the accuracy and sensitivity for PC classification were 94.3% (92.8–95.4%) and 92.5% (90.0–94.4%), and 95.7% (94.5–96.7%) and 99.3 (98.4–99.7%) for the convolutional neural network (CNN) and transformer-based DL models (both with PS), respectively. Implementing PS on a small-sized training dataset (randomly sampled 10%) increased accuracy by 20.5% and sensitivity by 37.0%. For external validation, the accuracy and sensitivity were 82.5% (78.3–86.1%) and 81.7% (77.3–85.4%) and 87.8% (84.0–90.8%) and 86.5% (82.3–89.8%) for the CNN and transformer-based DL models (both with PS), respectively. PS self-supervised learning can increase DL-based PC classification performance, reliability, and robustness of the model for unseen, and even small, datasets. The proposed DL model is potentially useful for PC diagnosis.