Having a hypoxic microenvironment is a common and salient feature of most solid tumors. Hypoxia has a profound effect on the biological behavior and malignant phenotype of cancer cells, mediates the effects of cancer chemotherapy, radiotherapy, and immunotherapy through complex mechanisms, and is closely associated with poor prognosis in various cancer patients. Accumulating studies have demonstrated that through normalization of the tumor vasculature, nanoparticle carriers and biocarriers can effectively increase the oxygen concentration in the tumor microenvironment, improve drug delivery and the efficacy of radiotherapy. They also increase infiltration of innate and adaptive anti-tumor immune cells to enhance the efficacy of immunotherapy. Furthermore, drugs targeting key genes associated with hypoxia, including hypoxia tracers, hypoxia-activated prodrugs, and drugs targeting hypoxia-inducible factors and downstream targets, can be used for visualization and quantitative analysis of tumor hypoxia and antitumor activity. However, the relationship between hypoxia and cancer is an area of research that requires further exploration. Here, we investigated the potential factors in the development of hypoxia in cancer, changes in signaling pathways that occur in cancer cells to adapt to hypoxic environments, the mechanisms of hypoxia-induced cancer immune tolerance, chemotherapeutic tolerance, and enhanced radiation tolerance, as well as the insights and applications of hypoxia in cancer therapy.

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Objective Pancreatic body tail carcinoma (PBTC) is a relatively few pancreatic cancer in clinical practice, and its specific clinicopathological features and prognosis have not been fully described. In this study, we aimed to create a nomogram to predict the overall survival (OS) of patients with advanced PBTC. Methods We extracted clinical and related prognostic data of advanced PBTC patients from 2000 to 2018 from the Surveillance, Epidemiology, and End Results database. Independent prognostic factors were selected using univariate and multivariate Cox analyses, and a nomogram was constructed using R software. The C-index, area under the curve (AUC) of receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA) were used to assess the clinical utility of the nomogram. Finally, OS was assessed using the Kaplan–Meier method. Results A total of 1256 patients with advanced PBTC were eventually included in this study. Age, grade, N stage, M stage, surgery, and chemotherapy were identified as independent risk factors using univariate and multivariate Cox regression analyses (p < 0.05). In the training cohort, the calibration index of the nomogram was 0.709, while the AUC values of the nomogram, age, grade, N stage, M stage, surgery, and chemotherapy were 0.777, 0.562, 0.621, 0.5, 0.576, 0.632, and 0.323, respectively. Meanwhile, in the validation cohort, the AUC values of the nomogram, age, grade, N stage, M stage, surgery, and chemotherapy were 0.772, 0.551, 0.629, 0.534, 0.577, 0.606, and 0.639, respectively. Good agreement of the model in the training and validation cohorts was demonstrated in the calibration and DCA curves. Univariate survival analysis showed a statistically significant effect of age, grade, M stage, and surgery on prognosis (p < 0.05). Conclusion Age, grade, M stage, and surgery were independently associated with OS, and the established nomogram was a visual tool to effectively predict OS in advanced PBTC patients.

Early and accurate diagnosis and treatment of pancreatic cancer (PC) remain challenging endeavors globally. Late diagnosis lag, high invasiveness, chemical resistance, and poor prognosis are unresolved issues of PC. The concept of metabolic reprogramming is a hallmark of cancer cells. Increasing evidence shows that PC cells alter metabolic processes such as glucose, amino acids, and lipids metabolism and require continuous nutrition for survival, proliferation, and invasion. Glucose metabolism, in particular, regulates the tumour microenvironment (TME). Furthermore, the link between glucose metabolism and TME also plays an important role in the targeted therapy, chemoresistance, radiotherapy ineffectiveness, and immunosuppression of PC. Altered metabolism with the TME has emerged as a key mechanism regulating PC progression. This review shed light on the relationship between TME, glucose metabolism, and various aspects of PC. The findings of this study provide a new direction in the development of PC therapy targeting the metabolism of cancer cells.

The liver is the most prevalent location of distant metastasis for pancreatic cancer (PC), which is highly aggressive. Pancreatic cancer with liver metastases (PCLM) patients have a poor prognosis. Furthermore, there is a lack of effective predictive tools for anticipating the diagnostic and prognostic techniques that are needed for the PCLM patients in current clinical work. Therefore, we aimed to construct two nomogram predictive models incorporating common clinical indicators to anticipate the risk factors and prognosis for PCLM patients. Clinicopathological information on pancreatic cancer that referred to patients who had been diagnosed between the years of 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses and a Cox regression analysis were utilized to recognize the independent risk variables and independent predictive factors for the PCLM patients, respectively. Using the independent risk as well as prognostic factors derived from the multivariate regression analysis, we constructed two novel nomogram models for predicting the risk and prognosis of PCLM patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, the consistency index (C-index), and the calibration curve were then utilized to establish the accuracy of the nomograms’ predictions and their discriminability between groups. Using a decision curve analysis (DCA), the clinical values of the two predictors were examined. Finally, we utilized Kaplan–Meier curves to examine the effects of different factors on the prognostic overall survival (OS). As many as 1898 PCLM patients were screened. The patient’s sex, primary site, histopathological type, grade, T stage, N stage, bone metastases, lung metastases, tumor size, surgical resection, radiotherapy, and chemotherapy were all found to be independent risks variables for PCLM in a multivariate logistic regression analysis. Using a multivariate Cox regression analysis, we discovered that age, histopathological type, grade, bone metastasis, lung metastasis, tumor size, and surgery were all independent prognostic variables for PCLM. According to these factors, two nomogram models were developed to anticipate the prognostic OS as well as the risk variables for the progression of PCLM in PCLM patients, and a web-based version of the prediction model was constructed. The diagnostic nomogram model had a C-index of 0.884 (95% CI: 0.876–0.892); the prognostic model had a C-index of 0.686 (95% CI: 0.648–0.722) in the training cohort and a C-index of 0.705 (95% CI: 0.647–0.758) in the validation cohort. Subsequent AUC, calibration curve, and DCA analyses revealed that the risk and predictive model of PCLM had high accuracy as well as efficacy for clinical application. The nomograms constructed can effectively predict risk and prognosis factors in PCLM patients, which facilitates personalized clinical decision-making for patients.