Various blood cell ratios exist which seem to have an impact on prognosis for resected gastric cancer patients. The aim of this systematic review was to investigate the prognostic role of blood cell ratios in patients with gastric cancer undergoing surgery in a curative attempt. A systematic literature search in MEDLINE (via PubMed), CENTRAL, and Web of Science was performed. Information on survival and cut-off values from all studies investigating any blood cell ratio in resected gastric cancer patients were extracted. Prognostic significance and optimal cut-off values were calculated by meta-analyses and a summary of the receiver operating characteristic. From 2831 articles, 65 studies investigated six different blood cell ratios (prognostic nutritional index (PNI), lymphocyte to monocyte ratio (LMR), systemic immune-inflammation index (SII), monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), and platelet to lymphocyte ratio (PLR)). There was a significant association for the PNI and NLR with overall survival and disease-free survival and for LMR and NLR with 5-year survival. The used cut-off values had high heterogeneity. The available literature is flawed by the use of different cut-off values hampering evidence-based patient treatment and counselling. This article provides optimal cut-off values recommendations for future research.

Background: Granulin is a secreted, glycosylated peptide—originated by cleavage from a precursor protein—which is involved in cell growth, tumor invasion and angiogenesis. However, the specific prognostic impact of granulin in human colorectal cancer has only been studied to a limited extent. Thus, we wanted to assess the expression of granulin in colorectal cancer patients to evaluate its potential as a prognostic biomarker. Methods: Expressional differences of granulin in colorectal carcinoma tissue (n = 94) and corresponding healthy colon mucosa were assessed using qRT-PCR. Immunohistochemistry was performed in colorectal cancer specimens (n = 97), corresponding healthy mucosa (n = 47) and colorectal adenomas (n = 19). Subsequently, the results were correlated with histopathological and clinical patients’ data. HCT-116 cells were transfected with siRNA for invasion and migration assays. Results: Immunohistochemistry and qRT-PCR revealed tumoral over expression of granulin in colorectal cancer specimens compared to corresponding healthy colon mucosa and adenomas. Tumoral overexpression of granulin was associated with a significantly impaired overall survival. Moreover, downregulation of granulin by siRNA significantly diminished the invasive capacities of HCT-116 cells in vitro. Conclusion: Expression of granulin differs in colorectal cancer tissue, adenomas and healthy colon mucosa. Furthermore, granulin features invasive and migrative capabilities and overexpression of granulin correlates with a dismal prognosis. This reveals its potential as a prognostic biomarker and granulin could be a worthwhile molecular target for individualized anticancer therapy.

AIM: In this study, we use Artificial Intelligence (AI), including Machine (ML) and Deep Learning (DL), to predict the long-term survival of resectable esophageal cancer (EC) patients in a high-volume surgical center. Our objective is to evaluate the predictive efficacy of AI methods for survival prognosis across different time points of oncological treatment. This involves comparing models trained with clinical data, integrating either Tumor, Node, Metastasis (TNM) classification or tumor biomarker analysis, for long-term survival predictions. METHODS: In this retrospective study, 1002 patients diagnosed with EC between 1996 and 2021 were analyzed. The original dataset comprised 55 pre- and postoperative patient characteristics and 55 immunohistochemically evaluated biomarkers following surgical intervention. To predict the five-year survival status, four AI methods (Random Forest RF, XG Boost XG, Artificial Neural Network ANN, TabNet TN) and Logistic Regression (LR) were employed. The models were trained using three predefined subsets of the training dataset as follows: (I) the baseline dataset (BL) consisting of pre-, intra-, and postoperative data, including the TNM but excluding tumor biomarkers, (II) clinical data accessible at the time of the initial diagnostic workup (primary staging dataset, PS), and (III) the PS dataset including tumor biomarkers from tissue microarrays (PS + biomarkers), excluding TNM status. We used permutation feature importance for feature selection to identify only important variables for AI-driven reduced datasets and subsequent model retraining. RESULTS: Model training on the BL dataset demonstrated similar predictive performances for all models (Accuracy, ACC: 0.73/0.74/0.76/0.75/0.73; AUC: 0.78/0.82/0.83/0.80/0.79 RF/XG/ANN/TN/LR, respectively). The predictive performance and generalizability declined when the models were trained with the PS dataset. Surprisingly, the inclusion of biomarkers in the PS dataset for model training led to improved predictions (PS dataset vs. PS dataset + biomarkers; ACC: 0.70 vs. 0.77/0.73 vs. 0.79/0.71 vs. 0.75/0.69 vs. 0.72/0.63 vs. 0.66; AUC: 0.77 vs. 0.83/0.80 vs. 0.85/0.76 vs. 0.86/0.70 vs. 0.76/0.70 vs. 0.69 RF/XG/ANN/TN/LR, respectively). The AI models outperformed LR when trained with the PS datasets. The important features shared after AI-driven feature selection in all models trained with the BL dataset included histopathological lymph node status (pN), histopathological tumor size (pT), clinical tumor size (cT), age at the time of surgery, and postoperative tracheostomy. Following training with the PS dataset with biomarkers, the important predictive features included patient age at the time of surgery, TP-53 gene mutation, Mesothelin expression, thymidine phosphorylase (TYMP) expression, NANOG homebox protein expression, and indoleamine 2,3-dioxygenase (IDO) expressed on tumor-infiltrating lymphocytes, as well as tumor-infiltrating Mast- and Natural killer cells. CONCLUSION: Different AI methods similarly predict the long-term survival status of patients with EC and outperform LR, the state-of-the-art classification model. Survival status can be predicted with similar predictive performance with patient data at an early stage of treatment when utilizing additional biomarker analysis. This suggests that individual survival predictions can be made early in cancer treatment by utilizing biomarkers, reducing the necessity for the pathological TNM status post-surgery. This study identifies important features for survival predictions that vary depending on the timing of oncological treatment.

Background: For many solid types of cancer including colorectal cancer treatment with the VEGF antibody bevacizumab as anti-angiogenic treatment has become standard of care. Nevertheless, long-term treatment with anti-angiogenic drugs in combination with other treatment modalities or alone can induce resistance through alternative pro-angiogenic pathways. In this study, we investigated the effects of nintedanib, an anti- VEGFR/PDGFR/FGFR kinase inhibitor, on tumor vasculature to determine the potential benefits of combined inhibition of multiple pro-angiogenic factors. Methods: In a colorectal xenograft model, subcutaneous tumors were treated with Nintedanib. Tumor growth patterns were measured and tumors were analysed histologically regarding effects on tumor angiogenesis and parameters of vascular normalization. Results: Inhibition of VEGFR/PDGFR/FGFR by Nintedanib was able to reduce tumor growth by significantly inhibiting angiogenesis and inducing tumor cell death. The remaining vessels showed decreased vascular leakage and improved oxygen delivery, indicating a functionally and structurally improved vascular bed resulting from vascular normalization. Conclusion: In xenograft mouse model of colorectal cancer Nintedanib revealed anti-tumoral effects and induced vascular normalization. Our findings indicate that the treatment with Nintedanib could be able to improve intratumoral oxygen and thereby drug delivery to potentially enhance the efficacy of preexisting oncological therapies such as chemotherapy and radiation.