Background: Lactate accumulation leads to an acidic tumor microenvironment (TME), in turn promoting colorectal cancer (CRC) progression. Tumor-associated macrophages (TAMs) are the predominant cells in TME. This study aimed to reveal the regulation mechanism of CRC cell-derived lactate on TAMs and explore the mechanism underlying lactate accumulation-induced aggravation in CRC. Methods: Cell growth and metastasis were evaluated by colony formation, Transwell, and wound healing assays. Western blot and RT-qPCR were applied to determine the protein and mRNA expression. Flow cytometry was used to analyze the polarization state and apoptotic rate of macrophages induced in THP-1 cells. The lactate in the cell supernatant was quantified using an ELISA kit. Immunofluorescence was performed to visualize the location of High Mobility Group Box 1 (HMGB1). H&E and Ki67 staining assays were used to assess tumorigenesis in nude mice bearing ectopic tumors. Results: Cell growth and metastasis were promoted in the hypoxic CRC cells. The hypoxic cell supernatant stimulated the M2-type polarization of macrophages. The lactate level increased in hypoxic cancer cells. However, the inhibition of lactate using 3-hydroxy-butyrate (3-OBA) reversed the effects of hypoxia. Also, macrophages showed no promoting effect on cancer cell growth and migration in the presence of 3-OBA. HMGB1 was secreted into the extracellular space of lactate-induced macrophages, further enhancing the malignant behaviors of cancer cells. ERK, EMT, and Wnt signaling pathways were activated in cancer cells due to HMGB1 upregulation. Conclusions: The lactate metabolized by cancer cells stimulated M2 polarization and HMGB1 secretion by macrophages, aggravating the carcinogenic behaviors of cancer cells.

Purpose: We propose three support vector machine (SVM) classifiers, using pre-and post-contrast T2 fluid-attenuated inversion recovery (FLAIR) subtraction and/or pre-and post-contrast T1WI subtraction, to differentiate treatment-related effects (TRE) from glioma recurrence. Materials and Methods: Fifty-six postoperative high-grade glioma patients with suspicious progression after radiotherapy and chemotherapy from two centers were studied. Pre-and post-contrast T1WI and T2 FLAIR were collected. Each pre-contrast image was voxel-wise subtracted from the co-registered post-contrast image. Dataset was randomly split into training, and testing on a 7:3 ratio, accordingly subjected to a five fold cross validation. Best feature subsets were selected by Pearson correlation coefficient and recursive feature elimination, whereupon a radiomics classifier was built with SVM. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results: In all, 186 features were extracted on each subtraction map. Top nine T1WI subtraction features, top thirteen T2 FLAIR subtraction features and top thirteen combination features were selected to build optimal SVM classifiers accordingly. The accuracies/AUCs/sensitivity/specificity/PPV/NPV of SVM based on sole T1WI subtraction were 80.00%/80.00% (CI: 0.5370– 1.0000)/100%/70.00%/62.50%/100%. Those results of SVM based on sole T2 FLAIR subtraction were 86.67%/84.00% (CI: 0.5962– 1.0000)/100%/80%/71.43%/100%. Those results of SVM based on both T1WI subtraction and T2 FLAIR subtraction were 93.33%/94.00% (CI: 0.7778– 1.0000)/100%/90%/83.33%/100%, respectively. Conclusion: Pre- and post-contrast T2 FLAIR subtraction provided added value for diagnosis between recurrence and TRE. SVM based on a combination of T1WI and T2 FLAIR subtraction maps was superior to the sole use of T1WI or T2 FLAIR for differentiating TRE from recurrence. The SVM classifier based on combination of pre-and post-contrast subtraction T2 FLAIR and T1WI imaging allowed for the accurate differential diagnosis of TRE from recurrence, which is of paramount importance for treatment management of postoperative glioma patients after radiation therapy.

Purpose: To develop a radiogenomics classifier to assess anaplastic lymphoma kinase (ALK) gene rearrangement status in pretreated solid lung adenocarcinoma noninvasively. Materials and Methods: This study consisted of 140 consecutive pretreated solid lung adenocarcinoma patients with complete enhanced CT scans who were tested for both EGFR mutations and ALK status. Pre-contrast CT and standard post-contrast CT radiogenomics machine learning classifiers were designed as two separate classifiers. In each classifier, dataset was randomly split into training and independent testing group on a 7:3 ratio, accordingly subjected to a 5-fold cross-validation. After normalization, best feature subsets were selected by Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) or recursive feature elimination (RFE), whereupon a radiomics classifier was built with support vector machine (SVM). The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: In classifier one, 98 cases were selected as training data set, 42 cases as independent testing data set. In classifier two, 87 cases were selected as training data set, 37 cases as independent testing data set. Both classifiers extracted 851 radiomics features. The top 25 pre-contrast features and top 19 post-contrast features were selected to build optimal ALK+ radiogenomics classifiers accordingly. The accuracies, AUCs, sensitivity, specificity, PPV, and NPV of pre-contrast CT classifier were 78.57%, 80.10% (CI: 0.6538– 0.9222), 71.43%, 82.14%, 66.67%, and 85.19%, respectively. Those results of standard post-contrast CT classifier were 81.08%, 82.85% (CI: 0.6630– 0.9567), 76.92%, 83.33%, 71.43%, and 86.96%. Conclusion: Solid lung adenocarcinoma ALK+ radiogenomics classifier of standard post-contrast CT radiomics biomarkers produced superior performance compared with that of pre-contrast one, suggesting that post-contrast CT radiomics should be recommended in the context of solid lung adenocarcinoma radiogenomics AI. Standard post-contrast CT machine learning radiogenomics classifier could help precisely identify solid adenocarcinoma ALK rearrangement status, which may act as a pragmatic and cost-efficient substitute for traditional invasive ALK status test.

Introduction: Angiogenesis is an essential feature of liver cancer. Tumor hypoxia results from abnormal vessel architecture. Numerous studies have sufficiently demonstrated that Tanshinone IIA (Tan IIA) can increase blood flow and enhance microcirculation. The objectives of this study are to: 1 assess the impact of Tan IIA on tumor angiogenesis and architecture, 2 determine the impact of Tan IIA on tumor hypoxia and susceptibility to Sorafenib, and 3 clarify the relevant mechanisms.Methods: CCK8 and flow cytometry measured cell proliferation and apoptosis, respectively. Tube creation assay was used to investigate medication effects on angiogenesis and structure. Drug effects on tumor development, metastasis, and hypoxic tumor microenvironment are assessed in an orthotopic xenograft model of liver tumors. Protein expression was measured by Western blotting and immunohistochemistry.Results: Our results demonstrated that Tan IIA could not reduce tumor proliferation or enhance Sorafenib’s anti-tumor effect in vitro. Nevertheless, it can prevent Sorafenib from demolishing the typical vascular structure and aid sorafenib in blocking the recruitment of vascular endothelial cells by liver cancer cells. Although Tan IIA cannot inhibit tumor growth in vivo, it can significantly boost Sorafenib’s inhibitory effect on liver cancer, alleviate tumor microenvironment hypoxia, and minimize lung metastasis. This effect may be achieved by reducing HIF-1α and HIF-2α expression via the PI3K-AKT signal pathway.Discussion: Our results reveal the mechanism of Tan IIA in normalizing tumor blood vessels, provide innovative concepts and approaches to overcome chemotherapy resistance, and provide a theoretical basis for the clinical transformation and usage of Tan IIA.

Fibroblast growth factor receptors (FGFRs) play critical roles in the regulation of cell growth, differentiation, and proliferation. Specifically, FGFR2 gene amplification has been implicated in gastric and breast cancer. Pan-FGFR inhibitors often cause large toxic side effects, and the highly conserved ATP-binding pocket in the FGFR1/2/3 isoforms poses an immense challenge in designing selective FGFR2 inhibitors. Recently, an indazole-based inhibitor has been discovered that can selectively target FGFR2. However, the detailed mechanism involved in selective inhibition remains to be clarified. To this end, we performed extensive molecular dynamics simulations of the apo and inhibitor-bound systems along with multiple analyses, including Markov state models, principal component analysis, a cross-correlation matrix, binding free energy calculation, and community network analysis. Our results indicated that inhibitor binding induced the phosphate-binding loop (P-loop) of FGFR2 to switch from the open to the closed conformation. This effect enhanced extensive hydrophobic FGFR2-inhibitor contacts, contributing to inhibitor selectivity. Moreover, the key conformational intermediate states, dynamics, and driving forces of this transformation were uncovered. Overall, these findings not only provided a structural basis for understanding the closed P-loop conformation for therapeutic potential but also shed light on the design of selective inhibitors for treating specific types of cancer.

Background: Phosphoinositide 3-kinases (PI3Ks) are lipid enzymes that regulate a wide range of intracellular functions. In contrast to Class I and Class III PI3K, which have more detailed descriptions, Class II PI3K has only recently become the focus of functional research. PIK3C2A is a classical member of the PI3Ks class II. However, the role of PIK3C2A in cancer prognosis and progression remains unknown.Methods: The expression pattern and prognostic significance of PIK3C2A in human malignancies were investigated using multiple datasets and scRNA-seq data. The PIK3C2A expression in renal clear cell carcinoma (KIRC) was then validated utilizing Western blot. The functional role of PIK3C2A in KIRC was assessed using combined function loss experiments with in vitro experiments. Furthermore, the correlation of PIK3C2A expression with tumor immunity was investigated in KIRC. The TCGA database was employed to investigate PIK3C2A functional networks.Results: Significant decrease in PIK3C2A expression in KIRC, demonstrated that it potentially influences the prognosis of diverse tumors, particularly KIRC. In addition, PIK3C2A was significantly correlated with the T stage, M stage, pathologic stage, and histologic grade of KIRC. Nomogram models were constructed and used to predict patient survival based on the results of multivariate Cox regression analysis. PIK3C2A knockdown resulted in significantly increased KIRC cell proliferation. Of note, PIK3C2A expression demonstrated a significant correlation with the infiltrating levels of primary immune cells in KIRC.Conclusion: These findings support the hypothesis that PIK3C2A is a novel biomarker for tumor progression and indicates dynamic shifts in immune infiltration in KIRC. Furthermore, aberrant PIK3C2A expression can influence the biological activity of cancer cells.