Discoidin Domain Receptor 1 Expression in Colon Cancer: Roles and Prognosis Impact

EDST: a decision stump based ensemble algorithm for synergistic drug combination prediction

INTRODUCTION

There are countless possibilities for drug combinations, which makes it expensive and time-consuming to rely solely on clinical trials to determine the effects of each possible drug combination. In order to screen out the most effective drug combinations more quickly, scholars began to apply machine learning to drug combination prediction. However, most of them are of low interpretability. Consequently, even though they can sometimes produce high prediction accuracy, experts in the medical and biological fields can still not fully rely on their judgments because of the lack of knowledge about the decision-making process.

RELATED WORK

Decision trees and their ensemble algorithms are considered to be suitable methods for pharmaceutical applications due to their excellent performance and good interpretability. We review existing decision trees or decision tree ensemble algorithms in the medical field and point out their shortcomings.

METHOD

This study proposes a decision stump (DS)-based solution to extract interpretable knowledge from data sets. In this method, a set of DSs is first generated to selectively form a decision tree (DST). Different from the traditional decision tree, our algorithm not only enables a partial exchange of information between base classifiers by introducing a stump exchange method but also uses a modified Gini index to evaluate stump performance so that the generation of each node is evaluated by a global view to maintain high generalization ability. Furthermore, these trees are combined to construct an ensemble of DST (EDST).

EXPERIMENT

The two-drug combination data sets are collected from two cell lines with three classes (additive, antagonistic and synergistic effects) to test our method. Experimental results show that both our DST and EDST perform better than other methods. Besides, the rules generated by our methods are more compact and more accurate than other rule-based algorithms. Finally, we also analyze the extracted knowledge by the model in the field of bioinformatics.

CONCLUSION

The novel decision tree ensemble model can effectively predict the effect of drug combination datasets and easily obtain the decision-making process.

Clinical significance of immunohistochemical expression of DDR1 and β-catenin in colorectal carcinoma

BACKGROUND

Despite recent advances in therapy modalities of colorectal cancer (CRC), it is still the third cause of cancer-related deaths worldwide. Thus, the search for new target therapies became mandatory. DDR1 is a collagen receptor that has a suggested role in cellular proliferation, tumor invasion, and metastasis.

MATERIAL AND METHODS

Forty-eight cases of CRC, 20 of CR adenoma, and 8 cases of non-tumoral colonic tissue were subjected to immunohistochemistry by DDR1 and β-catenin antibodies. Results were compared among the different studied groups and correlated with clinicopathologic data and available survival data. Also, the expression of both proteins was compared versus each other. Results were compared among the 3 studied groups and correlated with clinicopathologic and survival data.

RESULTS

It revealed a stepwise increase of DDR1 expression among studied groups toward carcinoma (P = 0.006). DDR1 expression showed a direct association with stage D in the modified Dukes' staging system (P = 0.013), higher-grade histologic types (P = 0.008), and lymph node invasion (P = 0.028) but inverse correlation with the presence of intratumoral inflammatory response (TIR) (P = 0.001). The shortest OS was associated with strong intensity of DDR1 (P = 0.012). The DDR1 and β-catenin expressions were significantly correlated (P = 0.028), and the combined expression of both was correlated with TNM staging (P = 0.017).

CONCLUSION

DDR1 overexpression is a frequent feature in CRC and CR adenoma. DDR1 is a poor prognostic factor and a suppressor of the TIR. DDR1 and β-catenin seem to have a synergistic action.

Deciphering the possible reciprocal loop between hepatic stellate cells and cancer cells in the tumor microenvironment of the liver

Activated hepatic stellate cells (HSCs)/myofibroblasts are the important sources of cancer-associated fibroblasts in the liver tumor microenvironment (TME). The crosstalk between activated HSCs and tumor cells mediates HCC progression, metastasis, tumor cell survival, angiogenesis and chemoresistance. In TME, HCC cells secrete various soluble factors responsible for the phenotypic activation of quiescent HSCs. Tumor cells use activated HSC-derived extracellular matrix (ECM) for migration and invasion. Further, in liver TME, activated HSCs and sinusoidal endothelial cells engage in a crosstalk that causes the secretion of angiogenesis and metastasis-related growth factors and cytokines. Activated HSCs and immune cells crosstalk to decrease immune surveillance in the liver TME by increasing the population of T regulatory cells and M2 macrophages or myeloid-derived suppressor cells. Thus, HSCs play a vital role in liver TME cell interactions. Therefore, a deep understanding of HSCs activation and their crosstalk with cancer and immune cells in TME may lead to the development of novel therapeutic strategies to target HCC.

New target DDR1: A "double-edged sword" in solid tumors

Globally, cancer is a major catastrophic disease that seriously threatens human health. Thus, there is an urgent need to find new strategies to treat cancer. Among them, identifying new targets is one of the best ways to treat cancer at present. Especially in recent years, scientists have discovered many new targets and made breakthroughs in the treatment of cancer, bringing new hope to cancer patients. As one of the novel targets for cancer treatment, DDR1 has attracted much attention due to its unique role in cancer. Hence, here, we focus on a new target, DDR1, which may be a "double-edged sword" of human solid tumors. In this review, we provide a comprehensive overview of how DDR1 acts as a "double-edged sword" in cancer. First, we briefly introduce the structure and normal physiological function of DDR1; Second, we delineate the DDR1 expression pattern in single cells; Next, we sorte out the relationship between DDR1 and cancer, including the abnormal expression of DDR1 in cancer, the mechanism of DDR1 and cancer occurrence, and the value of DDR1 on cancer prognosis. In addition, we introduced the current status of global drug and antibody research and development targeting DDR1 and its future design prospects; Finally, we summarize and look forward to designing more DDR1-targeting drugs in the future to make further progress in the treatment of solid tumors.

Clinical, Pathological, and Molecular Characteristics in Colorectal Cancer

Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and the second leading cause of death in patients with cancer [...].

The Insulin-like Growth Factor System and Colorectal Cancer

Insulin-like growth factors (IGFs) are peptides which exert mitogenic, endocrine and cytokine activities. Together with their receptors, binding proteins and associated molecules, they participate in numerous pathophysiological processes, including cancer development. Colorectal cancer (CRC) is a disease with high incidence and mortality rates worldwide, whose etiology usually represents a combination of the environmental and genetic factors. IGFs are most often increased in CRC, enabling excessive autocrine/paracrine stimulation of the cell growth. Overexpression or increased activation/accessibility of IGF receptors is a coinciding step which transmits IGF-related signals. A number of molecules and biochemical mechanisms exert modulatory effects shaping the final outcome of the IGF-stimulated processes, frequently leading to neoplastic transformation in the case of irreparable disbalance. The IGF system and related molecules and pathways which participate in the development of CRC are the focus of this review.