Up-regulated FNDC1 accelerates stemness and chemoradiation resistance in colorectal cancer cells

Investigation of the pan-cancer property of FNDC1 and its molecular mechanism to promote lung adenocarcinoma metastasis

BACKGROUND

Fibronectin type III domain containing 1 (FNDC1) has been associated with the metastasis of many tumors, but its function in lung cancer remains uncertain.

METHODS

FNDC1 expression was analyzed in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx), evaluate its prognostic value. Gene Set Enrichment Analysis (GSEA) enrichment analysis of differential expression of FNDC1 in lung cancer. The expression of FNDC1 was detected in five types of lung cancer cells, and screened to establish FNDC1 stable knockdown cell strains. To observe the migration and invasion ability of lung cancer cells after FNDC1 knockdown. Finally, we used rhIL-6 to interfere with the stable knockdown of FNDC1 in A549 cells and observed the recovery of migration and invasion.

RESULT

Our results showed that FNDC1 expression was increased in 21 tumor tissues, including lung cancer, and was associated with poor prognosis in five cancers, including lung adenocarcinoma (LUAD) (P < 0.05). GSEA enrichment analysis showed that FNDC1 was related to the pathways involved the JAK-STAT signaling pathway. Stable knockdown of FNDC1 in A549 and H292 cells resulted in decreased migration and invasion ability of both cells, accompanied by decreased expression of MMP-2 and Snail, and a significant decline in the expression of p-JAK2 and p-STAT3. The suppressive effect of FNDC1 knockdown on lung cancer cell metastasis counteracted by the JAK-STAT agonist rhIL-6 were presented in the nude mouse metastatic tumor model.

CONCLUSION

FNDC1 is implicated in poor prognosis of a diverse range of malignant tumors, which can promote metastasis and invasion of lung cancer through the JAK2-STAT3 signaling pathway.

High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data

Introduction: FOLFOX and FOLFIRI chemotherapy are considered standard first-line treatment options for colorectal cancer (CRC). However, the criteria for selecting the appropriate treatments have not been thoroughly analyzed. Methods: A newly developed machine learning model was applied on several gene expression data from the public repository GEO database to identify molecular signatures predictive of efficacy of 5-FU based combination chemotherapy (FOLFOX and FOLFIRI) in patients with CRC. The model was trained using 5-fold cross validation and multiple feature selection methods including LASSO and VarSelRF methods. Random Forest and support vector machine classifiers were applied to evaluate the performance of the models. Results and Discussion: For the CRC GEO dataset samples from patients who received either FOLFOX or FOLFIRI, validation and test sets were >90% correctly classified (accuracy), with specificity and sensitivity ranging between 85%-95%. In the datasets used from the GEO database, 28.6% of patients who failed the treatment therapy they received are predicted to benefit from the alternative treatment. Analysis of the gene signature suggests the mechanistic difference between colorectal cancers that respond and those that do not respond to FOLFOX and FOLFIRI. Application of this machine learning approach could lead to improvements in treatment outcomes for patients with CRC and other cancers after additional appropriate clinical validation.

A contrast set mining based approach for cancer subtype analysis

The task of detecting common and unique characteristics among different cancer subtypes is an important focus of research that aims to improve personalized therapies. Unlike current approaches mainly based on predictive techniques, our study aims to improve the knowledge about the molecular mechanisms that descriptively led to cancer, thus not requiring previous knowledge to be validated. Here, we propose an approach based on contrast set mining to capture high-order relationships in cancer transcriptomic data. In this way, we were able to extract valuable insights from several cancer subtypes in the form of highly specific genetic relationships related to functional pathways affected by the disease. To this end, we have divided several cancer gene expression databases by the subtype associated with each sample to detect which gene groups are related to each cancer subtype. To demonstrate the potential and usefulness of the proposed approach we have extensively analysed RNA-Seq gene expression data from breast, kidney, and colon cancer subtypes. The possible role of the obtained genetic relationships was further evaluated through extensive literature research, while its prognosis was assessed via survival analysis, finding gene expression patterns related to survival in various cancer subtypes. Some gene associations were described in the literature as potential cancer biomarkers while other results have been not described yet and could be a starting point for future research.

Mechanisms of radioresistance and the underlying signaling pathways in colorectal cancer cells

Radiotherapy is one of the most common modalities for the treatment of a wide range of tumors, including colorectal cancer (CRC); however, radioresistance of cancer cells remains a major limitation for this treatment. Following radiotherapy, the activities of various cellular mechanisms and cell signaling pathways are altered, resulting in the development of radioresistance, which leads to therapeutic failure and poor prognosis in patients with cancer. Furthermore, even though several inhibitors have been developed to target tumor resistance, these molecules can induce side effects in nontumor cells due to low specificity and efficiency. However, the role of these mechanisms in CRC has not been extensively studied. This review discusses recent studies regarding the relationship between radioresistance and the alterations in a series of cellular mechanisms and cell signaling pathways that lead to therapeutic failure and tumor recurrence. Our review also presents recent advances in the in vitro/in vivo study models aimed at investigating the radioresistance mechanism in CRC. Furthermore, it provides a relevant biochemical basis in theory, which can be useful to improve radiotherapy sensitivity and prolong patient survival.

Hypoxia-related tumor environment correlated with immune infiltration and therapeutic sensitivity in diffuse large B-cell lymphoma

Background: Due to the high heterogeneity of diffuse large B-cell lymphoma (DLBCL), traditional chemotherapy treatment ultimately failed in one-third of the patients. Big challenges existed in finding how to accurately predict prognosis and provide individualized treatment. Hypoxia, although being a key factor in the development and progression of DLBCL, plays its role in DLBCL prognosis, which has yet to be fully explored.

Methods: Data used in the current study were sourced from the Gene Expression Omnibus (GEO) database. DLBCL patients were divided according to different hypoxia-related subtypes based on the expressions of hypoxia-related genes (HRGs) relevant to survival. Differentially expressed genes (DEGs) between subtypes were identified using the limma package. Using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses, the prognostic signature was established to calculate risk scores. The tumor microenvironment (TME) in low- and high-risk groups was evaluated by single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE. The chemotherapeutic sensitivity in two groups was assessed by IC50 values.

Results: DLBCL patients were clustered into two hypoxia-related subtype groups according to different gene survival and expressions associated with increasing oxygen delivery and reducing oxygen consumption, and these two subtype groups were compared. Based on the differential expression, a risk model was established using univariate cox and LASSO regression analyses, FNDC1, ANTXR1, RARRES2, S100A9, and MT1M. The performance of the risk signature in predicting the prognosis of DLBCL patients was validated in the internal and external datasets, as evidenced by receiver operating characteristic (ROC) curves. In addition, we observed significant differences in the tumor microenvironment and chemotherapeutic response between low- and high-risk groups.

Conclusion: Our study developed novel hypoxia-related subtypes in DLBCL and identified five prognostic signatures for DLBCL patients. These findings may enrich our understanding of the role of hypoxia in DLBCL and help improve the treatment of DLBCL patients.