Five genes may predict metastasis in non-small cell lung cancer using bioinformatics analysis

Advances in the expression and function of Fyn in different human tumors

The tyrosine kinase Fyn is a member of the SRC family of kinases, and its sustained activation is closely linked to tumor cell migration, proliferation, and cell metabolism. Recently, Fyn has been found to be expressed in various tumor tissues, and the expression and function of Fyn vary between tumors, with Fyn acting as an oncogene to promote proliferation and metastasis in some tumors. This article summarizes the recent studies on the role of Fyn in different human tumors, focusing on the role of Fyn in melanoma, breast cancer, glioma, lung cancer, and peripheral T-cell lymphoma in order to provide a basis for future research and targeted therapy in different human tumors.

Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer

Differential expressions of certain genes during tumorigenesis may serve to identify novel manageable targets in the clinic. In this work with an integrated bioinformatics approach, we analyzed public microarray datasets from Gene Expression Omnibus (GEO) to explore the key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC). We identified a total of 984 common DEGs in 252 healthy and 254 NSCLC gene expression samples. The top 10 DEGs as a result of pathway enrichment and protein–protein interaction analysis were further investigated for their prognostic performances. Among these, we identified high expressions of CDC20, AURKA, CDK1, EZH2, and CDKN2A genes that were associated with significantly poorer overall survival in NSCLC patients. On the contrary, high mRNA expressions of CBL, FYN, LRKK2, and SOCS2 were associated with a significantly better prognosis. Furthermore, our drug target analysis for these hub genes suggests a potential use of Trichostatin A, Pracinostat, TGX-221, PHA-793887, AG-879, and IMD0354 antineoplastic agents to reverse the expression of these DEGs in NSCLC patients.

SPTAN1 Expression Predicts Treatment and Survival Outcomes in Colorectal Cancer

Simple Summary

Colorectal cancer (CRC) is a common and deadly form of cancer. Non-erythroid spectrin αII (SPTAN1), a protein of the cytoskeleton, is thought to be involved in CRC development and progression. In this study, we explore whether measuring SPTAN1 levels in resected CRC specimens might help to predict patient survival outcomes and response to chemotherapy. Indeed, we find that higher SPTAN1 protein and mRNA levels in CRC specimens associate with longer patient survival times. Using cell culture experiments, we then show that cells with lower SPTAN1 levels are less susceptible to FOLFOX chemotherapy, a standard treatment regimen for patients with CRC. Overall, our study underscores the importance of cytoskeletal proteins in shaping tumour biology and treatment responses and nominates SPTAN1 as a biomarker to improve patient stratification and refine therapeutic decisions in CRC.

Abstract

Colorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality. In a cohort of 189 patients with CRC, we recently showed that expression of the cytoskeletal scaffolding protein non-erythroid spectrin αII (SPTAN1) was lower in advanced metastatic tumours. The aim of the present study was to clarify the association of intratumoural SPTAN1 expression levels with treatment and survival outcomes in patients with CRC. The analysis was based on histologic assessment of SPTAN1 protein levels in our own CRC cohort, and transcriptome data of 573 CRC cases from The Cancer Genome Atlas (TCGA). We first establish that high intratumoural levels of SPTAN1 protein and mRNA associate with favourable survival outcomes in patients with CRC. Next, a response prediction signature applied to the TCGA data reveals a possible link between high SPTAN1 transcript levels and improved patient responses to FOLFOX chemotherapy. Complementary in vitro experiments confirm that SPTAN1 knockdown strains of the colon cancer cell lines HT-29, HCT116 mlh1-2 and Caco-2 are less responsive to FOLFOX chemotherapy compared with SPTAN1-proficient control strains. Taken together, we identify SPTAN1 as a novel prognostic biomarker in CRC and show that SPTAN1 expression levels may predict patient responses to chemotherapy. These investigations illustrate how an affordable, histology-based diagnostic test could directly impact therapeutic decision-making at the bedside.

A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD)

Background

Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients.

Methods

Raw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature.

Results

A prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041–39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779–3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis.

Conclusion

Our study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01975-z.

FOXO transcription factor family in cancer and metastasis

Forkhead box O (FOXO) transcription factors regulate diverse biological processes, affecting development, metabolism, stem cell maintenance and longevity. They have also been increasingly recognised as tumour suppressors through their ability to regulate genes essential for cell proliferation, cell death, senescence, angiogenesis, cell migration and metastasis. Mechanistically, FOXO proteins serve as key connection points to allow diverse proliferative, nutrient and stress signals to converge and integrate with distinct gene networks to control cell fate, metabolism and cancer development. In consequence, deregulation of FOXO expression and function can promote genetic disorders, metabolic diseases, deregulated ageing and cancer. Metastasis is the process by which cancer cells spread from the primary tumour often via the bloodstream or the lymphatic system and is the major cause of cancer death. The regulation and deregulation of FOXO transcription factors occur predominantly at the post-transcriptional and post-translational levels mediated by regulatory non-coding RNAs, their interactions with other protein partners and co-factors and a combination of post-translational modifications (PTMs), including phosphorylation, acetylation, methylation and ubiquitination. This review discusses the role and regulation of FOXO proteins in tumour initiation and progression, with a particular emphasis on cancer metastasis. An understanding of how signalling networks integrate with the FOXO transcription factors to modulate their developmental, metabolic and tumour-suppressive functions in normal tissues and in cancer will offer a new perspective on tumorigenesis and metastasis, and open up therapeutic opportunities for malignant diseases.

Cytoskeletal Remodeling in Cancer

Simple Summary

Cell migration is an essential process from embryogenesis to cell death. This is tightly regulated by numerous proteins that help in proper functioning of the cell. In diseases like cancer, this process is deregulated and helps in the dissemination of tumor cells from the primary site to secondary sites initiating the process of metastasis. For metastasis to be efficient, cytoskeletal components like actin, myosin, and intermediate filaments and their associated proteins should co-ordinate in an orderly fashion leading to the formation of many cellular protrusions-like lamellipodia and filopodia and invadopodia. Knowledge of this process is the key to control metastasis of cancer cells that leads to death in 90% of the patients. The focus of this review is giving an overall understanding of these process, concentrating on the changes in protein association and regulation and how the tumor cells use it to their advantage. Since the expression of cytoskeletal proteins can be directly related to the degree of malignancy, knowledge about these proteins will provide powerful tools to improve both cancer prognosis and treatment.

Abstract

Successful metastasis depends on cell invasion, migration, host immune escape, extravasation, and angiogenesis. The process of cell invasion and migration relies on the dynamic changes taking place in the cytoskeletal components; actin, tubulin and intermediate filaments. This is possible due to the plasticity of the cytoskeleton and coordinated action of all the three, is crucial for the process of metastasis from the primary site. Changes in cellular architecture by internal clues will affect the cell functions leading to the formation of different protrusions like lamellipodia, filopodia, and invadopodia that help in cell migration eventually leading to metastasis, which is life threatening than the formation of neoplasms. Understanding the signaling mechanisms involved, will give a better insight of the changes during metastasis, which will eventually help targeting proteins for treatment resulting in reduced mortality and longer survival.

Comprehensive Exome Analysis of Immunocompetent Metastatic Head and Neck Cancer Models Reveals Patient Relevant Landscapes

Immunocompetent metastatic head and neck cancer (HNC) models, although scarce, can help understanding cancer progression and therapy responses in vivo. Their comprehensive genome characterizations are essential for translational research. We first exome-sequenced the two most widely used spontaneous metastatic immunocompetent models, namely AT-84 and SCC VII, followed by comprehensive genomic analyses with three prior-sequenced models (MOC2, MOC2-10, and 4MOSC2), together with patient tumors for utility assessment. AT-84 and SCC VII bear high HNC tumor resemblance regarding mutational signatures-Trp53, Fanconi anemia, and MAPK and PI3K pathway defects. Collectively, the five models harbor genetic aberrations across 10 cancer hallmarks and 14 signaling pathways and machineries (metabolic, epigenetic, immune evasion), to extents similar in patients. Immune defects in HLA-A (H2-Q10, H2-Q4, H2-Q7, and H2-K1), Pdcd1, Tgfb1, Il2ra, Il12a, Cd40, and Tnfrsf14 are identified. Invasion/metastatic genome analyses first highlight potential druggable ERBB4 and KRAS mutations, for advanced/metastatic oral cavity cancer, as well as known metastasis players (Muc5ac, Trem3, Trp53, and Ttn) frequently captured by all models. Notable immunotherapy and precision druggable targets (Pdcd1, Erbb4, Fgfr1, H/Kras, Jak1, and Map2k2) and three druggable hubs (RTK family, MAPK, and DNA repair pathways) are frequently represented by these models. Immunocompetent metastatic HNC models are worth developing to address therapy- and invasion/metastasis-related questions in host immunity contexts.

Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma

Background

Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung adenocarcinoma by using gene expression levels in tumor samples. However, we need signatures including different types of molecular data, even cohort or patient-based biomarkers which are the candidates of molecular targeting.

Results

We built an R pipeline to carry out an integrated meta-analysis of the genomic alterations including single-nucleotide variations and the copy number variations, transcriptomics variations through RNA-seq and clinical data of patients with lung adenocarcinoma in The Cancer Genome Atlas project. We integrated significant genes including single-nucleotide variations or the copy number variations, differentially expressed genes and those in active subnetworks to construct a prognosis signature. Cox proportional hazards model with Lasso penalty and LOOCV was used to identify best gene signature among different gene categories.

We determined a 12-gene signature (BCHE, CCNA1, CYP24A1, DEPTOR, MASP2, MGLL, MYO1A, PODXL2, RAPGEF3, SGK2, TNNI2, ZBTB16) for prognostic risk prediction based on overall survival time of the patients with lung adenocarcinoma. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. The overall survival probability of these risk groups was highly significantly different for both training and test datasets.

Conclusions

This 12-gene signature could predict the prognostic risk of the patients with lung adenocarcinoma in TCGA and they are potential predictors for the survival-based risk clustering of the patients with lung adenocarcinoma. These genes can be used to cluster patients based on molecular nature and the best candidates of drugs for the patient clusters can be proposed. These genes also have a high potential for targeted cancer therapy of patients with lung adenocarcinoma.

Integrated analysis of the roles and prognostic value of RNA binding proteins in lung adenocarcinoma

Lung cancer is the top cause of carcinoma-associated deaths worldwide. RNA binding proteins (RBPs) dysregulation has been reported in various malignant tumors, and that dysregulation is closely associated with tumorigenesis and tumor progression. However, little is known about the roles of RBPs in lung adenocarcinoma (LUAD). In this study, we downloaded the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) database and determined the differently expressed RBPs between normal and cancer tissues. We then performed an integrative analysis to explore the expression and prognostic significance of these RBPs. A total of 164 differently expressed RBPs were identified, including 40 down-regulated and 124 up-regulated RBPs. Pathway and Gene ontology (GO) analysis indicated that the differently expressed RBPs were mainly related to RNA processing, RNA metabolic process, RNA degradation, RNA transport, splicing, localization, regulation of translation, RNA binding, TGF-beta signaling pathway, mRNA surveillance pathway, and aminoacyl-tRNA biosynthesis. Survival analysis revealed that the high expression of BOP1 or GNL3 or WDR12 or DCAF13 or IGF2BP3 or IGF2BP1 were associated with poor overall survival (OS). Conversely, overexpression of KHDRBS2/SMAD predicted high OS in these patients. ROC curve analysis showed that the eight hub genes with a better diagnostic accuracy to distinguish lung adenocarcinoma. The results provided novel insights into the pathogenesis of LUAD and the development of treatment targets and prognostic molecular markers.

Expression and prognostic analyses of ITGA11, ITGB4 and ITGB8 in human non-small cell lung cancer

Background

Integrins play a crucial role in the regulation process of cell proliferation, migration, differentiation, tumor invasion and metastasis. ITGA11, ITGB4 and ITGB8 are three encoding genes of integrins family. Accumulative evidences have proved that abnormal expression of ITGA11, ITGB4 and ITGB8 are a common phenomenon in different malignances. However, their expression patterns and prognostic roles for patients with non-small cell lung cancer (NSCLC) have not been completely illustrated.

Methods

We investigated the expression patterns and prognostic values of ITGA11, ITGB4 and ITGB8 in patients with NSCLC through using a series of databases and various datasets, including ONCOMINE, GEPIA, HPA, TCGA and GEO datasets.

Results

We found that the expression levels of ITGA11 and ITGB4 were significantly upregulated in both LUAD and LUSC, while ITGB8 was obviously upregulated in LUSC. Additionally, higher expression level of ITGB4 revealed a worse OS in LUAD.

Conclusion

Our findings suggested that ITGA11 and ITGB4 might have the potential ability to act as diagnostic biomarkers for both LUAD and LUSC, while ITGB8 might serve as diagnostic biomarker for LUSC. Furthermore, ITGB4 could serve as a potential prognostic biomarker for LUAD.