Prediction of tumor metastasis from sequencing data in the era of genome sequencing

GASIDN: identification of sub-Golgi proteins with multi-scale feature fusion

The Golgi apparatus is a crucial component of the inner membrane system in eukaryotic cells, playing a central role in protein biosynthesis. Dysfunction of the Golgi apparatus has been linked to neurodegenerative diseases. Accurate identification of sub-Golgi protein types is therefore essential for developing effective treatments for such diseases. Due to the expensive and time-consuming nature of experimental methods for identifying sub-Golgi protein types, various computational methods have been developed as identification tools. However, the majority of these methods rely solely on neighboring features in the protein sequence and neglect the crucial spatial structure information of the protein.To discover alternative methods for accurately identifying sub-Golgi proteins, we have developed a model called GASIDN. The GASIDN model extracts multi-dimension features by utilizing a 1D convolution module on protein sequences and a graph learning module on contact maps constructed from AlphaFold2.The model utilizes the deep representation learning model SeqVec to initialize protein sequences. GASIDN achieved accuracy values of 98.4% and 96.4% in independent testing and ten-fold cross-validation, respectively, outperforming the majority of previous predictors. To the best of our knowledge, this is the first method that utilizes multi-scale feature fusion to identify and locate sub-Golgi proteins. In order to assess the generalizability and scalability of our model, we conducted experiments to apply it in the identification of proteins from other organelles, including plant vacuoles and peroxisomes. The results obtained from these experiments demonstrated promising outcomes, indicating the effectiveness and versatility of our model. The source code and datasets can be accessed at https://github.com/SJNNNN/GASIDN .

Golgi_DF: Golgi proteins classification with deep forest

Introduction

Golgi is one of the components of the inner membrane system in eukaryotic cells. Its main function is to send the proteins involved in the synthesis of endoplasmic reticulum to specific parts of cells or secrete them outside cells. It can be seen that Golgi is an important organelle for eukaryotic cells to synthesize proteins. Golgi disorders can cause various neurodegenerative and genetic diseases, and the accurate classification of Golgi proteins is helpful to develop corresponding therapeutic drugs.

Methods

This paper proposed a novel Golgi proteins classification method, which is Golgi_DF with the deep forest algorithm. Firstly, the classified proteins method can be converted the vector features containing various information. Secondly, the synthetic minority oversampling technique (SMOTE) is utilized to deal with the classified samples. Next, the Light GBM method is utilized to feature reduction. Meanwhile, the features can be utilized in the penultimate dense layer. Therefore, the reconstructed features can be classified with the deep forest algorithm.

Results

In Golgi_DF, this method can be utilized to select the important features and identify Golgi proteins. Experiments show that the well-performance than the other art-of-the state methods. Golgi_DF as a standalone tools, all its source codes publicly available at https://github.com/baowz12345/golgiDF.

Discussion

Golgi_DF employed reconstructed feature to classify the Golgi proteins. Such method may achieve more available features among the UniRep features.

Multimodal AI for prediction of distant metastasis in carcinoma patients

Metastasis of cancer is directly related to death in almost all cases, however a lot is yet to be understood about this process. Despite advancements in the available radiological investigation techniques, not all cases of Distant Metastasis (DM) are diagnosed at initial clinical presentation. Also, there are currently no standard biomarkers of metastasis. Early, accurate diagnosis of DM is however crucial for clinical decision making, and planning of appropriate management strategies. Previous works have achieved little success in attempts to predict DM from either clinical, genomic, radiology, or histopathology data. In this work we attempt a multimodal approach to predict the presence of DM in cancer patients by combining gene expression data, clinical data and histopathology images. We tested a novel combination of Random Forest (RF) algorithm with an optimization technique for gene selection, and investigated if gene expression pattern in the primary tissues of three cancer types (Bladder Carcinoma, Pancreatic Adenocarcinoma, and Head and Neck Squamous Carcinoma) with DM are similar or different. Gene expression biomarkers of DM identified by our proposed method outperformed Differentially Expressed Genes (DEGs) identified by the DESeq2 software package in the task of predicting presence or absence of DM. Genes involved in DM tend to be more cancer type specific rather than general across all cancers. Our results also indicate that multimodal data is more predictive of metastasis than either of the three unimodal data tested, and genomic data provides the highest contribution by a wide margin. The results re-emphasize the importance for availability of sufficient image data when a weakly supervised training technique is used. Code is made available at: https://github.com/rit-cui-lab/Multimodal-AI-for-Prediction-of-Distant-Metastasis-in-Carcinoma-Patients.

Upregulation of lncRNA PINK1-AS Predicts the Distant Metastasis of Patients with Small Cell Lung Cancer

PINK1-AS has been shown to participate in gastric cancer, while its role in other tumors is unclear. This study was carried out to explore the participation of PINK1-AS in small cell lung cancer (SCLC). In this study, the expression of PINK1-AS in SCLC and paired non-cancer tissues from 60 SCLC patients and in plasma samples from 60 SCLC patients and 60 healthy controls was analyzed with RT-qPCR. Chi-squared t test was applied to analyze the associations between plasma expression levels of PINK1-AS and the clinical factors of the patients. Patients were followed up for 5 years to explore the role of PINK1-AS in the prognosis of SCLC. ROC curve analysis was applied to explore the role of PINK1-AS in the prediction of distant metastasis. Transwell assays were performed to evaluate the role of silencing and overexpression of PINK1-AS in the invasion and migration of SCLC cells. We found that PINK1-AS was upregulated in SCLC tissues compared to that in non-cancer tissues. Plasma expression levels of PINK1-AS were increased in SCLC patients compared to that in the controls. High plasma expression levels of PINK1-AS were closely associated with worse survival. Plasma expression of PINK1-AS was only closely correlated with distant tumor metastasis, but not other factors. High plasma expression levels of PINK1-AS effectively separated patients with distant metastasis from non-metastatic patients. Moreover, PINK1-AS positively regulated the migration and invasion of SCLC cells. Therefore, the upregulation of PINK1-AS predicts the distant metastasis of patients with SCLC.

A NOD-Like Receptor Signaling-Based Gene Signature Identified as a Novel Prognostic Biomarker for Predicting Overall Survival of Colorectal Cancer Patients

Background:

Colorectal Cancer (CRC) is the most frequently diagnosed gastrointestinal tract malignant tumor worldwide, which is closely associated with distant metastasis and poor prognosis. Due to high degree of heterogeneity, reliable prognostic biomarkers are urgently needed to guide the therapeutic intervention of CRC patients.

Objective:

The present study aimed to develop a NOD-Like Receptors (NLRs) signaling-based gene signature that can successfully predict the overall survival of CRC patients.

Methods:

Firstly, differentially expressed NLR signaling-related genes were identified between primary and metastatic human CRC samples. Genes with prognostic value were then screened through univariate Cox regression analysis. Next, the NLR signaling-based prognostic signature was constructed by LASSO-penalized Cox regression analysis, and its predictive ability was further confirmed in an independent cohort. Furthermore, functional studies including GO, GSEA, ssGSEA and chemotherapeutic response analyses were performed to explore the role of the NLR signaling-based signature in CRC pathogenesis and therapy.

Results:

The established prognostic signature that consisted of 7 NLR signaling-related genes can effectively stratify the high-risk and low-risk CRC patients in both training and validation cohorts. Moreover, the signature proved to be an independent indicator of overall survival in CRC patients. Functional annotation and chemotherapeutic response analyses showed that the signature was closely associated with immune status and chemotherapeutic sensitivity of CRC patients.

Conclusion:

The novel NLR signaling-based gene signature could serve as a potential tool for survival prediction and therapeutic evaluation, thereby contributing to the personalized prognostic management of CRC patients.

HMGA2 promotes the migration and invasion of gallbladder cancer cells and HMGA2 knockdown inhibits angiogenesis via targeting VEGFA

The high mobility group AT-hook 2 (HMGA2) protein has been found to be upregulated in the majority of tumor types and is associated with a poor prognosis. Previous studies have suggested the oncogenic role of HMGA2 in gallbladder cancer (GBC). The present study aimed to investigate the effects of HMGA2 on the invasion, migration and angiogenesis of GBC cells. To achieve this aim, HMGA2 was overexpressed or silenced in the GBC cell line, EH-GB1, and then the proliferation, migration, invasion and epithelial-mesenchymal transition (EMT) abilities of EH-GB1 cells were investigated using Cell Counting Kit-8, wound healing, Transwell and western blotting assays. In addition, the expression levels of VEGFA were determined in EH-GB1 cells using western blotting and reverse transcription-quantitative PCR following HMGA2 overexpression or silencing. Furthermore, HMGA2-silenced EH-GB1 cells were transfected with VEGFA overexpression plasmids to evaluate the tube formation ability of HUVECs using tube formation assay. The results demonstrated that HMGA2 silencing inhibited GBC cell proliferation, migration, invasion and EMT, as evidenced by the downregulated expression of Ki67, proliferating cell nuclear antigen, MMP2, MMP9, N-cadherin, snail family transcriptional repressor 2 and zinc finger E-box-binding homeobox 1, and attenuated cell migration and invasion. However, the opposite results were obtained following HMGA2 overexpression. Moreover, HMGA2 knockdown and overexpression downregulated and upregulated VEGFA expression, respectively. In addition, the tube formation ability of HUVECs and the expression levels of CD31, VEGFR1 and VEGFR2 were downregulated following HMGA2 silencing. However, these effects were partially rescued by simultaneous VEGFA overexpression. In conclusion, the findings of the present study revealed that HMGA2 may promote GBC cell migration, invasion, EMT and angiogenesis. Therefore, inhibiting HMGA2 expression could be considered as a possible therapeutic approach for GBC.

Novel Methylation Biomarkers for Colorectal Cancer Prognosis

Colorectal cancer (CRC) comprises the third most common cancer worldwide and the second regarding number of deaths. In order to make a correct and early diagnosis to predict metastasis formation, biomarkers are an important tool. Although there are multiple signaling pathways associated with cancer progression, the most recognized are the MAPK pathway, p53 pathway, and TGF-β pathway. These pathways regulate many important functions in the cell, such as cell cycle regulation, proliferation, differentiation, and metastasis formation, among others. Changes in expression in genes belonging to these pathways are drivers of carcinogenesis. Often these expression changes are caused by mutations; however, epigenetic changes, such as DNA methylation, are increasingly acknowledged to play a role in the deregulation of oncogenic genes. This makes DNA methylation changes an interesting biomarkers in cancer. Among the newly identified biomarkers for CRC metastasis INHBB, SMOC2, BDNF, and TBRG4 are included, all of which are highly deregulated by methylation and closely associated with metastasis. The identification of such biomarkers in metastasis of CRC may allow a better treatment and early identification of cancer formation in order to perform better diagnostics and improve the life expectancy.

Clinical Perspectives on Liquid Biopsy in Metastatic Colorectal Cancer

Liquid biopsy, which generally refers to the analysis of biological components such as circulating nuclear acids and circulating tumor cells in body fluids, particularly in peripheral blood, has shown good capacity to overcome several limitations faced by conventional tissue biopsies. Emerging evidence in recent decades has confirmed the promising role of liquid biopsy in the clinical management of various cancers, including colorectal cancer, which is one of the most prevalent cancers and the second leading cause of cancer-related deaths worldwide. Despite the challenges and poor clinical outcomes, patients with metastatic colorectal cancer can expect potential clinical benefits with liquid biopsy. Therefore, in this review, we focus on the clinical prospects of liquid biopsy in metastatic colorectal cancer, specifically with regard to the recently discovered various biomarkers identified on liquid biopsy. These biomarkers have been shown to be potentially useful in multiple aspects of metastatic colorectal cancer, such as auxiliary diagnosis of metastasis, prognosis prediction, and monitoring of therapy response.

A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma

BACKGROUND

This study aims to identify a predictive model to predict survival outcomes of osteosarcoma (OS) patients.

METHODS

A RNA sequencing dataset (the training set) and a microarray dataset (the validation set) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, respectively. Differentially expressed genes (DEGs) between metastatic and non-metastatic OS samples were identified in training set. Prognosis-related DEGs were screened and optimized by support vector machine (SVM) recursive feature elimination. A SVM classifier was built to classify metastatic and non-metastatic OS samples. Independent prognosic genes were extracted by multivariate regression analysis to build a risk score model followed by performance evaluation in two datasets by Kaplan-Meier (KM) analysis. Independent clinical prognostic indicators were identified followed by nomogram analysis. Finally, functional analyses of survival-related genes were conducted.

RESULT

Totally, 345 DEGs and 45 prognosis-related genes were screened. A SVM classifier could distinguish metastatic and non-metastatic OS samples. An eight-gene signature was an independent prognostic marker and used for constructing a risk score model. The risk score model could separate OS samples into high and low risk groups in two datasets (training set: log-rank p < 0.01, C-index = 0.805; validation set: log-rank p < 0.01, C-index = 0.797). Tumor metastasis and RS model status were independent prognostic factors and nomogram model exhibited accurate survival prediction for OS. Additionally, functional analyses of survival-related genes indicated they were closely associated with immune responses and cytokine-cytokine receptor interaction pathway.

CONCLUSION

An eight-gene predictive model and nomogram were developed to predict OS prognosis.

Identification of breast cancer risk modules via an integrated strategy

Breast cancer is one of the most common malignant cancers among females worldwide. This complex disease is not caused by a single gene, but resulted from multi-gene interactions, which could be represented by biological networks. Network modules are composed of genes with significant similarities in terms of expression, function and disease association. Therefore, the identification of disease risk modules could contribute to understanding the molecular mechanisms underlying breast cancer. In this paper, an integrated disease risk module identification strategy was proposed according to a multi-objective programming model for two similarity criteria as well as significance of permutation tests in Markov random field module score, function consistency score and Pearson correlation coefficient difference score. Three breast cancer risk modules were identified from a breast cancer-related interaction network. Genes in these risk modules were confirmed to play critical roles in breast cancer by literature review. These risk modules were enriched in breast cancer-related pathways or functions and could distinguish between breast tumor and normal samples with high accuracy for not only the microarray dataset used for breast cancer risk module identification, but also another two independent datasets. Our integrated strategy could be extended to other complex diseases to identify their risk modules and reveal their pathogenesis.

An Efficient and Flexible Method for Deconvoluting Bulk RNA-Seq Data with Single-Cell RNA-Seq Data

Estimating cell type compositions for complex diseases is an important step to investigate the cellular heterogeneity for understanding disease etiology and potentially facilitate early disease diagnosis and prevention. Here, we developed a computationally statistical method, referring to Multi-Omics Matrix Factorization (MOMF), to estimate the cell-type compositions of bulk RNA sequencing (RNA-seq) data by leveraging cell type-specific gene expression levels from single-cell RNA sequencing (scRNA-seq) data. MOMF not only directly models the count nature of gene expression data, but also effectively accounts for the uncertainty of cell type-specific mean gene expression levels. We demonstrate the benefits of MOMF through three real data applications, i.e., Glioblastomas (GBM), colorectal cancer (CRC) and type II diabetes (T2D) studies. MOMF is able to accurately estimate disease-related cell type proportions, i.e., oligodendrocyte progenitor cells and macrophage cells, which are strongly associated with the survival of GBM and CRC, respectively.