Evaluating DNA Methylation, Gene Expression, Somatic Mutation, and Their Combinations in Inferring Tumor Tissue-of-Origin

Protein structure prediction via deep learning: an in-depth review

The application of deep learning algorithms in protein structure prediction has greatly influenced drug discovery and development. Accurate protein structures are crucial for understanding biological processes and designing effective therapeutics. Traditionally, experimental methods like X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy have been the gold standard for determining protein structures. However, these approaches are often costly, inefficient, and time-consuming. At the same time, the number of known protein sequences far exceeds the number of experimentally determined structures, creating a gap that necessitates the use of computational approaches. Deep learning has emerged as a promising solution to address this challenge over the past decade. This review provides a comprehensive guide to applying deep learning methodologies and tools in protein structure prediction. We initially outline the databases related to the protein structure prediction, then delve into the recently developed large language models as well as state-of-the-art deep learning-based methods. The review concludes with a perspective on the future of predicting protein structure, highlighting potential challenges and opportunities.

A fusion model to predict the survival of colorectal cancer based on histopathological image and gene mutation

Colorectal cancer (CRC) is a prevalent gastrointestinal tumor worldwide with high morbidity and mortality. Predicting the survival of CRC patients not only enhances understanding of their life expectancies but also aids clinicians in making informed decisions regarding suitable adjuvant treatments. Although there are many clinical, genomic, and transcriptomic studies on this hot topic, only a few studies have explored the direction of integrating advanced deep learning algorithms and histopathological images. In addition, it is still unclear if combining histopathological images and molecular data can better predict patients' survival. To fill in this gap, we proposed in this study a novel multimodal deep learning computational framework using Multimodal Compact Bilinear Pooling (MCBP) to predict the 5-year survival of CRC patients from histopathological images, clinical information, and molecular data. We applied our framework to the cancer genome atlas (TCGA) CRC data, consisting of 84 samples with histopathological images, clinical information, mRNA sequencing data, and gene mutation data all available. Under the 5-fold cross-validation, the model using only histopathological images achieved an area under the curve (AUC) of 0.743. Whereas, the model combining image and clinical information and the model combining image and gene mutation information achieved AUCs of 0.771 and 0.773 respectively, better than that of the image solely. Our study demonstrates that histopathological images can reasonably predict the 5-year survival of CRC patients, and that the appropriate integration of these images with clinical or molecular data can further enhance predictive performance.

Pan-cancer analysis and experimental verification of cytochrome B561 as a prognostic and therapeutic biomarker in breast cancer

OBJECTIVE

This study investigates Cytochrome B561 (CYB561) expression in Pan-Cancer, its relationship with immune invasion, and its prognostic value in Breast Cancer (BRCA) patients.

METHODS

Data from The Cancer Genome Atlas (TCGA) were analyzed. CYB561 expression in normal and tumor tissues was examined, with correlations to immune invasion, mutation, and immune checkpoints. Wilcoxon rank-sum test assessed expression differences. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. Logistic regression, Kaplan-Meier, and Cox regression analyses evaluated clinicopathological features and survival outcomes. A Cox multivariate analysis-based Nomogram predicted CYB561's prognostic impact. CYB561 knockout in breast cancer cells assessed functional effects. Single-cell RNA sequencing identified prognostic biomarkers.

RESULTS

CYB561 was highly expressed in most tumors. BRCA showed the highest correlation with ESTIMATE scores and significant negative correlation with immune checkpoints. High CYB561 expression correlated with specific clinicopathological features and survival outcomes. The nomogram predicted BRCA prognosis. CYB561 knockout inhibited breast cancer cell proliferation. Seven predictive agents for CYB561 inhibition were identified.

CONCLUSIONS

CYB561 exhibits aberrant expression in tumors, particularly in BRCA, and serves as a predictive marker for immune-related therapies and a prognostic indicator in BRCA.

A machine learning-based method for feature reduction of methylation data for the classification of cancer tissue origin

BACKGROUND

Genome DNA methylation profiling is a promising yet costly method for cancer classification, involving substantial data. We developed an ensemble learning model to identify cancer types using methylation profiles from a limited number of CpG sites.

METHODS

Analyzing methylation data from 890 samples across 10 cancer types from the TCGA database, we utilized ANOVA and Gain Ratio to select the most significant CpG sites, then employed Gradient Boosting to reduce these to just 100 sites.

RESULTS

This approach maintained high accuracy across multiple machine learning models, with classification accuracy rates between 87.7% and 93.5% for methods including Extreme Gradient Boosting, CatBoost, and Random Forest. This method effectively minimizes the number of features needed without losing performance, helping to classify primary organs and uncover subgroups within specific cancers like breast and lung.

CONCLUSIONS

Using a gradient boosting feature selector shows potential for streamlining methylation-based cancer classification.

Diagnosis progress of carcinoma of unknown primary

Carcinoma of unknown primary (CUP) is a common and complex type of tumor in clinical practice, where the primary site cannot be determined through conventional diagnostic methods, posing significant challenges for clinical diagnosis and treatment. In recent years, advancements in gene expression profiling and genetic testing technologies have provided new perspectives for CUP research, driving progress in this field. By analyzing gene expression profiles, researchers can more effectively identify the tissue origin of tumors, thereby improving diagnostic accuracy. At the same time, the potential application of genetic testing is continuously being explored, offering new possibilities for personalized treatment. This article aims to discuss the latest advancements in the diagnosis of CUP, analyze the importance of gene expression profiling and genetic testing in tumor origin identification and their clinical applications, and summarize current research progress and future research directions, with the goal of providing a theoretical basis for the early diagnosis and treatment of CUP.

Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models

Relapse and metastasis occur in 30-40% of breast cancer patients, even after targeted treatments like trastuzumab for HER2-positive breast cancer. Accurate individual prognosis is essential for determining appropriate adjuvant treatment and early intervention. This study aims to enhance relapse and metastasis prediction using an innovative framework with machine learning (ML) and ensemble learning (EL) techniques. The developed framework is analyzed using The Cancer Genome Atlas (TCGA) data, which has 123 HER2-positive breast cancer patients. Our two-stage experimental approach first applied six basic ML models (support vector machine, logistic regression, decision tree, random forest, adaptive boosting, and extreme gradient boosting) and then ensembled these models using weighted averaging, soft voting, and hard voting techniques. The weighted averaging ensemble approach achieved enhanced performances of 88.46% accuracy, 89.74% precision, 94.59% sensitivity, 73.33% specificity, 92.11% F-Value, 71.07% Mathew's correlation coefficient, and an AUC of 0.903. This framework enables the accurate prediction of relapse and metastasis in HER2-positive breast cancer patients using H&E images and clinical data, thereby assisting in better treatment decision-making.

PICALM as a Novel Prognostic Biomarker and Its Correlation with Immune Infiltration in Breast Cancer

PICALM (phosphatidylinositol-binding clathrin assembly protein) mutations have been linked to a number of human disorders, including leukemia, Alzheimer's disease, and Parkinson's disease. Nevertheless, the effect of PICALM on cancer, particularly on prognosis and immune infiltration in individuals with BRCA, is unknown. We obtained the data of breast cancer patients from The Cancer Genome Atlas (TCGA) database, and analyzed the expression of PICALM in breast cancer, its impact on survival' and its role in tumor immune invasion. Finally, in vitro cellular experiments were performed to validate the results. Research has found that PICALM expression was shown to be downregulated in BRCA and to be substantially linked with clinical stage, histological type, PAM50, and age. PICALM downregulation was linked to a lower overall survival (OS) and disease-specific survival (DSS) in BRCA patients. A multivariate Cox analysis revealed that PICALM is an independent predictor of OS. The enriched pathways revealed by functional enrichment analysis included oxidative phosphorylation, angiogenesis, the TGF signaling pathway, and the IL-6/JAK/STAT3 signaling system. Furthermore, the amount of immune cell infiltration by B cells, eosinophils, mast cells, neutrophils, and T cells was positively linked with PICALM expression. Finally, we experimentally verified that low expression of PICALM can reduce proliferation, migration, and invasion in tumor cells. This evidence shows that PICALM expression impacts prognosis, immune infiltration, and pathway expression in breast cancer patients, and it might be a potential predictive biomarker for the disease.

Seeing the primary tumor because of all the trees: Cancer type prediction on low-dimensional data

The Cancer of Unknown Primary (CUP) syndrome is characterized by identifiable metastases while the primary tumor remains hidden. In recent years, various data-driven approaches have been suggested to predict the location of the primary tumor (LOP) in CUP patients promising improved diagnosis and outcome. These LOP prediction approaches use high-dimensional input data like images or genetic data. However, leveraging such data is challenging, resource-intensive and therefore a potential translational barrier. Instead of using high-dimensional data, we analyzed the LOP prediction performance of low-dimensional data from routine medical care. With our findings, we show that such low-dimensional routine clinical information suffices as input data for tree-based LOP prediction models. The best model reached a mean Accuracy of 94% and a mean Matthews correlation coefficient (MCC) score of 0.92 in 10-fold nested cross-validation (NCV) when distinguishing four types of cancer. When considering eight types of cancer, this model achieved a mean Accuracy of 85% and a mean MCC score of 0.81. This is comparable to the performance achieved by approaches using high-dimensional input data. Additionally, the distribution pattern of metastases appears to be important information in predicting the LOP.

Fusion Genes Landscape of Lung Cancer Patients From Inner Mongolia, China

Lung cancer is the leading cause of cancer-related deaths globally. Gene fusion, a key driver of tumorigenesis, has led to the identification of numerous driver gene fusions for lung cancer diagnosis and treatment. However, previous studies focused on Western populations, leaving the possibility of unrecognized lung cancer-associated gene fusions specific to Inner Mongolia due to its unique genetic background and dietary habits. To address this, we conducted DNA sequencing analysis on tumor and adjacent nontumor tissues from 1200 individuals with lung cancer in Inner Mongolia. Our analysis established a comprehensive fusion gene landscape specific to lung cancer in Inner Mongolia, shedding light on potential region-specific molecular mechanisms underlying the disease. Compared to Western cohorts, we observed a higher occurrence of ALK and RET fusions in Inner Mongolian patients. Additionally, we discovered eight novel fusion genes in three patients: SLC34A2-EPHB1, CCT6P3-GSTP1, BARHL2-APC, HRAS-MELK, FAM134B-ERBB2, ABCB1-GIPC1, GPR98-ALK, and FAM134B-SALL1. These previously unreported fusion genes suggest potential regional specificity. Furthermore, we characterized the fusion genes' structures based on breakpoints and described their impact on major functional gene domains. Importantly, the identified novel fusion genes exhibited significant clinical and pathological relevance. Notably, patients with SLC34A2-EPHB1, CCT6P3-GSTP1, and BARHL2-APC fusions showed sensitivity to the combination of chemotherapy and immunotherapy. Patients with HRAS-MELK, FAM134B-ERBB2, and ABCB1-GIPC1 fusions showed sensitivity to chemotherapy. In summary, our study provides novel insights into the frequency, distribution, and characteristics of specific fusion genes, offering valuable guidance for the development of effective clinical treatments, particularly in Inner Mongolia.

Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns

Cancer, a collection of more than two hundred different diseases, remains a leading cause of morbidity and mortality worldwide. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore, the early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of various cancer types. Epigenetic changes such as DNA methylation are some of the early events underlying carcinogenesis. Here, we report on an interpretable machine learning model that can classify 13 cancer types as well as non-cancer tissue samples using only DNA methylome data, with 98.2% accuracy. We utilize the features identified by this model to develop EMethylNET, a robust model consisting of an XGBoost model that provides information to a deep neural network that can generalize to independent data sets. We also demonstrate that the methylation-associated genomic loci detected by the classifier are associated with genes, pathways and networks involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.

Association of ADP‑ribosylation factor family genes with prognosis and immune infiltration of breast cancer

Breast cancer (BC) is the most common type of cancer found in women. ADP-ribosylation factors (ARFs) are a group of small proteins that bind to GTP and are involved in controlling different cellular functions. The function and evolution of multiple ARFs in BC have remained to be fully elucidated, despite existing studies on this protein family in Homo sapiens and other species. In the present study, a systematic analysis of ARF expression levels in BC tissues compared to normal breast tissues was performed using data from The Cancer Genome Atlas database. The analysis revealed significantly higher expression of ARFs in BC tissues. In addition, the prognostic significance of ARF1 and ARF3-6 expression levels was assessed in patients with BC. Of note, elevated ARF1 expression was associated with reduced rates of distant metastasis-free survival (DMFS), overall survival (OS) and recurrence-free survival (RFS) in affected individuals. Similarly, patients with high expression levels of ARF3 had lower post-progression survival (PPS) rates. In addition, patients with higher ARF4 expression had worse PPS and patients with high ARF5 expression exhibited lower DMFS. Patients with high ARF6 expression had worse DMFS, OS, RFS and predictive power score values. Furthermore, the expression of ARF was found to be strongly linked to the infiltration of various immune cell types, namely dendritic cells, macrophages, neutrophils, CD8+ T cells and B cells. These significant associations offer a solid foundation for the potential utilization of new therapeutic targets and predictive markers for the treatment of BC.

Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning

Tumor mutational burden (TMB) is a significant predictive biomarker for selecting patients that may benefit from immune checkpoint inhibitor therapy. Whole exome sequencing is a common method for measuring TMB; however, its clinical application is limited by the high cost and time-consuming wet-laboratory experiments and bioinformatics analysis. To address this challenge, we downloaded multimodal data of 326 gastric cancer patients from The Cancer Genome Atlas, including histopathological images, clinical data and various molecular data. Using these data, we conducted a comprehensive analysis to investigate the relationship between TMB, clinical factors, gene expression and image features extracted from hematoxylin and eosin images. We further explored the feasibility of predicting TMB levels, i.e. high and low TMB, by utilizing a residual network (Resnet)-based deep learning algorithm for histopathological image analysis. Moreover, we developed a multimodal fusion deep learning model that combines histopathological images with omics data to predict TMB levels. We evaluated the performance of our models against various state-of-the-art methods using different TMB thresholds and obtained promising results. Specifically, our histopathological image analysis model achieved an area under curve (AUC) of 0.749. Notably, the multimodal fusion model significantly outperformed the model that relied only on histopathological images, with the highest AUC of 0.971. Our findings suggest that histopathological images could be used with reasonable accuracy to predict TMB levels in gastric cancer patients, while multimodal deep learning could achieve even higher levels of accuracy. This study sheds new light on predicting TMB in gastric cancer patients.

Effects of different KRAS mutants and Ki67 expression on diagnosis and prognosis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a prevalent form of non-small cell lung cancer with a rising incidence in recent years. Understanding the mutation characteristics of LUAD is crucial for effective treatment and prediction of this disease. Among the various mutations observed in LUAD, KRAS mutations are particularly common. Different subtypes of KRAS mutations can activate the Ras signaling pathway to varying degrees, potentially influencing the pathogenesis and prognosis of LUAD. This study aims to investigate the relationship between different KRAS mutation subtypes and the pathogenesis and prognosis of LUAD. A total of 63 clinical samples of LUAD were collected for this study. The samples were analyzed using targeted gene sequencing panels to obtain sequencing data. To complement the dataset, additional clinical and sequencing data were obtained from TCGA and MSK. The analysis revealed significantly higher Ki67 immunohistochemical scores in patients with missense mutations compared to controls. Moreover, the expression level of KRAS was found to be significantly correlated with Ki67 expression. Enrichment analysis indicated that KRAS missense mutations activated the SWEET_LUNG_CANCER_KRAS_DN and CREIGHTON_ENDOCRINE_THERAPY_RESISTANCE_2 pathways. Additionally, patients with KRAS missense mutations and high Ki67 IHC scores exhibited significantly higher tumor mutational burden levels compared to other groups, which suggests they are more likely to be responsive to ICIs. Based on the data from MSK and TCGA, it was observed that patients with KRAS missense mutations had shorter survival compared to controls, and Ki67 expression level could more accurately predict patient prognosis. In conclusion, when utilizing KRAS mutations as biomarkers for the treatment and prediction of LUAD, it is important to consider the specific KRAS mutant subtypes and Ki67 expression levels. These findings contribute to a better understanding of LUAD and have implications for personalized therapeutic approaches in the management of this disease.

Co-expression of IL-21-Enhanced NKG2D CAR-NK cell therapy for lung cancer

BACKGROUND

Adoptive cell therapy has achieved great success in treating hematological malignancies. However, the production of chimeric antigen receptor T (CAR-T) cell therapy still faces various difficulties. Natural killer (NK)-92 is a continuously expandable cell line and provides a promising alternative for patient's own immune cells.

METHODS

We established CAR-NK cells by co-expressing natural killer group 2 member D (NKG2D) and IL-21, and evaluated the efficacy of NKG2D-IL-21 CAR-NK cells in treating lung cancer in vitro and in vivo.

RESULTS

Our data suggested that the expression of IL-21 effectively increased the cytotoxicity of NKG2D CAR-NK cells against lung cancer cells in a dose-dependent manner and suppressed tumor growth in vitro and in vivo. In addition, the proliferation of NKG2D-IL-21 CAR-NK cells were enhanced while the apoptosis and exhaustion of these cells were suppressed. Mechanistically, IL-21-mediated NKG2D CAR-NK cells function by activating AKT signaling pathway.

CONCLUSION

Our findings provide a novel option for treating lung cancer using NKG2D-IL-21 CAR-NK cell therapy.

New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges

Despite a standardized diagnostic examination, cancer of unknown primary (CUP) is a rare metastatic malignancy with an unidentified tissue of origin (TOO). Patients diagnosed with CUP are typically treated with empiric chemotherapy, although their prognosis is worse than those with metastatic cancer of a known origin. TOO identification of CUP has been employed in precision medicine, and subsequent site-specific therapy is clinically helpful. For example, molecular profiling, including genomic profiling, gene expression profiling, epigenetics and proteins, has facilitated TOO identification. Moreover, machine learning has improved identification accuracy, and non-invasive methods, such as liquid biopsy and image omics, are gaining momentum. However, the heterogeneity in prediction accuracy, sample requirements and technical fundamentals among the various techniques is noteworthy. Accordingly, we systematically reviewed the development and limitations of novel TOO identification methods, compared their pros and cons and assessed their potential clinical usefulness. Our study may help patients shift from empirical to customized care and improve their prognoses.

Colorectal cancer with low SLC35A3 is associated with immune infiltrates and poor prognosis

The expression level of SLC35A3 is associated with the prognosis of many cancers, but its role in colorectal cancer (CRC) is unclear. The purpose of our study was to elucidate the role of SLC35A3 in CRC. The expression levels of SLC35A3 in CRC were evaluated through tumor immune resource assessment (TIMER), The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC), Human Protein Atlas (HPA), qRT-PCR, and immunohistochemical evaluation. TCGA, GEO, and ICGC databases were used to analyze the diagnostic and prognostic value of SLC35A3 in CRC. A overall survival (OS) model was constructed and validated based on the expression level of SLC35A3 and multivariable analysis results. The cBioPortal tool was used to analyze SLC35A3 mutation in CRC. The UALCAN tool was used to analyze the promoter methylation level of SLC35A3 in colorectal cancer. In addition, the role of SLC35A3 in CRC was determined through GO analysis, KEGG analysis, gene set enrichment analysis (GSEA), immune infiltration analysis, and immune checkpoint correlation analysis. In vitro experiments validated the function of SLC35A3 in colorectal cancer cells. Compared with adjacent normal tissues and colonic epithelial cells, the expression of SLC35A3 was decreased in CRC tissues and CRC cell lines. Low expression of SLC35A3 was associated with N stage, pathological stage, and lymphatic infiltration, and it was unfavorable for OS, disease-specific survival (DSS), recurrence-free survival (RFS), and post-progression survival (PPS). According to the Receiver Operating Characteristic (ROC) analysis, SLC35A3 is a potential important diagnostic biomarker for CRC patients. The nomograph based on the expression level of SLC35A3 showed a better predictive model for OS than single prognostic factors and TNM staging. SLC35A3 has multiple types of mutations in CRC, and its promoter methylation level is significantly decreased. GO and KEGG analysis indicated that SLC35A3 may be involved in transmembrane transport protein activity, cell communication, and interaction with neurotransmitter receptors. GSEA revealed that SLC35A3 may be involved in energy metabolism, DNA repair, and cancer pathways. In addition, SLC35A3 was closely related to immune cell infiltration and immune checkpoint expression. Immunohistochemistry confirmed the positive correlation between SLC35A3 and helper T cell infiltration. In vitro experiments showed that overexpression of SLC35A3 inhibited the proliferation and invasion capability of colorectal cancer cells and promoted apoptosis. The results of this study indicate that decreased expression of SLC35A3 is closely associated with poor prognosis and immune cell infiltration in colorectal cancer, and it can serve as a promising independent prognostic biomarker and potential therapeutic target.

Methylation Array Signals are Predictive of Chronological Age Without Bisulfite Conversion

DNA methylation data has been used to make "epigenetic clocks" which attempt to measure chronological and biological aging. These models rely on data derived from bisulfite-based measurements, which exploit a semi-selective deamination and a genomic reference to determine methylation states. Here, we demonstrate how another hallmark of aging, genomic instability, influences methylation measurements in both bisulfite sequencing and methylation arrays. We found that non-methylation factors lead to "pseudomethylation" signals that are both confounding of epigenetic clocks and uniquely age predictive. Quantifying these covariates in aging studies will be critical to building better clocks and designing appropriate studies of epigenetic aging.

Integrative landscape analysis of prognostic model biomarkers and immunogenomics of disulfidptosis-related genes in breast cancer based on LASSO and WGCNA analyses

BACKGROUND

Disulfidptosis is a novel type of programmed cell death. However, the value of disulfidptosis-related genes (DRGs) in the prediction of breast cancer prognosis is unclear.

METHODS

RNA-seq data of 1231 patients, together with information on patient clinical characteristics and prognosis, were downloaded from TCGA. DRGs were identified between cancerous and non-cancerous tissues. The LASSO algorithm was used to assign half of the samples to the training set. Risk scores were used for construction of a prognostic model for risk stratification and prognosis prediction, and the clinical applicability was examined using a line diagram. The relationships between risk scores, immune cell infiltration, molecular subtypes, and responses to immunotherapy and chemotherapy were examined.

RESULTS

We identified and obtained four DRG-related prognostic lncRNAs (AC009097.2, AC133552.5, YTHDF3-AS1, and AC084824.5), which were used for establishing the risk model. Longer survival was associated with low risk. The DRG-associated lncRNAs were found to independently predict patient prognosis. The AUCs under the ROCs for one-, three-, and 5-year survival in the training cohort were 0.720, 0.687, and 0.692, respectively. The model showed that the high-risk patients had reduced overall survival as well as high tumor mutation burdens. Furthermore, high-risk patients showed increased sensitivity to therapeutic drugs, including docetaxel, paclitaxel, and oxaliplatin.

CONCLUSION

The risk score model was effective for predicting both prognosis and sensitivity to therapeutic drugs, suggesting its possible usefulness for the management of patients with breast cancer.

DLC-ac4C: A Prediction Model for N4-acetylcytidine Sites in Human mRNA Based on DenseNet and Bidirectional LSTM Methods

Introduction

N4 acetylcytidine (ac4C) is a highly conserved nucleoside modification that is essential for the regulation of immune functions in organisms. Currently, the identification of ac4C is primarily achieved using biological methods, which can be time-consuming and labor-intensive. In contrast, accurate identification of ac4C by computational methods has become a more effective method for classification and prediction.

Aim

To the best of our knowledge, although there are several computational methods for ac4C locus prediction, the performance of the models they constructed is poor, and the network structure they used is relatively simple and suffers from the disadvantage of network degradation. This study aims to improve these limitations by proposing a predictive model based on integrated deep learning to better help identify ac4C sites.

Methods

In this study, we propose a new integrated deep learning prediction framework, DLC-ac4C. First, we encode RNA sequences based on three feature encoding schemes, namely C2 encoding, nucleotide chemical property (NCP) encoding, and nucleotide density (ND) encoding. Second, one-dimensional convolutional layers and densely connected convolutional networks (DenseNet) are used to learn local features, and bi-directional long short-term memory networks (Bi-LSTM) are used to learn global features. Third, a channel attention mechanism is introduced to determine the importance of sequence characteristics. Finally, a homomorphic integration strategy is used to limit the generalization error of the model, which further improves the performance of the model.

Results

The DLC-ac4C model performed well in terms of sensitivity (Sn), specificity (Sp), accuracy (Acc), Mathews correlation coefficient (MCC), and area under the curve (AUC) for the independent test data with 86.23%, 79.71%, 82.97%, 66.08%, and 90.42%, respectively, which was significantly better than the prediction accuracy of the existing methods.

Conclusion

Our model not only combines DenseNet and Bi-LSTM, but also uses the channel attention mechanism to better capture hidden information features from a sequence perspective, and can identify ac4C sites more effectively.

Machine learning for pan-cancer classification based on RNA sequencing data

Despite recent improvements in cancer diagnostics, 2%-5% of all malignancies are still cancers of unknown primary (CUP), for which the tissue-of-origin (TOO) cannot be determined at the time of presentation. Since the primary site of cancer leads to the choice of optimal treatment, CUP patients pose a significant clinical challenge with limited treatment options. Data produced by large-scale cancer genomics initiatives, which aim to determine the genomic, epigenomic, and transcriptomic characteristics of a large number of individual patients of multiple cancer types, have led to the introduction of various methods that use machine learning to predict the TOO of cancer patients. In this review, we assess the reproducibility, interpretability, and robustness of results obtained by 20 recent studies that utilize different machine learning methods for TOO prediction based on RNA sequencing data, including their reported performance on independent data sets and identification of important features. Our review investigates the strengths and weaknesses of different methods, checks the correspondence of their results, and identifies potential issues with datasets used for model training and testing, assessing their potential usefulness in a clinical setting and suggesting future improvements.

Overexpression of ferritin light chain as a poor prognostic factor for breast cancer

BACKGROUND

Ferritin light chain (FTL) is involved in tumor progression, but the specific molecular processes by which FTL affects the development of breast cancer (BRCA) have remained unknown. In this research, the clinicopathological significance of FTL overexpression in BRCA was investigated.

METHODS

To investigate the role of FTL in BRCA, we utilized multiple online databases to analyse FTL expression levels in BRCA. Next, we reviewed the expression and localization of the FTL protein in BRCA by immunohistochemistry (IHC), Western blot (WB) and immunofluorescence (IF) staining. To assess the impact of FTL on patient prognosis, we conducted Kaplan‒Meier, univariate and multivariate survival analyses. The relationship between FTL and immune infiltration in BRCA was also analysed in the TISCH and SangerBox databases. MTT, malondialdehyde (MDA) and reactive oxygen species (ROS) assays were carried out to investigate the molecular mechanisms of FTL action in BRCA cells.

RESULTS

FTL was significantly upregulated in BRCA compared to normal tissues. Its expression significantly linked to histological grade (P = 0.038), PR expression (P = 0.021), Her2 expression (P = 0.012) and Ki-67 expression (P = 0.040) in patients with BRCA. Furthermore, the expression of the FTL protein was higher in the BRCA cell lines than in the normal breast cells and mainly localized in the cytoplasm. Compared to patients with a low level of FTL expression, patients with a high level of FTL expression showed lower overall survival (OS). More convincingly, univariate and multivariate statistical analyses revealed that FTL expression (P = 0.000), ER expression (P = 0.036) and Her2 expression (P = 0.028) were meaningful independent prognostic factors in patients with BRCA. FTL was associated with immune infiltration in BRCA. Functional experiments further revealed that FTL knockdown inhibited the capacity of proliferation and increased the level of oxidative stress in BRCA cells.

CONCLUSIONS

Overexpression of FTL was associated with the progression of BRCA. FTL overexpression may become a biomarker for the evaluation of poor prognosis in patients with BRCA.

A cross-cohort computational framework to trace tumor tissue-of-origin based on RNA sequencing

Carcinoma of unknown primary (CUP) is a type of metastatic cancer with tissue-of-origin (TOO) unidentifiable by traditional methods. CUP patients typically have poor prognosis but therapy targeting the original cancer tissue can significantly improve patients' prognosis. Thus, it's critical to develop accurate computational methods to infer cancer TOO. While qPCR or microarray-based methods are effective in inferring TOO for most cancer types, the overall prediction accuracy is yet to be improved. In this study, we propose a cross-cohort computational framework to trace TOO of 32 cancer types based on RNA sequencing (RNA-seq). Specifically, we employed logistic regression models to select 80 genes for each cancer type to create a combined 1356-gene set, based on transcriptomic data from 9911 tissue samples covering the 32 cancer types with known TOO from the Cancer Genome Atlas (TCGA). The selected genes are enriched in both tissue-specific and tissue-general functions. The cross-validation accuracy of our framework reaches 97.50% across all cancer types. Furthermore, we tested the performance of our model on the TCGA metastatic dataset and International Cancer Genome Consortium (ICGC) dataset, achieving an accuracy of 91.09% and 82.67%, respectively, despite the differences in experiment procedures and pipelines. In conclusion, we developed an accurate yet robust computational framework for identifying TOO, which holds promise for clinical applications. Our code is available at http://github.com/wangbo00129/classifybysklearn .

A meta-analysis of tissue microbial biomarkers for recurrence and metastasis in multiple cancer types

Background. Local recurrence and distant metastasis are the main causes of death in patients with cancer. Only considering species abundance changes when identifying markers of recurrence and metastasis in patients hinders finding solutions.Hypothesis. Consideration of microbial abundance changes and microbial interactions facilitates the identification of microbial markers of tumour recurrence and metastasis.Aim. This study aims to simultaneously consider microbial abundance changes and microbial interactions to identify microbial markers of recurrence and metastasis in multiple cancer types.Method. One thousand one hundred and six non-RM (patients without recurrence and metastasis within 3 years after initial surgery) tissue samples and 912 RM (patients with recurrence or metastasis within 3 years after initial surgery) tissue samples representing 11 cancer types were collected from The Cancer Genome Atlas (TCGA).Results. Tumour tissue bacterial composition differed significantly among 11 cancers. Among them, the tissue microbiome of four cancers, head and neck squamous cell carcinoma (HNSC), lung squamous cell carcinoma (LUSC), stomach adenocarcinoma (STAD) and uterine corpus endometrial carcinoma (UCEC), showed relatively good performance in predicting recurrence and metastasis in patients, with areas under the receiver operating characteristic curve (AUCs) of 0.78, 0.74, 0.91 and 0.93, respectively. Considering both species abundance changes and microbial interactions for the four cancers, a combination of nine genera (Niastella, Schlesneria, Thioalkalivibrio, Phaeobacter, Sphaerotilus, Thiomonas, Lawsonia, Actinobacillus and Spiroplasma) performed best in predicting patient survival.Conclusion. Taken together, our results imply that comprehensive consideration of microbial abundance changes and microbial interactions is helpful for mining bacterial markers that carry prognostic information.

Molecular characterization of colorectal adenoma and colorectal cancer via integrated genomic transcriptomic analysis

Introduction

Colorectal adenoma can develop into colorectal cancer. Determining the risk of tumorigenesis in colorectal adenoma would be critical for avoiding the development of colorectal cancer; however, genomic features that could help predict the risk of tumorigenesis remain uncertain.

Methods

In this work, DNA and RNA parallel capture sequencing data covering 519 genes from colorectal adenoma and colorectal cancer samples were collected. The somatic mutation profiles were obtained from DNA sequencing data, and the expression profiles were obtained from RNA sequencing data.

Results

Despite some similarities between the adenoma samples and the cancer samples, different mutation frequencies, co-occurrences, and mutually exclusive patterns were detected in the mutation profiles of patients with colorectal adenoma and colorectal cancer. Differentially expressed genes were also detected between the two patient groups using RNA sequencing. Finally, two random forest classification models were built, one based on mutation profiles and one based on expression profiles. The models distinguished adenoma and cancer samples with accuracy levels of 81.48% and 100.00%, respectively, showing the potential of the 519-gene panel for monitoring adenoma patients in clinical practice.

Conclusion

This study revealed molecular characteristics and correlations between colorectal adenoma and colorectal cancer, and it demonstrated that the 519-gene panel may be used for early monitoring of the progression of colorectal adenoma to cancer.

Intratumoral Microbiota-Host Interactions Shape the Variability of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma in Recurrence and Metastasis

Differences in tissue microbiota-host interaction between lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) about recurrence and metastasis have not been well studied. In this study, we performed bioinformatics analyses to identify the genes and tissue microbes significantly associated with recurrence or metastasis. All lung cancer patients were divided into the recurrence or metastasis (RM) group and the nonrecurrence and nonmetastasis (non-RM) group according to whether or not they had recurred or metastasized within 3 years after the initial surgery. Results showed that there were significant differences between LUAD and LUSC in gene expression and microbial abundance associated with recurrence and metastasis. Compared with non-RM, the bacterial community of RM had a lower richness in LUSC. In LUSC, host genes significantly correlated with tissue microbe, whereas host-tissue microbe interaction in LUAD was rare. Then, we established a novel multimodal machine learning model based on genes and microbes to predict the recurrence and metastasis risk of a LUSC patient, which achieves an area under the curve (AUC) of 0.81. In addition, the predicted risk score was significantly associated with the patient's survival. IMPORTANCE Our study elucidates significant differences in RM-associated host-microbe interactions between LUAD and LUSC. Besides, the microbes in tumor tissue could be used to predict the RM risk of LUSC, and the predicted risk score is associated with patients' survival.

Application of oncolytic virus in tumor therapy

Oncolytic viruses (OVs) can selectively kill tumor cells without affecting normal cells, as well as activate the innate and adaptive immune systems in patients. Thus, they have been considered as a promising measure for safe and effective cancer treatment. Recently, a few genetically engineered OVs have been developed to further improve the effect of tumor elimination by expressing specific immune regulatory factors and thus enhance the body's antitumor immunity. In addition, the combined therapies of OVs and other immunotherapies have been applied in clinical. Although there are many studies on this hot topic, a comprehensive review is missing on illustrating the mechanisms of tumor clearance by OVs and how to modify engineered OVs to further enhance their antitumor effects. In this study, we provided a review on the mechanisms of immune regulatory factors in OVs. In addition, we reviewed the combined therapies of OVs with other therapies including radiotherapy and CAR-T or TCR-T cell therapy. The review is useful in further generalize the usage of OV in cancer treatment.

Neutrophil-to-lymphocyte ratio is a negative prognostic biomarker for luminal A breast cancer

BACKGROUND

Inflammation plays an important role in the occurrence, development, and metastasis of tumors. However, the prognostic role of the neutrophil-to-lymphocyte ratio (NLR) in patients with luminal A breast cancer has rarely been reported in the literature. The purpose of this study was to investigate the relationship between preoperative peripheral blood NLR and the survival rate of patients with luminal A breast cancer.

METHODS

Data from 226 eligible patients with luminal A breast cancer at the Chongqing University Cancer Hospital between 2011 and 2016 were obtained. The cut-off value for NLR for predicting overall survival (OS) rate was obtained from the receiver operating characteristic (ROC) curve. The baseline characteristics of 2 groups were compared using the Chi-square test or Fisher's exact test, and OS was estimated using the Kaplan-Meier method. Cox analysis was performed to determine the correlation between clinicopathological parameters and prognosis.

RESULTS

ROC curve analysis showed that the cutoff value for NLR to predict OS was 2.0. Kaplan-Meier analysis revealed that the OS of patients with a NLR <2.0 was significantly longer than that of patients with a higher NLR >2 (P<0.0001). The area under the curve (AUC) for NLR to predict OS was 0.781 [95% confidence interval (CI): 0.712-0.851], sensitivity was 54.17%, and specificity was 97.06%. In univariate Cox regression analysis, NLR, tumor (T) stage (T3-T4 vs. T1-T2), and histological grade (II-III vs. I) were all significantly associated with OS. In multivariate Cox regression analysis, NLR and histology grade (II-III vs. I) were independent prognostic factors for OS.

CONCLUSIONS

The results suggested that higher preoperative NLR was associated with worse prognosis in luminal A breast cancer.

Current Understanding of Microbiomes in Cancer Metastasis

Cancer has been the first killer that threatens people's lives and health. Despite recent improvements in cancer treatment, metastasis continues to be the main reason for death from cancer. The functions of microbiome in cancer metastasis have been studied recently, and it is proved that microbiome can influence tumor metastasis, as well as positive or negative responses to therapy. Here, we summarize the mechanisms of microorganisms affecting cancer metastasis, which include epithelial-mesenchymal transition (EMT), immunity, fluid shear stress (FSS), and matrix metalloproteinases (MMPs). This review will not only give a further understanding of relationship between microbiome and cancer metastasis, but also provide a new perspective for the microbiome's application in cancer metastasis prevention, early detection, and treatment.

The diagnosis and treatment for a patient with cancer of unknown primary: A case report

Background: Cancer of unknown primary (CUP) is a class of metastatic malignant tumors whose primary location cannot be determined. The diagnosis and treatment of CUP are a considerable challenge for clinicians. Herein, we report a CUP case whose corresponding primary tumor sites were successfully identified, and the patient received proper treatment. Case report: In February 2022, a 74-year-old woman was admitted to the Medical Oncology Department at Sir Run Run Shaw Hospital for new lung and intestinal tumors after more than 9 years of breast cancer surgery. After laparoscopically assisted right hemicolectomy, pathology revealed mucinous adenocarcinoma; the pathological stage was pT2N0M0. Results from needle biopsies of lung masses suggested poorly differentiated cancer, ER (-), PR (-), and HER2 (-), which combined with the clinical history, did not rule out metastatic breast cancer. A surgical pathology sample was needed to determine the origin of the tumor tissue, but the patient's chest structure showed no indications for surgery. Analysis of the tumor's traceable gene expression profile prompted breast cancer, and analysis of next-generation amplification sequencing (NGS) did not obtain a potential drug target. We developed a treatment plan based on comprehensive immunohistochemistry, a gene expression profile, and NGS analysis. The treatment plan was formulated using paclitaxel albumin and capecitabine in combination with radiotherapy. The efficacy evaluation was the partial response (PR) after four cycles of chemotherapy and two cycles combined with radiotherapy. Conclusion: This case highlighted the importance of identifying accurate primary tumor location for patients to benefit from treatment, which will provide a reference for the treatment decisions of CUP tumors in the future.

Palindrome-Embedded Hairpin Structure and Its Target-Catalyzed Padlock Cyclization for Label-Free MicroRNA-Initiated Rolling Circle Amplification

Highly sensitive detection of microRNAs (miRNAs) is of great significance in early diagnosis of cancers. Here, we develop a palindrome-embedded hairpin structure and its target-catalyzed padlock cyclization for rolling circle amplification, named PHP-RCA for simplicity, which can be applied in label-free ultrasensitive detection of miRNA. PHP-RCA is a facile system that consists of only an oligonucleotide probe with a palindrome-embedded hairpin structure (PHP). The two ends of PHP were extended as overhangs and designed with the complementary sequences of the target. Hence, the phosphorylated PHP can be cyclized by T4 DNA ligase in the presence of the target that serves as the ligation template. This ligation has formed a palindrome-embedded dumbbell-shaped probe (PDP) that allows phi29 polymerase to perform a typical target-primed RCA on PDP by taking miRNA as a primer, resulting in the production of a lengthy tandem repeat. Benefits from the palindromic sequences and hairpin-shaped structure in padlock double-stranded structures can be infinitely produced during the RCA reaction and provide numerous binding sites for SYBR Green I, a double-stranded dye, achieving a sharp response signal for label-free target detection. We have demonstrated that the proposed system exhibits a good linear range from 0.1 fM to 5 nM with a low detection limit of 0.1 fM, and the non-target miRNA can be clearly distinguished. The advantages of high efficiency, label-free signaling, and the use of only one oligonucleotide component make the PHP-RCA suitable for ultrasensitive, economic, and convenient detection of target miRNAs. This simple and powerful system is expected to provide a promising platform for tumor diagnosis, prognosis, and therapy.

The microbiome types of colorectal tissue are potentially associated with the prognosis of patients with colorectal cancer

As the second leading cause of cancer worldwide, colorectal cancer (CRC) is associated with a poor prognosis. Although recent studies have explored prognostic markers in patients with CRC, whether tissue microbes carry prognostic information remains unknown. Here, by assessing the colorectal tissue microbes of 533 CRC patients, we found that Proteobacteria (43.5%), Firmicutes (25.3%), and Actinobacteria (23.0%) dominated the colorectal tissue microbiota, which was different from the gut microbiota. Moreover, two clear clusters were obtained by clustering based on the tissue microbes across all samples. By comparison, the relative abundances of Proteobacteria and Bacteroidetes in cluster 1 were significantly higher than those in cluster 2; while compared with cluster 1, Firmicutes and Actinobacteria were more abundant in cluster 2. In addition, the Firmicutes/Bacteroidetes ratios in cluster 1 were significantly lower than those in cluster 2. Further, compared with cluster 2, patients in cluster 1 had relatively poor survival (Log-rank test, p = 0.0067). By correlating tissue microbes with patient survival, we found that the relative abundance of dominant phyla, including Proteobacteria, Firmicutes, and Bacteroidetes, was significantly associated with survival in CRC patients. Besides, the co-occurrence network of tissue microbes at the phylum level of cluster 2 was more complicated than that of cluster 1. Lastly, we detected some pathogenic bacteria enriched in cluster 1 that promote the development of CRC, thus leading to poor survival. In contrast, cluster 2 showed significant increases in the abundance of some probiotics and genera that resist cancer development. Altogether, this study provides the first evidence that the tissue microbiome of CRC patients carries prognostic information and can help design approaches for clinically evaluating the survival of CRC patients.

miRNA-1260b Promotes Breast Cancer Cell Migration and Invasion by Downregulating CCDC134

BACKGROUND

Breast cancer (BRCA) is the most common type of cancer among women worldwide. MiR-1260b has been widely demonstrated to participate in multiple crucial biological functions of cancer tumorigenesis, but its functional effect and mechanism in human breast cancer have not been fully understood.

METHODS

qRT-PCR was used to detect miR-1260b expression in 29 pairs of breast cancer tissues and normal adjacent tissues. Besides, the expression level of miR-1260b in BRCA cells was also further validated by qRT-PCR. miR-1260b played its role in the prognostic process by using Kaplan-Meier curves. In addition, miR-1260b knockdown and target gene CCDC134 overexpression model was constructed in cell line MDA-MB-231. Transwell migration and invasion assay was performed to analyze the effect of miR-1260b and CCDC134 on the biological function of BRCA cells. TargetScan and miRNAWalk were used to find possible target mRNAs. The relationship between CCDC134 and immune cell surface markers was analyzed using TIMER and database and the XIANTAO platform. GSEA analysis was used to identify possible CCDC134-associated molecular mechanisms and pathways.

RESULTS

In the present study, miR-1260b expression was significantly upregulated in human breast cancer tissue and a panel of human breast cancer cell lines, while the secretory protein coiled-coil domain containing 134 (CCDC134) exhibited lower mRNA expression. High expression of miR-1260b was associated with poor overall survival among the patients by KM plot. Knockdown of miR-1260b significantly suppressed breast cancer cell migration and invasion and yielded the opposite result. In addition, overexpression of CCDC134 could inhibit breast cancer migration and invasion, and knockdown yielded the opposite result. There were significant positive correlations of CCDC134 with CD25 (IL2RA), CD80 and CD86. GSEA showed that miR-1260b could function through the MAPK pathway by downregulating CCDC134.

CONCLUSION

Collectively, these results suggested that miR-1260b might be an oncogene of breast cancer and might promote the migration and invasion of BRCA cells by down-regulating its target gene CCDC134 and activating MAPK signaling pathway as well as inhibiting immune function and causing immune escape in human breast cancer.

Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning

MOTIVATION

Tumor mutational burden (TMB) is an indicator of the efficacy and prognosis of immune checkpoint therapy in colorectal cancer (CRC). In general, patients with higher TMB values are more likely to benefit from immunotherapy. Though whole-exome sequencing is considered the gold standard for determining TMB, it is difficult to be applied in clinical practice due to its high cost. There are also a few DNA panel-based methods to estimate TMB; however, their detection cost is also high, and the associated wet-lab experiments usually take days, which emphasize the need for faster and cheaper alternatives.

RESULTS

In this study, we propose a multi-modal deep learning model based on a residual network (ResNet) and multi-modal compact bilinear pooling to predict TMB status (i.e. TMB high (TMB_H) or TMB low(TMB_L)) directly from histopathological images and clinical data. We applied the model to CRC data from The Cancer Genome Atlas and compared it with four other popular methods, namely, ResNet18, ResNet50, VGG19 and AlexNet. We tested different TMB thresholds, namely, percentiles of 10%, 14.3%, 15%, 16.3%, 20%, 30% and 50%, to differentiate TMB_H and TMB_L.For the percentile of 14.3% (i.e. TMB value 20) and ResNet18, our model achieved an area under the receiver operating characteristic curve of 0.817 after 5-fold cross-validation, which was better than that of other compared models. In addition, we also found that TMB values were significantly associated with the tumor stage and N and M stages. Our study shows that deep learning models can predict TMB status from histopathological images and clinical information only, which is worth clinical application.

A deep ensemble learning-based automated detection of COVID-19 using lung CT images and Vision Transformer and ConvNeXt

Since the outbreak of COVID-19, hundreds of millions of people have been infected, causing millions of deaths, and resulting in a heavy impact on the daily life of countless people. Accurately identifying patients and taking timely isolation measures are necessary ways to stop the spread of COVID-19. Besides the nucleic acid test, lung CT image detection is also a path to quickly identify COVID-19 patients. In this context, deep learning technology can help radiologists identify COVID-19 patients from CT images rapidly. In this paper, we propose a deep learning ensemble framework called VitCNX which combines Vision Transformer and ConvNeXt for COVID-19 CT image identification. We compared our proposed model VitCNX with EfficientNetV2, DenseNet, ResNet-50, and Swin-Transformer which are state-of-the-art deep learning models in the field of image classification, and two individual models which we used for the ensemble (Vision Transformer and ConvNeXt) in binary and three-classification experiments. In the binary classification experiment, VitCNX achieves the best recall of 0.9907, accuracy of 0.9821, F1-score of 0.9855, AUC of 0.9985, and AUPR of 0.9991, which outperforms the other six models. Equally, in the three-classification experiment, VitCNX computes the best precision of 0.9668, an accuracy of 0.9696, and an F1-score of 0.9631, further demonstrating its excellent image classification capability. We hope our proposed VitCNX model could contribute to the recognition of COVID-19 patients.

A multi-omics machine learning framework in predicting the recurrence and metastasis of patients with pancreatic adenocarcinoma

Local recurrence and distant metastasis are the main causes of death in patients with pancreatic adenocarcinoma (PDAC). Microbial content in PDAC metastasis is still not well-characterized. Here, the tissue microbiome was comprehensively compared between metastatic and non-metastatic PDAC patients. We found that the pancreatic tissue microbiome of metastatic patients was significantly different from that of non-metastatic patients. Further, 10 potential bacterial biomarkers (Kurthia, Gulbenkiania, Acetobacterium and Planctomyces etc.) were identified by differential analysis. Meanwhile, significant differences in expression patterns across multiple omics (lncRNA, miRNA, and mRNA) of PDAC patients were found. The highest accuracy was achieved when these 10 bacterial biomarkers were used as features to predict recurrence or metastasis in PDAC patients, with an AUC of 0.815. Finally, the recurrence and metastasis in PDAC patients were associated with reduced survival and this association was potentially driven by the 10 biomarkers we identified. Our studies highlight the association between the tissue microbiome and recurrence or metastasis of pancreatic adenocarcioma patients, as well as the survival of patients.

MAGEA11 as a STAD Prognostic Biomarker Associated with Immune Infiltration

Expression of MAGE family member A11 (MAGEA11) is upregulated in different tumors. However, in gastric cancer, the prognostic significance of MAGEA11 and its relationship with immune infiltration remain largely unknown. The expression of MAGEA11 in pan-cancer and the receiver operating characteristic (ROC) and survival impact of gastric cancer were evaluated by The Cancer Genome Atlas (TCGA). Whether MAGEA11 was an independent risk factor was assessed by Cox analysis. Nomograms were constructed from MAGEA11 and clinical variables. Gene functional pathway enrichment was obtained based on MAGEA11 differential analysis. The relationship between MAGEA11 and immune infiltration was determined by the Tumor Immunity Estimation Resource (TIMER) and the Tumor Immune System Interaction Database (TISIDB). Finally, MAGEA11-sensitive drugs were predicted based on the CellMiner database. The results showed that the expression of MAGEA11 mRNA in gastric cancer tissues was significantly higher than that in normal tissues. The ROC curve indicated an AUC value of 0.667. Survival analysis showed that patients with high MAGEA11 had poor prognosis (HR = 1.43, p = 0.034). In correlation analysis, MAGEA11 mRNA expression was found to be associated with tumor purity and immune invasion. Finally, drug sensitivity analysis found that the expression of MAGEA11 was correlated with seven drugs. Our study found that upregulated MAGEA11 in gastric cancer was significantly associated with lower survival and invasion by immune infiltration. It is suggested that MAGEA11 may be a potential biomarker and immunotherapy target for gastric cancer.

Upregulation of TIMM8A is correlated with prognosis and immune regulation in BC

Backgrounds

Breast cancer is a common malignant tumors in women. TIMM8A was up-regulated in different cancers. The aim of this work was to clarify the value of TIMM8A in the diagnosis, prognosis of Breast Cancer (BC), and its association with immune cells and immune detection points. Gene mutations.

Methods

The transcription and expression profile of TIMM8A between BC and normal tissues was downloaded from The Cancer Genome atlas (TCGA). The expression of TIMM8A protein was evaluated by human protein map. The correlation between TIMM8A and clinical features was analyzed using the R package to establish a ROC diagnostic curve. cBioPortal and MethSurv were used to identify gene alterations and DNA methylation and their effects on prognosis. The tumor immune estimation resource (TIMER) database and tumor immune system interaction database (TISIDB) database were used to determine the relationship between TIMM8A gene expression levels and immune infiltration. The CTD database was used to predict related drugs that inhibit TIMM8A, and the PubChem database was used to determine the molecular structure of potentially effective drug small molecules.

Results

The expression of TIMM8A in breast cancer tissues was significantly higher than that in normally adjacent tissues to cancer. ROC curve analysis showed that the AUC value of TIMM8A was 0.679. Kaplan-Meier method showed that patients with high TIMM8A had a lower prognosis (Overall Survival HR = 1.83 (1.31 − 2.54), P < 0.001) than patients with low TIMM8A expression of breast cancer (148.5 months vs. 115.4 months, P < 0.001). Methylation levels at seven CpG were associated with prognosis. Correlation analysis showed that TIMM8A expression was associated with tumor immune cell infiltration. There was a significant positive correlation of TIMM8A with PDL-1, and CTLA-4 in BC. In addition, CTD database analysis identified 15 small molecular drugs that target TIMM8A, such as Cyclosporine, Leflunomide, and Tretinoin, which might be effective therapies for targeted inhibition of TIMM8A.

Conclusion

In breast cancer, up-regulated TIMM 8A was significantly related to lower survival rate and higher immune invasiveness. Our research showed that TIMM 8A could be used as a biomarker for poor prognosis of breast cancer and a potential target of immunotherapy.

A bioinformatics framework to identify the biomarkers and potential drugs for the treatment of colorectal cancer

Colorectal cancer (CRC), a common malignant tumor, is one of the main causes of death in cancer patients in the world. Therefore, it is critical to understand the molecular mechanism of CRC and identify its diagnostic and prognostic biomarkers. The purpose of this study is to reveal the genes involved in the development of CRC and to predict drug candidates that may help treat CRC through bioinformatics analyses. Two independent CRC gene expression datasets including The Cancer Genome Atlas (TCGA) database and GSE104836 were used in this study. Differentially expressed genes (DEGs) were analyzed separately on the two datasets, and intersected for further analyses. 249 drug candidates for CRC were identified according to the intersected DEGs and the Crowd Extracted Expression of Differential Signatures (CREEDS) database. In addition, hub genes were analyzed using Cytoscape according to the DEGs, and survival analysis results showed that one of the hub genes, TIMP1 was related to the prognosis of CRC patients. Thus, we further focused on drugs that could reverse the expression level of TIMP1. Eight potential drugs with documentary evidence and two new drugs that could reverse the expression of TIMP1 were found among the 249 drugs. In conclusion, we successfully identified potential biomarkers for CRC and achieved drug repurposing using bioinformatics methods. Further exploration is needed to understand the molecular mechanisms of these identified genes and drugs/small molecules in the occurrence, development and treatment of CRC.

Identifying potential microRNA biomarkers for colon cancer and colorectal cancer through bound nuclear norm regularization

Colon cancer and colorectal cancer are two common cancer-related deaths worldwide. Identification of potential biomarkers for the two cancers can help us to evaluate their initiation, progression and therapeutic response. In this study, we propose a new microRNA-disease association identification method, BNNRMDA, to discover potential microRNA biomarkers for the two cancers. BNNRMDA better combines disease semantic similarity and Gaussian Association Profile Kernel (GAPK) similarity, microRNA function similarity and GAPK similarity, and the bound nuclear norm regularization model. Compared to other five classical microRNA-disease association identification methods (MIDPE, MIDP, RLSMDA, GRNMF, AND LPLNS), BNNRMDA obtains the highest AUC of 0.9071, demonstrating its strong microRNA-disease association identification performance. BNNRMDA is applied to discover possible microRNA biomarkers for colon cancer and colorectal cancer. The results show that all 73 known microRNAs associated with colon cancer in the HMDD database have the highest association scores with colon cancer and are ranked as top 73. Among 137 known microRNAs associated with colorectal cancer in the HMDD database, 129 microRNAs have the highest association scores with colorectal cancer and are ranked as top 129. In addition, we predict that hsa-miR-103a could be a potential biomarker of colon cancer and hsa-mir-193b and hsa-mir-7days could be potential biomarkers of colorectal cancer.

Identification of novel cuproptosis-related lncRNA signatures to predict the prognosis and immune microenvironment of breast cancer patients

Background

Cuproptosis is a new modality of cell death regulation that is currently considered as a new cancer treatment strategy. Nevertheless, the prognostic predictive value of cuproptosis-related lncRNAs in breast cancer (BC) remains unknown. Using cuproptosis-related lncRNAs, this study aims to predict the immune microenvironment and prognosis of BC patients. and develop new therapeutic strategies that target the disease.

Methods

The Cancer Genome Atlas (TCGA) database provided the RNA-seq data along with the corresponding clinical and prognostic information. Univariate and multivariate Cox regression analyses were performed to acquire lncRNAs associated with cuproptosis to establish predictive features. The Kaplan-Meier method was used to calculate the overall survival rate (OS) in the high-risk and low-risk groups. High risk and low risk gene sets were enriched to explore functional discrepancies among risk teams. The mutation data were analyzed using the "MAFTools" r-package. The ties of predictive characteristics and immune status had been explored by single sample gene set enrichment analysis (ssGSEA). Last, the correlation between predictive features and treatment condition in patients with BC was analyzed. Based on prognostic risk models, we assessed associations between risk subgroups and immune scores and immune checkpoints. In addition, drug responses in at-risk populations were predicted.

Results

We identified a set of 11 Cuproptosis-Related lncRNAs (GORAB-AS1, AC 079922.2, AL 589765.4, AC 005696.4, Cytor, ZNF 197-AS1, AC 002398.1, AL 451085.3, YTH DF 3-AS1, AC 008771.1, LINC 02446), based on which to construct the risk model. In comparison to the high-risk group, the low-risk patients lived longer (p < 0.001). Moreover, cuproptosis-related lncRNA profiles can independently predict prognosis in BC patients. The AUC values for receiver operating characteristics (ROC) of 1-, 3-, and 5-year risk were 0.849, 0.779, and 0.794, respectively. Patients in the high-risk group had lower OS than those in the low-risk group when they were divided into groups based on various clinicopathological variables. The tumor burden mutations (TMB) correlation analysis showed that high TMB had a worse prognosis than low-TMB, and gene mutations were found to be different in high and low TMB groups, such as PIK3CA (36% versus 32%), SYNE1 (4% versus 6%). Gene enrichment analysis indicated that the differential genes were significantly concentrated in immune-related pathways. The predictive traits were significantly correlated with the immune status of BC patients, according to ssGSEA results. Finally, high-risk patients showed high sensitivity in anti-CD276 immunotherapy and conventional chemotherapeutic drugs such as imatinib, lapatinib, and pazopanib.

Conclusion

We successfully constructed of a cuproptosis-related lncRNA signature, which can independently predict the prognosis of BC patients and can be used to estimate OS and clinical treatment outcomes in BRCA patients. It will serve as a foundation for further research into the mechanism of cuproptosis-related lncRNAs in breast cancer, as well as for the development of new markers and therapeutic targets for the disease.

Bacterial biomarkers capable of identifying recurrence or metastasis carry disease severity information for lung cancer

Background

Local recurrence and distant metastasis are the main causes of death in patients with lung cancer. Multiple studies have described the recurrence or metastasis of lung cancer at the genetic level. However, association between the microbiome of lung cancer tissue and recurrence or metastasis remains to be discovered. Here, we aimed to identify the bacterial biomarkers capable of distinguishing patients with lung cancer from recurrence or metastasis, and how it related to the severity of patients with lung cancer.

Methods

We applied microbiome pipeline to bacterial communities of 134 non-recurrence and non-metastasis (non-RM) and 174 recurrence or metastasis (RM) samples downloaded from The Cancer Genome Atlas (TCGA). Co-occurrence network was built to explore the bacterial interactions in lung cancer tissue of RM and non-RM. Finally, the Kaplan–Meier survival analysis was used to evaluate the association between bacterial biomarkers and patient survival.

Results

Compared with non-RM, the bacterial community of RM had lower richness and higher Bray–Curtis dissimilarity index. Interestingly, the co-occurrence network of non-RM was more complex than RM. The top 500 genera in relative abundance obtained an area under the curve (AUC) of 0.72 when discriminating between RM and non-RM. There were significant differences in the relative abundances of Acidovorax, Clostridioides, Succinimonas, and Shewanella, and so on between RM and non-RM. These biomarkers played a role in predicting the survival of lung cancer patients and were significantly associated with lung cancer stage.

Conclusion

This study provides the first evidence for the prediction of lung cancer recurrence or metastasis by bacteria in lung cancer tissue. Our results highlights that bacterial biomarkers that distinguish RM and non-RM are also associated with patient survival and disease severity.

A Prognostic Ferroptosis-Related lncRNA Model Associated With Immune Infiltration in Colon Cancer

Colon cancer (CC) is a common malignant tumor worldwide, and ferroptosis plays a vital role in the pathology and progression of CC. Effective prognostic tools are required to guide clinical decision-making in CC. In our study, gene expression and clinical data of CC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We identified the differentially expressed ferroptosis-related lncRNAs using the differential expression and gene co-expression analysis. Then, univariate and multivariate Cox regression analyses were used to identify the effective ferroptosis-related lncRNAs for constructing the prognostic model for CC. Gene set enrichment analysis (GSEA) was conducted to explore the functional enrichment analysis. CIBERSORT and single-sample GSEA were performed to investigate the association between our model and the immune microenvironment. Finally, three ferroptosis-related lncRNAs (XXbac-B476C20.9, TP73-AS1, and SNHG15) were identified to construct the prognostic model. The results of the validation showed that our model was effective in predicting the prognosis of CC patients, which also was an independent prognostic factor for CC. The GSEA analysis showed that several ferroptosis-related pathways were significantly enriched in the low-risk group. Immune infiltration analysis suggested that the level of immune cell infiltration was significantly higher in the high-risk group than that in the low-risk group. In summary, we established a prognostic model based on the ferroptosis-related lncRNAs, which could provide clinical guidance for future laboratory and clinical research on CC.

Predicting recurrence and metastasis risk of endometrial carcinoma via prognostic signatures identified from multi-omics data

Objectives

Endometrial carcinoma (EC) is one of the three major gynecological malignancies, in which 15% - 20% patients will have recurrence and metastasis. Though there are many studies on the prognosis on this cancer, the performances of existing models evaluating the risk of its recurrence and metastasis are yet to be improved. In addition, a comprehensive multi-omics analyses on the prognostic signatures of EC are on demand. In this study, we aimed to construct a relatively stable and reliable model for predicting recurrence and metastasis of EC. This will help determine the risk level of patients and choose appropriate adjuvant therapy, thereby avoiding improper treatment, and improving the prognosis of patients.

Methods

The mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), copy number variation (CNV) data and clinical information of patients with EC were downloaded from The Cancer Genome Atlas (TCGA). Differential expression analyses were performed between the recurrence or metastasis group and the non-recurrence/metastasis group. Then, we screened potential prognostic markers from the four kinds of omics data respectively and established prediction models using three classifiers.

Results

We achieved differential expressed mRNAs, lncRNAs, miRNAs and CNVs between the two groups. According to feature selection scores by the random forest algorithm, 275 CNV features, 50 lncRNA features, 150 miRNA features and 150 mRNA features were selected, respectively. And the prediction model constructed by the features of lncRNA data using random forest method showed the best performance, with an area under the curve of 0.763, and an accuracy of 0.819 under 10-fold cross-validation.

Conclusion

We developed a computational model using omics information, which is able to predicting recurrence and metastasis risk of EC accurately.

Prognostic and immunological role of cuproptosis-related protein FDX1 in pan-cancer

Background: Cancer is the second cause of death worldwide. Copperoptosis is a new mode of regulated cell death and is strongly associated with metabolic pathways. FDX1 is a key gene that promotes copperoptosis, and its impact on tumor pathogenesis and tumor immune response is indistinct and needs further exploration.

Methods: Data was mined from the Cancer Genome Atlas database, the Broad Institute Cancer Cell Line Encyclopedia database, and the International Cancer Genome Consortium. Survival analyses included the Kaplan–Meier method for calculating the cumulative incidence of survival events and the log-rank method for comparing survival curves between groups. Immune cell infiltration levels were calculated using the Spearman correlation test and correlated with FDX1 expression to assess significance. More correlation analyses between FDX1 expression and mutational markers, such as tumor mutational burden (TMB) and microsatellite instability (MSI), were also examined via Spearman assay to explore the relation between FDX1 expression and the sensitivity of common antitumor drugs.

Results: FDX1 expression was downregulated in most kinds of cancers, and this high expression indicated better overall survival and death-specific survival. For several cancer types, FDX1 expression had a positive correlation with immune cell infiltration, and FDX1 also had a positive correlation with TMB and MSI in some cancer types, linking its expression to the assessment of possible treatment responses.

Conclusion: The correlations between FDX1 expression and cancer in varioustissues, including clear links to cancer survival and prognosis, make FDX1 aninteresting biomarker and potential therapeutic target for cancer surveillance and futureresearch.

Identifying cancer tissue-of-origin by a novel machine learning method based on expression quantitative trait loci

Cancer of unknown primary (CUP) refers to cancer with primary lesion unidentifiable by regular pathological and clinical diagnostic methods. This kind of cancer is extremely difficult to treat, and patients with CUP usually have a very short survival time. Recent studies have suggested that cancer treatment targeting primary lesion will significantly improve the survival of CUP patients. Thus, it is critical to develop accurate yet fast methods to infer the tissue-of-origin (TOO) of CUP. In the past years, there are a few computational methods to infer TOO based on single omics data like gene expression, methylation, somatic mutation, and so on. However, the metastasis of tumor involves the interaction of multiple levels of biological molecules. In this study, we developed a novel computational method to predict TOO of CUP patients by explicitly integrating expression quantitative trait loci (eQTL) into an XGBoost classification model. We trained our model with The Cancer Genome Atlas (TCGA) data involving over 7,000 samples across 20 types of solid tumors. In the 10-fold cross-validation, the prediction accuracy of the model with eQTL was over 0.96, better than that without eQTL. In addition, we also tested our model in an independent data downloaded from Gene Expression Omnibus (GEO) consisting of 87 samples across 4 cancer types. The model also achieved an f1-score of 0.7-1 depending on different cancer types. In summary, eQTL was an important information in inferring cancer TOO and the model might be applied in clinical routine test for CUP patients in the future.

Molecular classification grade 3 endometrial endometrioid carcinoma using a next-generation sequencing–based gene panel

Over the past two decades, the incidence of endometrial cancer (EC) is increasing, and there is a need for molecular biomarkers to predict prognosis and guide treatment. A recent study from The Cancer Genome Atlas suggested to implement the EC analysis by molecular profile for improving diagnosis, prognosis, and therapeutic treatment. In this study, next-generation sequencing was performed on 70 cases of G3 endometrioid ECs (EECs) using an 11-gene panel (TP53, MLH1, MSH2, MSH6, PMS2, EPCAM, PIK3CA, CTNNB1, KRAS, PTEN, and POL) for molecular classification. The molecular classification based on the 11-gene NGS panel identified four molecular subgroups: POLE-ultramutated (n = 20, 28.6%), MSI-H (n = 27, 38.6%), NSMP (n = 13, 18.6%) and TP53mut (n = 10, 14.3%). The NGS method showed 98.6% (69 of 70 cases, kappa value 98%) in concordance with the cases assessed by immunohistochemistry (IHC). Among the seven dead cases, four were MSI-H tumors, two were TP53mut/p53abn tumors, and one was NSMP tumors with an average overall survival (OS) of 14.7 months. TP53mut subgroup showed that poor OS rates and POLE group have favorable prognosis. Our work suggested that the 11-gene panel is suitable for molecular classification in G3 EECs and for guiding prognosis and treatment decisions.

Discussion of tumor mutation burden as an indicator to predict efficacy of immune checkpoint inhibitors: A case report

There are many treatment options for advanced lung cancer, among which immunotherapy has developed rapidly and benefited a lot of patients. However, immunotherapy can only benefit a subgroup of patients, and how to select patients suitable for this therapy is critical. Tumor mutation burden (TMB) is one of the important reference indicators for immune checkpoint inhibitors (ICIs). However, there are many factors influencing the usage of this indicator, which will lead to considerable consequences if not treated well. In this study, we performed a case study on a male advanced lung squamous cell carcinoma patient of age 83. The patient suffered from “cough and sputum”, and did chest CT scans on 24 October 2018, which showed “a mass-like mass in the anterior segment of the right lung upper lobe, about 38mm×28mm”. He was treated with systemic chemotherapy; however, the tumor was still under progression. Although PD-L1 was not tested in gene testing, he had a TMB value of 10.26 mutations/Mb with a quantile value 88.63%. Thus, “toripalimab injection” was added as immunotherapy and the size of the lesion decreased. In summary, we adopted a clinical case as the basis to explore the value and significance of TMB in immunotherapy in this study. We hope that more predictive molecular markers will be discovered, which will bring more treatment methods for advanced lung cancer.

Finding Lung-Cancer-Related lncRNAs Based on Laplacian Regularized Least Squares With Unbalanced Bi-Random Walk

Lung cancer is one of the leading causes of cancer-related deaths. Thus, it is important to find its biomarkers. Furthermore, there is an increasing number of studies reporting that long noncoding RNAs (lncRNAs) demonstrate dense linkages with multiple human complex diseases. Inferring new lncRNA-disease associations help to identify potential biomarkers for lung cancer and further understand its pathogenesis, design new drugs, and formulate individualized therapeutic options for lung cancer patients. This study developed a computational method (LDA-RLSURW) by integrating Laplacian regularized least squares and unbalanced bi-random walk to discover possible lncRNA biomarkers for lung cancer. First, the lncRNA and disease similarities were computed. Second, unbalanced bi-random walk was, respectively, applied to the lncRNA and disease networks to score associations between diseases and lncRNAs. Third, Laplacian regularized least squares were further used to compute the association probability between each lncRNA-disease pair based on the computed random walk scores. LDA-RLSURW was compared using 10 classical LDA prediction methods, and the best AUC value of 0.9027 on the lncRNADisease database was obtained. We found the top 30 lncRNAs associated with lung cancers and inferred that lncRNAs TUG1, PTENP1, and UCA1 may be biomarkers of lung neoplasms, non-small–cell lung cancer, and LUAD, respectively.

A Novel Nomogram Model to Predict the Recurrence-Free Survival and Overall Survival of Hepatocellular Carcinoma

Background

Treatments for patients with early‐stage hepatocellular carcinoma (HCC) include liver transplantation (LT), liver resection (LR), radiofrequency ablation (RFA), and microwave ablation (MWA), are critical for their long-term survival. However, a computational model predicting treatment-independent prognosis of patients with HCC, such as overall survival (OS) and recurrence-free survival (RFS), is yet to be developed, to our best knowledge. The goal of this study is to identify prognostic factors associated with OS and RFS in patients with HCC and develop nomograms to predict them, respectively.

Methods

We retrospectively retrieved 730 patients with HCC from three hospitals in China and followed them up for 3 and 5 years after invasive treatment. All enrolled patients were randomly divided into the training cohort and the validation cohort with a 7:3 ratio, respectively. Independent prognostic factors associated with OS and RFS were determined by the multivariate Cox regression analysis. Two nomogram prognostic models were built and evaluated by concordance index (C-index), calibration curves, area under the receiver operating characteristics (ROC) curve, time-dependent area under the ROC curve (AUC), the Kaplan–Meier survival curve, and decision curve analyses (DCAs), respectively.

Results

Prognostic factors for OS and RFS were identified, and nomograms were successfully built. Calibration discrimination was good for both the OS and RFS nomogram prediction models (C-index: 0.750 and 0.746, respectively). For both nomograms, the AUC demonstrated outstanding predictive performance; the DCA shows that the model has good decision ability; and the calibration curve demonstrated strong predictive power. The nomograms successfully discriminated high-risk and low-risk patients with HCC associated with OS and RFS.

Conclusions

We developed nomogram survival prediction models to predict the prognosis of HCC after invasive treatment with acceptable accuracies in both training and independent testing cohorts. The models may have clinical values in guiding the selection of clinical treatment strategies.

Lung Cancer Stage Prediction Using Multi-Omics Data

Lung cancer is one of the leading causes of cancer death. Patients with early-stage lung cancer can be treated by surgery, while patients in the middle and late stages need chemotherapy or radiotherapy. Therefore, accurate staging of lung cancer is crucial for doctors to formulate accurate treatment plans for patients. In this paper, the random forest algorithm is used as the lung cancer stage prediction model, and the accuracy of lung cancer stage prediction is discussed in the microbiome, transcriptome, microbe, and transcriptome fusion groups, and the accuracy of the model is measured by indicators such as ACC, recall, and precision. The results showed that the prediction accuracy of microbial combinatorial transcriptome fusion analysis was the highest, reaching 0.809. The study reveals the role of multimodal data and fusion algorithm in accurately diagnosing lung cancer stage, which could aid doctors in clinics.