Cancer is one of the most frequently diagnosed malignant diseases worldwide, posing a serious, long-term threat to patients’ health and life. Systemic chemotherapy remains the first-line therapeutic approach for recurrent or metastatic cancer patients after surgery, with the potential to effectively extend patient survival. However, the development of drug resistance seriously limits the clinical efficiency of chemotherapy and ultimately results in treatment failure and patient death. A large number of studies have shown that non-coding RNAs (ncRNAs), particularly microRNAs, long non-coding RNAs, and circular RNAs, are widely involved in the regulation of cancer drug resistance. Their dysregulation contributes to the development of cancer drug resistance by modulating the expression of specific target genes involved in cellular apoptosis, autophagy, drug efflux, epithelial-to-mesenchymal transition (EMT), and cancer stem cells (CSCs). Moreover, some ncRNAs also possess great potential as efficient, specific biomarkers in diagnosis and prognosis as well as therapeutic targets in cancer patients. In this review, we summarize the recent findings on the emerging role and underlying mechanisms of ncRNAs involved in cancer drug resistance and focus on their clinical applications as biomarkers and therapeutic targets in cancer treatment. This information will be of great benefit to early diagnosis and prognostic assessments of cancer as well as the development of ncRNA-based therapeutic strategies for cancer patients.

https://www.frontiersin.org/articles/10.3389/fonc.2022.951864/pdf

Non-coding RNA in cancer drug resistance: Underlying mechanisms and clinical applications

Ovarian cancer (OC) is one of the most common malignancies that causes death in women and is a heterogeneous disease with complex molecular and genetic changes. Because of the relatively high recurrence rate of OC, it is crucial to understand the associated mechanisms of drug resistance and to discover potential target for rational targeted therapy. Cell death is a genetically determined process. Active and orderly cell death is prevalent during the development of living organisms and plays a critical role in regulating life homeostasis. Ferroptosis, a novel type of cell death discovered in recent years, is distinct from apoptosis and necrosis and is mainly caused by the imbalance between the production and degradation of intracellular lipid reactive oxygen species triggered by increased iron content. Necroptosis is a regulated non-cysteine protease–dependent programmed cell necrosis, morphologically exhibiting the same features as necrosis and occurring via a unique mechanism of programmed cell death different from the apoptotic signaling pathway. Pyroptosis is a form of programmed cell death that is characterized by the formation of membrane pores and subsequent cell lysis as well as release of pro-inflammatory cell contents mediated by the abscisin family. Studies have shown that ferroptosis, necroptosis, and pyroptosis are involved in the development and progression of a variety of diseases, including tumors. In this review, we summarized the recent advances in ferroptosis, necroptosis, and pyroptosis in the occurrence, development, and therapeutic potential of OC.

/pdf/ferroptosis-necroptosis-and-pyroptosis-in-the-occurrence-and-2q1oukwh.pdf

Ferroptosis, necroptosis, and pyroptosis in the occurrence and development of ovarian cancer

Underlying mechanisms of epithelial splicing regulatory proteins in cancer progression

Abstract Cancer is a leading disease-related cause of death worldwide. Despite advances in therapeutic interventions, cancer remains a major global public health problem. Cancer pathogenesis is extremely intricate and largely unknown. Fas-associated protein with death domain (FADD) was initially identified as an adaptor protein for death receptor-mediated extrinsic apoptosis. Recent evidence suggests that FADD plays a vital role in non-apoptotic cellular processes, such as proliferation, autophagy, and necroptosis. FADD expression and activity of are modulated by a complicated network of processes, such as DNA methylation, non-coding RNA, and post-translational modification. FADD dysregulation has been shown to be closely associated with the pathogenesis of numerous types of cancer. However, the detailed mechanisms of FADD dysregulation involved in cancer progression are still not fully understood. This review mainly summarizes recent findings on the structure, functions, and regulatory mechanisms of FADD and focuses on its role in cancer progression. The clinical implications of FADD as a biomarker and therapeutic target for cancer patients are also discussed. The information reviewed herein may expand researchers’ understanding of FADD and contribute to the development of FADD-based therapeutic strategies for cancer patients. 

https://molmed.biomedcentral.com/counter/pdf/10.1186/s10020-022-00560-y

FADD as a key molecular player in cancer progression

Ferroptosis is a type of regulated cell death catalyzed by the iron-dependent accumulation of lipid hydroperoxides to lethal levels. Chronic lymphocytic leukemia (CLL) is a chronic lymphoproliferative disorder. However, the understanding of ferroptosis in CLL remains largely poor. In this study, we investigated the stratification and prognostic role of ferroptosis-related genes in CLL patients of ICGC cohort. We obtained fourteen genes with prognostic value by screening 110 ferroptosis-related genes (FRGs). Based on the expression profiles of these 14 genes, we classified CLL patients into two clusters. Most of the FRGs were highly expressed in cluster 1, and cluster 1 was associated with better overall survival (OS). Subsequently, we developed an eight-gene signature (TP63, STEAP3, NQO1, ELAVL1, PRKAA1, HELLS, FANCD2, and CDKN2A) by using LASSO analysis. This risk signature divided CLL patients into high- and low-risk groups. We used Cox regression analysis and ROC analysis demonstrated the risk signature was reliable and robust. And we validated the risk model in an external cohort (GSE22762). We also conducted enrichment analysis and genomic mutation analysis. Finally, we explored the potential effect of chemotherapy between the two risk groups. Our study contributed to understanding the role of ferroptosis in CLL and facilitated personalized and precision treatment.

A Ferroptosis Molecular Subtype-Related Signature for Predicting Prognosis and Response to Chemotherapy in Patients with Chronic Lymphocytic Leukemia

Ferroptosis is a novel form of programmed cell death, and it is characterized by iron-dependent oxidative damage, lipid peroxidation and reactive oxygen species accumulation. Notable studies have revealed that ferroptosis plays vital roles in tumor occurrence and that abundant ferroptosis in cells can inhibit tumor progression. Recently, some noncoding RNAs (ncRNAs), particularly microRNAs, long noncoding RNAs, and circular RNAs, have been shown to be involved in biological processes of ferroptosis, thus affecting cancer growth. However, the definite regulatory mechanism of this phenomenon is still unclear. To clarify this issue, increasing studies have focused on the regulatory roles of ncRNAs in the initiation and development of ferroptosis and the role of ferroptosis in progression of various cancers, such as lung, liver, and breast cancers. In this review, we systematically summarized the relationship between ferroptosis-associated ncRNAs and cancer progression. Moreover, additional evidence is needed to identify the role of ferroptosis-related ncRNAs in cancer progression. This review will help us to understand the roles of ncRNAs in ferroptosis and cancer progression and may provide new ideas for exploring novel diagnostic and therapeutic biomarkers for cancer in the future.

/pdf/ferroptosis-in-cancer-progression-role-of-noncoding-rnas-2q6v8qto.pdf

Ferroptosis in Cancer Progression: Role of Noncoding RNAs

The tumor-infiltrating lymphocytes (TILs) and its correlation with tumors play a critical role in the development and progression of breast cancer. Existing studies have demonstrated that the combination of the whole-slide pathological images (WSIs) and genomic data can better characterize the immunological mechanisms of TILs and assess the prognostic outcome in breast cancer. However, it is still very challenging to characterize the intersections between TILs and tumors in WSIs because of their large size and heterogeneity patterns, and the high dimensional genomic data also brings difficulty for the integrative analysis with WSIs data. To address the above challenges, in this paper, we propose an interpretable multi-modal fusion framework, IMGFN, that can fuse the interaction information between TILs and tumors with the genomic data via an attention mechanism for prognosis predictions of breast cancer. Specifically, for WSIs data, we use the graph attention network (i.e., GAT) to describe the spatial interactions of TILs and tumor regions across WSIs. As to genomic data, we use co-expression network analysis algorithms to cluster genes into co-expressed modules followed by applying the Concrete Autoencoders to select survival-associated modules. Finally, a self-attention layer is adopted to combine both the imaging and genomic features for the prognosis prediction of breast cancer. The experimental results on The Cancer Genome Atlas(TCGA) dataset suggest that the proposed IMGFN can not only achieve better prognosis results than the comparing methods but also identify consistent survival-associated imaging and genomic biomarkers correlated strongly with the interaction between TILs and tumors. 

Identify Consistent Imaging Genomic Biomarkers for Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors

The tumor-infiltrating lymphocytes (TILs) and its correlation with tumors have shown significant values in the development of cancers. Many observations indicated that the combination of the whole-slide pathological images (WSIs) and genomic data can better characterize the immunological mechanisms of TILs. However, the existing image-genomic studies evaluated the TILs by the combination of pathological image and single-type of omics data (e.g., mRNA), which is difficulty in assessing the underlying molecular processes of TILs holistically. Additionally, it is still very challenging to characterize the intersections between TILs and tumor regions in WSIs and the high dimensional genomic data also brings difficulty for the integrative analysis with WSIs. Based on the above considerations, we proposed an end-to-end deep learning framework i.e., IMO-TILs that can integrate pathological image with multi-omics data (i.e., mRNA and miRNA) to analyze TILs and explore the survival-associated interactions between TILs and tumors. Specifically, we firstly apply the graph attention network to describe the spatial interactions between TILs and tumor regions in WSIs. As to genomic data, the Concrete AutoEncoder (i.e., CAE) is adopted to select survival-associated Eigengenes from the high-dimensional multi-omics data. Finally, the deep generalized canonical correlation analysis (DGCCA) accompanied with the attention layer is implemented to fuse the image and multi-omics data for prognosis prediction of human cancers. The experimental results on three cancer cohorts derived from the Cancer Genome Atlas (TCGA) indicated that our method can both achieve higher prognosis results and identify consistent imaging and multi-omics bio-markers correlated strongly with the prognosis of human cancers.

Characterizing the Survival-Associated Interactions between Tumor-infiltrating Lymphocytes and Tumors from Pathological Images and Multi-omics Data.

Lung cancer is one of the most widely spread cancers in the world. So far, the histopathological image remains the “gold standard” in diagnosing lung cancers, and multiple types of pathological images features have been associated with lung cancer diagnosis and progression. However, most of the existing studies only utilized single type of image features, which did not take advantages of multiple types of image features. In this paper, we propose a Block based Multi-View Graph Convolutional Network (i.e., BMVGCN), which integrates multiple types of image features from histopathological images for lung cancer diagnosis. Specifically, our method utilizes the block-based bilinear combination model to fuse different types of features. By considering the correlation among different samples, we also introduce the Graph Convolutional Network to exploit the correlations among samples that could lead to better diagnosis performance. To evaluate the effectiveness of the proposed method, we conduct the experiments for the classification of the cancer tissue and non-cancer tissue in both Lung Adenocarcinoma (i.e., LUAD) and Lung Squamous Cell Carcinoma (i.e.,LUSC), and the discrimination between LUAD and LUSC. The results show that our method can achieve superior classification performance than the comparing methods. 

Integrative Analysis of Multi-view Histopathological Image Features for the Diagnosis of Lung Cancer

Survival analysis is to estimate the survival time for an individual or a group of patients, which is a valid solution for cancer treatments. Recent studies suggested that the integrative analysis of histopathological images and genomic data can better predict the survival of cancer patients than simply using single bio-marker, for different bio-markers may provide complementary information. However, for the given multi-modal data that may contain irrelevant or redundant features, it is still challenge to design a distance metric that can simultaneously discover significant features and measure the difference of survival time among different patients. To solve this issue, we propose a Feature-Aware Multi-modal Metric Learning method (FAM3L), which not only learns the metric for distance constraints on patients' survival time, but also identifies important images and genomic features for survival analysis. Specifically, for each modality of data, we firstly design one feature-aware metric that can be decoupled into a traditional distance metric and a diagonal weight for important feature identification. Then, in order to explore the complex correlation across multiple modality data, we apply Hilbert-Schmidt Independence Criterion (HSIC) to jointly learn multiple metrics. Finally, based on the learned distance metrics, we apply the Cox proportional hazards model for prognosis prediction. We evaluate the performance of our proposed FAM3L method on three cancer cohorts derived from The Cancer Genome Atlas (TCGA), the experimental results demonstrate that our method can not only achieve superior performance for cancer prognosis, but also identify meaningful image and genomic features correlating strongly with cancer survival.

Ying Zuo

Papers

Ferroptosis in Cancer Progression: Role of Noncoding RNAs

Identify Consistent Imaging Genomic Biomarkers for Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors

Characterizing the Survival-Associated Interactions between Tumor-infiltrating Lymphocytes and Tumors from Pathological Images and Multi-omics Data.

Integrative Analysis of Multi-view Histopathological Image Features for the Diagnosis of Lung Cancer

FAM3L:Feature-Aware Multi-modal Metric Learning for Integrative Survival Analysis of Human Cancers.