Landscape of cell heterogeneity and evolutionary trajectory in ulcerative colitis-associated colon cancer revealed by single-cell RNA sequencing

Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine

Colorectal cancer (CRC) is one of the most heterogeneous and deadly diseases, with a global incidence of 1.5 million cases per year. Genomics has revolutionized the clinical management of CRC by enabling comprehensive molecular profiling of cancer. However, a deeper understanding of the molecular factors is needed to identify new prognostic and predictive markers that can assist in designing more effective therapeutic regimens for the improved management of CRC. Recent breakthroughs in single-cell analysis have identified new cell subtypes that play a critical role in tumor progression and could serve as potential therapeutic targets. Spatial analysis of the transcriptome and proteome holds the key to unlocking pathogenic cellular interactions, while liquid biopsy profiling of molecular variables from serum holds great potential for monitoring therapy resistance. Furthermore, gene expression signatures from various pathways have emerged as promising prognostic indicators in colorectal cancer and have the potential to enhance the development of equitable medicine. The advancement of these technologies for identifying new markers, particularly in the domain of predictive and personalized medicine, has the potential to improve the management of patients with CRC. Further investigations utilizing similar methods could uncover molecular subtypes specific to emerging therapies, potentially strengthening the development of personalized medicine for CRC patients.

Comprehensive analysis reveals key genes and environmental toxin exposures underlying treatment response in ulcerative colitis based on in-silico analysis and Mendelian randomization

BACKGROUND

UC is increasingly prevalent worldwide and represents a significant global disease burden. Although medical therapeutics are employed, they often fall short of being optimal, leaving patients struggling with treatment non-responsiveness and many related complications.

MATERIALS AND METHODS

The study utilized gene microarray data and clinical information from GEO. Gene enrichment and differential expression analyses were conducted using Metascape and Limma, respectively. Lasso Regression Algorithm was constructed using glmnet and heat maps were generated using pheatmap. ROC curves were used to assess diagnostic parameter capability, while XSum was employed to screen for small-molecule drugs exacerbating UC. Molecular docking was carried out using Autodock Vina. The study also performed Mendelian randomization analysis based on TwoSampleMR and used CTD to investigate the relationship between exposure to environmental chemical toxicants and UC therapy responsiveness.

RESULTS

Six genes (ELL2, DAPP1, SAMD9L, CD38, IGSF6, and LYN) were found to be significantly overexpressed in UC patient samples that did not respond to multiple therapies. Lasso analysis identified ELL2 and DAPP1 as key genes influencing UC treatment response. Both genes accurately predicted intestinal inflammation in UC and impacted the immunological infiltration status. Clofibrate showed therapeutic potential for UC by binding to ELL2 and DAPP1 proteins. The study also reviews environmental toxins and drug exposures that could impact UC progression.

CONCLUSIONS

We used microarray technology to identify DAPP1 and ELL2 as key genes that impact UC treatment response and inflammatory progression. Clofibrate was identified as a promising UC treatment. Our review also highlights the impact of environmental toxins on UC treatment response, providing valuable insights for personalized clinical management.

Circular RNA: A promising new star of vaccine

Circular RNAs (circRNAs) are a class of single-stranded RNAs with covalently closed structures. Owing to their not having 3' or 5' ends, circRNAs are highly durable and insusceptible to exonuclease-mediated degradation. Moreover, some circRNAs with certain structures are translatable, making them novel vaccines. Vaccines are efficient tools for immunotherapy, such as for the prevention of infectious diseases and cancer treatment. The immune system is activated during immunotherapy to fight against abnormal allies or invaders. CircRNA vaccines represent a potential new avenue in the vaccine era. Recently, several circRNA vaccines have been synthesized and tested in vitro and in vivo. Our review briefly introduces the current understanding of the biology and function of translatable circRNAs, molecular biology, synthetic methods, delivery of circRNA, and current circRNA vaccines. We also discussed the challenges and future directions in the field by summarizing the developments in circRNA vaccines in the past few years.

Computational single cell oncology: state of the art

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

Colitis-associated carcinogenesis: crosstalk between tumors, immune cells and gut microbiota

Colorectal cancer (CRC) is the third most common cancer worldwide. One of the main causes of colorectal cancer is inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn's disease (CD). Intestinal epithelial cells (IECs), intestinal mesenchymal cells (IMCs), immune cells, and gut microbiota construct the main body of the colon and maintain colon homeostasis. In the development of colitis and colitis-associated carcinogenesis, the damage, disorder or excessive recruitment of different cells such as IECs, IMCs, immune cells and intestinal microbiota play different roles during these processes. This review aims to discuss the various roles of different cells and the crosstalk of these cells in transforming intestinal inflammation to cancer, which provides new therapeutic methods for chemotherapy, targeted therapy, immunotherapy and microbial therapy.

Prognostic Biomarkers of Cell Proliferation in Colorectal Cancer (CRC): From Immunohistochemistry to Molecular Biology Techniques

Colorectal cancer (CRC) is one of the most common and severe malignancies worldwide. Recent advances in diagnostic methods allow for more accurate identification and detection of several molecular biomarkers associated with this cancer. Nonetheless, non-invasive and effective prognostic and predictive testing in CRC patients remains challenging. Classical prognostic genetic markers comprise mutations in several genes (e.g., APC, KRAS/BRAF, TGF-β, and TP53). Furthermore, CIN and MSI serve as chromosomal markers, while epigenetic markers include CIMP and many other candidates such as SERP, p14, p16, LINE-1, and RASSF1A. The number of proliferation-related long non-coding RNAs (e.g., SNHG1, SNHG6, MALAT-1, CRNDE) and microRNAs (e.g., miR-20a, miR-21, miR-143, miR-145, miR-181a/b) that could serve as potential CRC markers has also steadily increased in recent years. Among the immunohistochemical (IHC) proliferative markers, the prognostic value regarding the patients' overall survival (OS) or disease-free survival (DFS) has been confirmed for thymidylate synthase (TS), cyclin B1, cyclin D1, proliferating cell nuclear antigen (PCNA), and Ki-67. In most cases, the overexpression of these markers in tissues was related to worse OS and DFS. However, slowly proliferating cells should also be considered in CRC therapy (especially radiotherapy) as they could represent a reservoir from which cells are recruited to replenish the rapidly proliferating population in response to cell-damaging factors. Considering the above, the aim of this article is to review the most common proliferative markers assessed using various methods including IHC and selected molecular biology techniques (e.g., qRT-PCR, in situ hybridization, RNA/DNA sequencing, next-generation sequencing) as prognostic and predictive markers in CRC.

Identification of hepatocellular carcinoma-related subtypes and development of a prognostic model: a study based on ferritinophagy-related genes

BACKGROUND

Hepatocellular carcinoma still has a high incidence and mortality rate worldwide, and further research is needed to investigate its occurrence and development mechanisms in depth in order to identify new therapeutic targets. Ferritinophagy is a type of autophagy and a key factor in ferroptosis that could influence tumor onset and progression. Although, the potential role of ferritinophagy-related genes (FRGs) in liver hepatocellular carcinoma (LIHC) is unknown.

METHODS

Single-cell RNA sequencing (scRNA-seq) data of LIHC were obtained from the Gene Expression Omnibus (GEO) dataset. In addition, transcriptome and clinical follow-up outcome data of individuals with LIHC were extracted from the The Cancer Genome Atlas (TCGA) dataset. FRGs were collected through the GeneCards database. Differential cell subpopulations were distinguished, and differentially expressed FRGs (DEFRGs) were obtained. Differential expression of FRGs and prognosis were observed according to the TCGA database. An FRG-related risk model was constructed to predict patient prognosis by absolute shrinkage and selection operator (LASSO) and COX regression analyses, and its prognosis predictive power was validated. Ultimately, the association between risk score and tumor microenvironment (TME), immune cell infiltration, immune checkpoints, drug sensitivity, and tumor mutation burden (TMB) was analyzed. We also used quantitative reverse transcription polymerase chain reaction (qRT-PCR) to validate the expression of key genes in normal liver cells and liver cancer cells.

RESULTS

We ultimately identified 8 cell types, and 7 differentially expressed FRGs genes (ZFP36, NCOA4, FTH1, FTL, TNF, PCBP1, CYB561A3) were found among immune cells, and we found that Monocytes and Macrophages were closely related to FRGs genes. Subsequently, COX regression analysis showed that patients with high expression of FTH1, FTL, and PCBP1 had significantly worse prognosis than those with low expression, and our survival prediction model, constructed based on age, stage, and risk score, showed better prognostic prediction ability. Our risk model based on 3 FRGs genes ultimately revealed significant differences between high-risk and low-risk groups in terms of immune infiltration and immune checkpoint correlation, drug sensitivity, and somatic mutation risk. Finally, we validated the key prognostic genes FTH1, FTL, using qRT-PCR, and found that the expression of FTH1 and FTL was significantly higher in various liver cancer cells than in normal liver cells. At the same time, immunohistochemistry showed that the expression of FTH1, FTL in tumor tissues was significantly higher than that in para-tumor tissues.

CONCLUSION

This study identifies a considerable impact of FRGs on immunity and prognosis in individuals with LIHC. The collective findings of this research provide new ideas for personalized treatment of LIHC and a more targeted therapy approach for individuals with LIHC to improve their prognosis.

Single-cell analysis reveals cellular reprogramming in advanced colon cancer following FOLFOX-bevacizumab treatment

Introduction

The combination of FOLFOX and bevacizumab (FOLFOX-Bev) is a promising treatment for advanced colorectal cancer (CRC). However, the response of the tumor microenvironment to FOLFOX-Bev is still largely unexplored.

Methods

We conducted single-cell transcriptomic analysis of CRC samples derived from a patient before and after treatment to gain insights into the cellular changes associated with FOLFOX-Bev treatment.

Results

We found that cancer cells with high proliferative, metastatic, and pro-angiogenic properties respond better to FOLFOX-Bev treatment. Moreover, FOLFOX-Bev enhances CD8+ T cell cytotoxicity, thereby boosting the anti-tumor immune response. Conversely, FOLFOX-Bev impairs the functionality of tumor-associated macrophages, plasma cells, and cancer-associated fibroblasts, leading to a decrease in VEGFB-mediated angiogenesis. Furthermore, FOLFOX-Bev treatment reset intercellular communication, which could potentially affect the function of non-cancer cells.

Discussion

Our findings provide valuable insights into the molecular mechanisms underlying the response of advanced CRC to FOLFOX-Bev treatment and highlight potential targets for improving the efficacy of this treatment strategy.

A novel enterocyte-related 4-gene signature for predicting prognosis in colon adenocarcinoma

Background

Colon adenocarcinoma (COAD) is a fatal disease, and its cases are constantly increasing worldwide. Further, the therapeutic and management strategies for patients with COAD are still unsatisfactory due to the lack of accurate patient classification and prognostic models. Therefore, our study aims to identify prognostic markers in patients with COAD and construct a cell subtype-specific prognostic model with high accuracy and robustness.

Methods

Single-cell transcriptomic data of six samples were retrieved from the Gene expression omnibus (GEO) database. The cluster annotation and cell-cell communication analysis identified enterocytes as a key player mediating signal communication networks. A four-gene signature prognostic model was constructed based on the enterocyte-related differentially expressed genes (ERDEGs) in patients with COAD of the Cancer Genome Atlas cohort. The prognostic model was validated on three external validation cohorts from the GEO database. The correlation between immune cell infiltration, immunotherapy response, drug sensitivity, and the four-gene signature prognostic model was investigated. Finally, immunohistochemistry (IHC) was performed to determine the expression of the four genes.

Results

We found that the proportion of epithelial cells was obviously large in COAD samples. Further analysis of epithelial cells showed that the activity of the enterocytes was highest in the cell-cell communication network. Based on enterocyte data, 30 ERDEGs were identified and a 4-gene prognostic model including CPM, CLCA4, ELOVL6, and ATP2A3 was developed and validated. The risk score derived from this model was considered as an independent variable factor to predict overall survival. The patients were divided into high- and low-risk groups based on the median riskscore value. The correlation between immune cell infiltration, immunotherapy response, immune status, clinical characteristics, drug sensitivity, and risk score was analyzed. IHC confirmed the expression of signature genes in tissues from normal individuals, patients with polyps, and COAD.

Conclusion

In this study, we constructed and validated a novel four-gene signature prognostic model, which could effectively predict the response to immunotherapy and overall survival in patients with COAD. More importantly, this model provides useful insight into the management of colon cancer patients and aids in designing personalized therapy.

Effect of Lactobacillus fermentum ZS40 on the NF-κB signaling pathway in an azomethane-dextran sulfate sodium-induced colon cancer mouse model

The occurrence of intestinal diseases such as colon cancer is closely related to the intestinal flora. Lactobacillus fermentum is a gut probiotic that plays an important role in chronic intestinal inflammation and colon cancer. In the current study, we investigated the effect of Lactobacillus fermentum ZS40 on NF-κB signaling pathway of azomethane-dextran sulfate sodium (AOM-DSS) -induced colon cancer in mice. Animals were divided into control group (NC), AOM-DSS-induced model group (CRC), AOM-DSS plus high-dose Lactobacillus fermentum ZS40 (ZS40-H), AOM-DSS plus low-dose Lactobacillus fermentum ZS40 (ZS40-L), AOM-DSS plus Lactobacillus bulgaricus (BLA), and AOM-DSS plus sulfasalazine (SD)-treated group. Observation of animal physiological activity (body weight and defecation), biochemical measurements, histopathological examination of colon tissue, qPCR to evaluate the expression of inflammation-related genes, immunohistochemical analysis of CD34 and CD117, and Western blot analysis of NF-κB signaling pathway were performed. Compared with the CRC group, the ZS40-H, ZS40-L, BLA, and SD groups had decreased levels of colon cancer marker proteins CD34 and CD117, and the number of abnormal colonic lesions observed by colon histology decreased, while the ZS40-H group showed excellent results. In addition, all probiotic interventions showed weight loss effects. The expression of inflammatory stimulators TNF-α and IL-1β in the probiotic treatment group decreased; the expression of key proteins IκBα and p65 in the NF-κB signaling pathway also decreased, resulting in a decrease in the expression of the target protein Cox-2. Therefore, administration of Lactobacillus fermentum ZS40 as a probiotic can alleviate intestinal inflammation and prevent colon cancer in mice.

A Detailed Overview About the Single-Cell Analyses of Solid Tumors Focusing on Colorectal Cancer

In recent years, the evolution of the molecular biological technical background led to the widespread application of single-cell sequencing, a versatile tool particularly useful in the investigation of tumor heterogeneity. Even 10 years ago the comprehensive characterization of colorectal cancers by The Cancer Genome Atlas was based on measurements of bulk samples. Nowadays, with single-cell approaches, tumor heterogeneity, the tumor microenvironment, and the interplay between tumor cells and their surroundings can be described in unprecedented detail. In this review article we aimed to emphasize the importance of single-cell analyses by presenting tumor heterogeneity and the limitations of conventional investigational approaches, followed by an overview of the whole single-cell analytic workflow from sample isolation to amplification, sequencing and bioinformatic analysis and a review of recent literature regarding the single-cell analysis of colorectal cancers.