A Systems Biology Approach to Identifying a Master Regulator That Can Transform the Fast Growing Cellular State to a Slowly Growing One in Early Colorectal Cancer Development Model

Landscape of N1-methyladenosin (m1A) modification pattern in colorectal cancer

BACKGROUND

N1-methyladenosine (m1A) is a recently identified mRNA modification. However, it is still unclear that how m1A alteration affects the development of colorectal cancer (CRC).

AIMS

The landscape of m1A modification patterns regarding tumor immune microenvironment (TIME) in CRC is a lack of knowledge. Thus, this study will utilize the public database to comprehensively evaluate of multiple m1A methylation regulators in CRC.

METHODS AND RESULTS

We retrospectively analyzed 398 patients with CRC and 39 healthy people for negative control, using the The Cancer Genome Atlas (TCGA) database to evaluate m1A modification patterns regarding tumor immune microenvironment (TIME) in CRC. The m1Ascore was developed via principal component analysis. And its clinical value in prognosis of CRC was further explored. Our study revealed 12 key m1A-related DEGs including CLDN3, MUC2 and CCDC85B which are identified associated with invasion and metastasis in CRC. The most important biological processes linked to weak immune response and poor prognosis were the regulation of RNA metabolism and RNA biosynthesis. Furthermore, we found that compared to patients with low m1A scores, those with high m1A scores had higher percentage, larger tumor burdens, and worse prognosis.

CONCLUSION

Significantly diverse m1A modification patterns can be seen in CRC. Through its impact on TIME and immunological dysfunction, the heterogeneity of m1A alteration patterns influences the prognosis of CRC. This study provided novel insights into the m1A modification in CRC which might promote the development of personalized immunotherapy strategies.

Use of exosome transcriptome-based analysis to identify novel biomarkers in patients with locally advanced esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiotherapy

Background

The prognosis of esophageal squamous cell carcinoma (ESCC) is improved by neoadjuvant chemoradiotherapy (nCRT), especially for patients with pathologic complete response (pCR). Despite the efforts to predict treatment response using multimodality, no molecule has proven to be a strong biomarker. This study aimed to profile the expression of exosome transcriptome that could predict pCR in ESCC before and after nCRT.

Methods

We collected paired blood samples of 15 patients with ESCC who received nCRT and radical surgery. They were divided into 3 groups: (A) residual tumor in the first clinical response evaluation (CRE-1), (B) no residual tumor in CRE-1 but with residual tumor in CRE-2 which was performed after 5-6 weeks, and (C) no residual tumor in CRE-1 or CRE-2. For each patient, the blood sample was collected before nCRT (time point 0); and then 6 weeks after nCRT, the clinical response was evaluated, and another blood sample was collected (time point 1).

Results

Using the intersection of different sets, we found 23 progression-associated messenger RNAs (mRNAs) and 67 remission-associated mRNAs. Between remission-associated mRNAs and the targets of progression-associated (carcinogenic) microRNAs (miRNAs), the intersection was acquired, and 2 miRNA-mRNA networks (IFIT2-miR-3615-IFIT2-miR-484 and BTN3A3-miR-6803-3p) were identified. Among the intersection of progression-associated (carcinogenic) mRNAs and the targets of remission-associated miRNAs, there is a network with miR-132-3p (remission-associated miRNA) located at the core, matched with DICER1, KLHL8, ANKRD12, ASH1L, and IMP4.

Conclusions

Our findings identified altered plasma exosome RNAs among the different groups and between different time points of nCRT, as well as the corresponding enrichments and regulatory networks, which may serve as potentially predictors of treatment response for patients with ESCC after nCRT.

Single-cell RNA sequencing in cancer: Applications, advances, and emerging challenges

Cancer has become one of the greatest threats to human health, and new technologies are urgently needed to further clarify the mechanisms of cancer so that better detection and treatment strategies can be developed. At present, extensive genomic analysis and testing of clinical specimens shape the insights into carcinoma. Nevertheless, carcinoma of humans is a complex ecosystem of cells, including carcinoma cells and immunity-related and stroma-related subsets, with accurate characteristics obscured by extensive genome-related approaches. A growing body of research shows that sequencing of single-cell RNA (scRNA-seq) is emerging to be an effective way for dissecting human tumor tissue at single-cell resolution, presenting one prominent way for explaining carcinoma biology. This review summarizes the research progress of scRNA-seq in the field of tumors, focusing on the application of scRNA-seq in tumor circulating cells, tumor stem cells, tumor drug resistance, the tumor microenvironment, and so on, which provides a new perspective for tumor research.

Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion

The molecular regulatory network (MRN) within a cell determines cellular states and transitions between them. Thus, modeling of MRNs is crucial, but this usually requires extensive analysis of time-series measurements, which is extremely difficult to obtain from biological experiments. However, single-cell measurement data such as single-cell RNA-sequencing databases have recently provided a new insight into resolving this problem by ordering thousands of cells in pseudo-time according to their differential gene expressions. Neural network modeling can be employed by using temporal data as learning data. In contrast, Boolean network modeling of MRNs has a growing interest, as it is a parameter-free logical modeling and thereby robust to noisy data while still capturing essential dynamics of biological networks. In this study, we propose a Boolean feedforward neural network (FFN) modeling by combining neural network and Boolean network modeling approach to reconstruct a practical and useful MRN model from large temporal data. Furthermore, analyzing the reconstructed MRN model can enable us to identify control targets for potential cellular state conversion. Here, we show the usefulness of Boolean FFN modeling by demonstrating its applicability through a toy model and biological networks.