Rho GTPase gene expression and breast cancer risk: a Mendelian randomization analysis

Molecular basis and current insights of atypical Rho small GTPase in cancer

Atypical Rho GTPases are a subtype of the Rho GTPase family that are involved in diverse cellular processes. The typical Rho GTPases, led by RhoA, Rac1 and Cdc42, have been well studied, while relative studies on atypical Rho GTPases are relatively still limited and have great exploration potential. With the increase in studies, current evidence suggests that atypical Rho GTPases regulate multiple biological processes and play important roles in the occurrence and development of human cancers. Therefore, this review mainly discusses the molecular basis of atypical Rho GTPases and their roles in cancer. We summarize the sequence characteristics, subcellular localization and biological functions of each atypical Rho GTPase. Moreover, we review the recent advances and potential mechanisms of atypical Rho GTPases in the development of multiple cancers. A comprehensive understanding and extensive exploration of the biological functions of atypical Rho GTPases and their molecular mechanisms in tumors will provide important insights into the pathophysiology of tumors and the development of cancer therapeutic strategies.

Efficient plasma metabolic fingerprinting as a novel tool for diagnosis and prognosis of gastric cancer: a large-scale, multicentre study

OBJECTIVE

Metabolic biomarkers are expected to decode the phenotype of gastric cancer (GC) and lead to high-performance blood tests towards GC diagnosis and prognosis. We attempted to develop diagnostic and prognostic models for GC based on plasma metabolic information.

DESIGN

We conducted a large-scale, multicentre study comprising 1944 participants from 7 centres in retrospective cohort and 264 participants in prospective cohort. Discovery and verification phases of diagnostic and prognostic models were conducted in retrospective cohort through machine learning and Cox regression of plasma metabolic fingerprints (PMFs) obtained by nanoparticle-enhanced laser desorption/ionisation-mass spectrometry (NPELDI-MS). Furthermore, the developed diagnostic model was validated in prospective cohort by both NPELDI-MS and ultra-performance liquid chromatography-MS (UPLC-MS).

RESULTS

We demonstrated the high throughput, desirable reproducibility and limited centre-specific effects of PMFs obtained through NPELDI-MS. In retrospective cohort, we achieved diagnostic performance with areas under curves (AUCs) of 0.862-0.988 in the discovery (n=1157 from 5 centres) and independent external verification dataset (n=787 from another 2 centres), through 5 different machine learning of PMFs, including neural network, ridge regression, lasso regression, support vector machine and random forest. Further, a metabolic panel consisting of 21 metabolites was constructed and identified for GC diagnosis with AUCs of 0.921-0.971 and 0.907-0.940 in the discovery and verification dataset, respectively. In the prospective study (n=264 from lead centre), both NPELDI-MS and UPLC-MS were applied to detect and validate the metabolic panel, and the diagnostic AUCs were 0.855-0.918 and 0.856-0.916, respectively. Moreover, we constructed a prognosis scoring system for GC in retrospective cohort, which can effectively predict the survival of GC patients.

CONCLUSION

We developed and validated diagnostic and prognostic models for GC, which also contribute to advanced metabolic analysis towards diseases, including but not limited to GC.

The relation of blood cell division control protein 42 level with disease risk, comorbidity, tumor features/markers, and prognosis in colorectal cancer patients

BACKGROUND

Cell division control protein 42 (CDC42) is involved in colorectal cancer (CRC) progression by modulating CD8⁺ T cell activation, immune escape, and direct oncogenetic biological processes. This study aimed to explore the correlation of blood CDC42 with disease risk, comorbidities, disease features, tumor markers, and prognosis among CRC patients.

METHODS

CDC42 in peripheral blood mononuclear cells was detected by reverse transcription-quantitative polymerase chain reaction from 250 resectable CRC patients and 50 healthy controls (HCs). CDC42 was divided by quartiles, as well as high and low expressions in CRC patients for correlation and survival analysis.

RESULTS

CDC42 was elevated in CRC patients vs. HCs (p < 0.001), which had a good ability to distinguish CRC patients from HCs with the area under the curve (95% confidence interval) of 0.889 (0.841-0.937). In CRC patients, CDC42 was not associated with demographics or comorbidities (all p > 0.05), while its higher quartile was linked to increased T stage (p < 0.001), N stage (p = 0.009), TNM stage (p < 0.001), abnormal carcinoembryonic antigen (p = 0.043), and adjuvant chemotherapy administration (p = 0.002). Higher CDC42 quartile (p = 0.002) and CDC42 high (vs. low) (p < 0.001) were related to worse disease-free survival (DFS); meanwhile, elevated CDC42 quartile (p = 0.002) and CDC42 high (vs. low) (p = 0.001) were also linked to poor overall survival (OS). Multivariate Cox's regression analysis presented that CDC42 quartile 3 and 4 (vs. quartile 1) independently predicted declined DFS and OS (all p < 0.05).

CONCLUSION

Circulating CDC42 relates to higher disease risk, T, N, and TNM stage, abnormal tumor marker, and poor prognosis among CRC patients.

MarkerGenie: an NLP-enabled text-mining system for biomedical entity relation extraction

Motivation

Natural language processing (NLP) tasks aim to convert unstructured text data (e.g. articles or dialogues) to structured information. In recent years, we have witnessed fundamental advances of NLP technique, which has been widely used in many applications such as financial text mining, news recommendation and machine translation. However, its application in the biomedical space remains challenging due to a lack of labeled data, ambiguities and inconsistencies of biological terminology. In biomedical marker discovery studies, tools that rely on NLP models to automatically and accurately extract relations of biomedical entities are valuable as they can provide a more thorough survey of all available literature, hence providing a less biased result compared to manual curation. In addition, the fast speed of machine reader helps quickly orient research and development.

Results

To address the aforementioned needs, we developed automatic training data labeling, rule-based biological terminology cleaning and a more accurate NLP model for binary associative and multi-relation prediction into the MarkerGenie program. We demonstrated the effectiveness of the proposed methods in identifying relations between biomedical entities on various benchmark datasets and case studies.

Availability and implementation

MarkerGenie is available at https://www.genegeniedx.com/markergenie/. Data for model training and evaluation, term lists of biomedical entities, details of the case studies and all trained models are provided at https://drive.google.com/drive/folders/14RypiIfIr3W_K-mNIAx9BNtObHSZoAyn?usp=sharing.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.