Discovering the molecular differences between right- and left-sided colon cancer using machine learning methods

pyRforest: a comprehensive R package for genomic data analysis featuring scikit-learn Random Forests in R

Random Forest models are widely used in genomic data analysis and can offer insights into complex biological mechanisms, particularly when features influence the target in interactive, nonlinear, or nonadditive ways. Currently, some of the most efficient Random Forest methods in terms of computational speed are implemented in Python. However, many biologists use R for genomic data analysis, as R offers a unified platform for performing additional statistical analysis and visualization. Here, we present an R package, pyRforest, which integrates Python scikit-learn "RandomForestClassifier" algorithms into the R environment. pyRforest inherits the efficient memory management and parallelization of Python, and is optimized for classification tasks on large genomic datasets, such as those from RNA-seq. pyRforest offers several additional capabilities, including a novel rank-based permutation method for biomarker identification. This method can be used to estimate and visualize P-values for individual features, allowing the researcher to identify a subset of features for which there is robust statistical evidence of an effect. In addition, pyRforest includes methods for the calculation and visualization of SHapley Additive exPlanations values. Finally, pyRforest includes support for comprehensive downstream analysis for gene ontology and pathway enrichment. pyRforest thus improves the implementation and interpretability of Random Forest models for genomic data analysis by merging the strengths of Python with R. pyRforest can be downloaded at: https://www.github.com/tkolisnik/pyRforest with an associated vignette at https://github.com/tkolisnik/pyRforest/blob/main/vignettes/pyRforest-vignette.pdf.

Integrating Omics Data and AI for Cancer Diagnosis and Prognosis

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from 1 January 2020 to 22 December 2023. Articles were collected using key search terms such as "artificial intelligence" and "machine learning." Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, and clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. Eight studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

Comparison of left- and right-sided colorectal cancer to explore prognostic signatures related to pyroptosis

Background

Colorectal cancer (CRC) is one of the most common malignancies, and pyroptosis exerts an immunoregulatory role in CRC. Although the location of the primary tumor is a prognostic factor for patients with CRC, the mechanisms of pyroptosis in left- and right-sided CRC remain unclear.

Methods

Expression and clinical data were collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Differences in clinical characteristics, immune cell infiltration, and somatic mutations between left- and right-sided CRC were then compared. After screening for differentially expressed genes, Pearson correlation analysis was performed to select pyroptosis-related genes, followed by a gene set enrichment analysis. Univariate and multivariate Cox regression analyses were used to construct and validate the prognostic model and nomogram for predicting prognosis. Collected left- and right-sided CRC samples were subjected to reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to validate the expression of key pyroptosis-related genes.

Results

Left- and right-sided CRC exhibited significant differences in clinical features and immune cell infiltration. Five prognostic signatures were identified from among 134 pyroptosis-related differentially expressed genes to construct a risk score-based prognostic model, and adverse outcomes for high-risk patients were further verified using an external cohort. A nomogram was also generated based on three independent prognostic factors to predict survival probabilities, while calibration curves confirmed the consistency between the predicted and actual survival. Experiment data confirmed the significant differential expression of five genes between left- and right-sided CRC.

Conclusion

The five identified pyroptosis-related gene signatures may be potential biomarkers for predicting prognosis in left- and right-sided CRC and may help improve the clinical outcomes of patients with CRC.

Identifying important microbial and genomic biomarkers for differentiating right- versus left-sided colorectal cancer using random forest models

BACKGROUND

Colorectal cancer (CRC) is a heterogeneous disease, with subtypes that have different clinical behaviours and subsequent prognoses. There is a growing body of evidence suggesting that right-sided colorectal cancer (RCC) and left-sided colorectal cancer (LCC) also differ in treatment success and patient outcomes. Biomarkers that differentiate between RCC and LCC are not well-established. Here, we apply random forest (RF) machine learning methods to identify genomic or microbial biomarkers that differentiate RCC and LCC.

METHODS

RNA-seq expression data for 58,677 coding and non-coding human genes and count data for 28,557 human unmapped reads were obtained from 308 patient CRC tumour samples. We created three RF models for datasets of human genes-only, microbes-only, and genes-and-microbes combined. We used a permutation test to identify features of significant importance. Finally, we used differential expression (DE) and paired Wilcoxon-rank sum tests to associate features with a particular side.

RESULTS

RF model accuracy scores were 90%, 70%, and 87% with area under curve (AUC) of 0.9, 0.76, and 0.89 for the human genomic, microbial, and combined feature sets, respectively. 15 features were identified as significant in the model of genes-only, 54 microbes in the model of microbes-only, and 28 genes and 18 microbes in the model with genes-and-microbes combined. PRAC1 expression was the most important feature for differentiating RCC and LCC in the genes-only model, with HOXB13, SPAG16, HOXC4, and RNLS also playing a role. Ruminococcus gnavus and Clostridium acetireducens were the most important in the microbial-only model. MYOM3, HOXC4, Coprococcus eutactus, PRAC1, lncRNA AC012531.25, Ruminococcus gnavus, RNLS, HOXC6, SPAG16 and Fusobacterium nucleatum were most important in the combined model.

CONCLUSIONS

Many of the identified genes and microbes among all models have previously established associations with CRC. However, the ability of RF models to account for inter-feature relationships within the underlying decision trees may yield a more sensitive and biologically interconnected set of genomic and microbial biomarkers.

Comprehensive genomic profiling of colorectal cancer patients reveals differences in mutational landscapes among clinical and pathological subgroups

With the widespread of colonoscopy, colorectal cancer remains to be one of the most detrimental types of cancer. Though there were multiple studies investigating the genomic landscape of colorectal cancer, a comprehensive analysis uncovering the differences between various types of colorectal cancer is still lacking. In our study, we performed genomic analysis on 133 patients with colorectal cancer. Mutated FAT1 and PKHD1 and altered Hippo pathway genes were found to be enriched in early-onset colorectal cancer. APOBEC signature was prevalent in microsatellite stable (MSS) patients and was related to lymph node metastasis. ZNF217 mutations were significantly associated with early-stage colorectal cancer. In all, this study represents a comprehensive genomic analysis uncovering potential molecular mechanisms underneath different subgroups of colorectal cancer thus providing new targets for precision treatment development.

Salivary metabolomics with machine learning for colorectal cancer detection

As the worldwide prevalence of colorectal cancer (CRC) increases, it is vital to reduce its morbidity and mortality through early detection. Saliva‐based tests are an ideal noninvasive tool for CRC detection. Here, we explored and validated salivary biomarkers to distinguish patients with CRC from those with adenoma (AD) and healthy controls (HC). Saliva samples were collected from patients with CRC, AD, and HC. Untargeted salivary hydrophilic metabolite profiling was conducted using capillary electrophoresis–mass spectrometry and liquid chromatography–mass spectrometry. An alternative decision tree (ADTree)‐based machine learning (ML) method was used to assess the discrimination abilities of the quantified metabolites. A total of 2602 unstimulated saliva samples were collected from subjects with CRC (n = 235), AD (n = 50), and HC (n = 2317). Data were randomly divided into training (n = 1301) and validation datasets (n = 1301). The clustering analysis showed a clear consistency of aberrant metabolites between the two groups. The ADTree model was optimized through cross‐validation (CV) using the training dataset, and the developed model was validated using the validation dataset. The model discriminating CRC + AD from HC showed area under the receiver‐operating characteristic curves (AUC) of 0.860 (95% confidence interval [CI]: 0.828‐0.891) for CV and 0.870 (95% CI: 0.837‐0.903) for the validation dataset. The other model discriminating CRC from AD + HC showed an AUC of 0.879 (95% CI: 0.851‐0.907) and 0.870 (95% CI: 0.838‐0.902), respectively. Salivary metabolomics combined with ML demonstrated high accuracy and versatility in detecting CRC.

Tumour genotypes account for survival differences in right- and left-sided colon cancers

AIM

We sought to identify genetic differences between right- and left-sided colon cancers and using these differences explain lower survival in right-sided cancers.

METHOD

A retrospective review of patients diagnosed with colon cancer was performed using The Cancer Genome Atlas, a cancer genetics registry with patient and tumour data from 20 North American institutions. The primary outcome was 5-year overall survival. Predictors for survival were identified using directed acyclic graphs and Cox proportional hazards models.

RESULTS

A total of 206 right- and 214 left-sided colon cancer patients with 84 recorded deaths were identified. The frequency of mutated alleles differed significantly in 12 of 25 genes between right- and left-sided tumours. Right-sided tumours had worse survival with a hazard ratio of 1.71 (95% confidence interval 1.10-2.64, P = 0.017). The total effect of the genetic loci on survival showed five genes had a sizeable effect on survival: DNAH5, MUC16, NEB, SMAD4, and USH2A. Lasso-penalized Cox regression selected 13 variables for the highest-performing model, which included cancer stage, positive resection margin, and mutated alleles at nine genes: MUC16, USH2A, SMAD4, SYNE1, FLG, NEB, TTN, OBSCN, and DNAH5. Post-selection inference demonstrated that mutations in MUC16 (P = 0.01) and DNAH5 (P = 0.02) were particularly predictive of 5-year overall survival.

CONCLUSIONS

Our study showed that genetic mutations may explain survival differences between tumour sites. Further studies on larger patient populations may identify other genes, which could form the foundation for more precise prognostication and treatment decisions beyond current rudimentary TNM staging.

Advances in radiological staging of colorectal cancer

The role of imaging in clinically staging colorectal cancer has grown substantially in the 21^st century with more widespread availability of multi-row detector computed tomography (CT), high-resolution magnetic resonance imaging (MRI) with diffusion weighted imaging (DWI), and integrated positron-emission tomography (PET)/CT. In contrast to staging many other cancers, increasing colorectal cancer stage does not highly correlate with survival. As has been the case previously, clinical practice incorporates advances in staging and it is used to guide therapy before adoption into international staging guidelines. Emerging imaging techniques show promise to become part of future staging standards.