Technical differences between sequencing and microarray platforms impact transcriptomic subtyping of colorectal cancer

Transcriptomic profiling reveals mechanism, therapeutic potential, and prognostic value of cancer stemness characteristic in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) recurrence, distant metastasis, and drug resistance remain significant obstacles in clinical prognosis. Cancer stemness is hypothesized to be a key contributor, though direct evidence is sparse. We utilized bioinformatics and machine learning techniques on single-cell RNA-seq and bulk transcriptomic datasets, complemented by basic experiments, to investigate stemness-based characteristics in NPC. Our analysis identified two potential developmental trajectories of nasopharyngeal cancer cells, each exhibiting varying levels of stemness. We subsequently identified and validated a cancer stemness-related signature (STEM-signature). Single-cell profiling revealed enrichment of LAYN + CD8 + , CTLA4 + CD4 + , CXCL13 + CD4 + T cells, tumor-associated macrophages, and CD14 + monocytes in NPC patients with high stemness. NicheNet analysis suggested these immune cells regulate cancer stemness. Bulk transcriptomic analysis corroborated these findings, indicating a poor therapeutic response in high-stemness NPC. We predicted 13 potential drugs and identified 13 stemness-related miRNAs for NPC with high stemness. A Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model, based on this miRNA signature, predicted overall survival with an AUC of 0.71 and 0.72 in validation and testing sets, respectively. The miRNA-based stemness signature outperformed previous established signatures. Multivariate Cox regression analysis indicated that our prognostic signature could serve as an independent prognostic factor (p < 0.001). Basic experiments showed that miR-300, miR-361-5p, miR-1246, and miR-1290 enhanced the stemness characteristics of NPC cells, promoting proliferation, invasion, and migration. These findings suggest that these four stemness-related miRNAs could serve as therapeutic targets, potentially improving therapeutic responses by targeting stemness-related genes.

Development and validation of a biomarker-based prediction model for metastasis in patients with colorectal cancer: Application of machine learning algorithms

Objective

The purpose of the current study was to develop and validate a biomarker-based prediction model for metastasis in patients with colorectal cancer (CRC).

Methods

Two datasets, GSE68468 and GSE41568, were retrieved from the Gene Expression Omnibus (GEO) database. In the GSE68468 dataset, key biomarkers were identified through a screening process involving differential expression analysis, redundancy analysis, and recursive feature elimination technique. Subsequently, the prediction model was developed and internally validated using five machine learning (ML) algorithms including lasso and elastic-net regularized generalized linear model (glmnet), k-nearest neighbors (kNN), support vector machine (SVM) with Radial Basis Function Kernel, random forest (RF), and eXtreme Gradient Boosting (XGBoost). The predictive performance of the algorithm with the highest accuracy was then externally validated on the GSE41568 dataset.

Results

Among 22,283 registered genes in the GSE68468 dataset, the screening process identified 16 key genes including MMP3, CCDC102B, CDH2, SCGB1A1, KRT7, CYP1B1, LAMC3, ALB, DIXDC1, VWF, MMP1, CYP4B1, NKX3-2, TMEM158, GADD45B, SERPINA1 and these genes were used to build the prediction model. On the internal validation dataset, the prediction performance of five ML algorithms was as follows; RF (accuracy = 0.97 and kappa = 0.91), XGBoost (0.93, 0.81), kNN (0.93, 0.81), glmnet (0.93, 0.82) and SVM (0.92, 0.80). Top five biomarkers were MMP3, CCDC102B, CDH2, VWF and MMP1. The RF model exhibited an accuracy of 0.97, a kappa value of 0.92, and an area under the curve (AUC) of 0.99 in the external validation dataset.

Conclusion

The results of this study have identified biomarkers through ML algorithms which help to identify patients with CRC prone to metastasis.

Multiregional transcriptomics identifies congruent consensus subtypes with prognostic value beyond tumor heterogeneity of colorectal cancer

Intra-tumor heterogeneity compromises the clinical value of transcriptomic classifications of colorectal cancer. We investigated the prognostic effect of transcriptomic heterogeneity and the potential for classifications less vulnerable to heterogeneity in a single-hospital series of 1093 tumor samples from 692 patients, including multiregional samples from 98 primary tumors and 35 primary-metastasis sets. We show that intra-tumor heterogeneity of the consensus molecular subtypes (CMS) is frequent and has poor-prognostic associations independently of tumor microenvironment markers. Multiregional transcriptomics uncover cancer cell-intrinsic and low-heterogeneity signals that recapitulate the intrinsic CMSs proposed by single-cell sequencing. Further subclassification identifies congruent CMSs that explain a larger proportion of variation in patient survival than intra-tumor heterogeneity. Plasticity is indicated by discordant intrinsic phenotypes of matched primary and metastatic tumors. We conclude that multiregional sampling reconciles the prognostic power of tumor classifications from single-cell and bulk transcriptomics in the context of intra-tumor heterogeneity, and phenotypic plasticity challenges the reconciliation of primary and metastatic subtypes.

Sparassis latifolia and exercise training as complementary medicine mitigated the 5-fluorouracil potent side effects in mice with colorectal cancer: bioinformatics approaches, novel monitoring pathological metrics, screening signatures, and innovative management tactic

BACKGROUND

Prompt identification and assessment of the disease are essential for reducing the death rate associated with colorectal cancer (COL). Identifying specific causal or sensitive components, such as coding RNA (cRNA) and non-coding RNAs (ncRNAs), may greatly aid in the early detection of colorectal cancer.

METHODS

For this purpose, we gave natural chemicals obtained from Sparassis latifolia (SLPs) either alone or in conjunction with chemotherapy (5-Fluorouracil to a mouse colorectal tumor model induced by AOM-DSS. The transcription profile of non-coding RNAs (ncRNAs) and their target hub genes was evaluated using qPCR Real-Time, and ELISA techniques.

RESULTS

MSX2, MMP7, ITIH4, and COL1A2 were identified as factors in inflammation and oxidative stress, leading to the development of COL. The hub genes listed, upstream regulatory factors such as lncRNA PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p have been discovered as biomarkers for prognosis and diagnosis of COL. The SLPs and exercise, effectively decreased the size and quantity of tumors.

CONCLUSIONS

This effect may be attributed to the modulation of gene expression levels, including MSX2, MMP7, ITIH4, COL1A2, PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p. Ultimately, SLPs and exercise have the capacity to be regarded as complementing and enhancing chemotherapy treatments, owing to their efficacious components.

Transcriptomic subtyping of malignant peripheral nerve sheath tumours highlights immune signatures, genomic profiles, patient survival and therapeutic targets

BACKGROUND

Malignant peripheral nerve sheath tumour (MPNST) is an aggressive orphan disease commonly affecting adolescents or young adults. Current knowledge of molecular tumour biology has been insufficient for development of rational treatment strategies. We aimed to discover molecular subtypes of potential clinical relevance.

METHODS

Fresh frozen samples of MPNSTs (n = 94) and benign neurofibromas (n = 28) from 115 patients in a European multicentre study were analysed by DNA copy number and/or transcriptomic profiling. Unsupervised transcriptomic subtyping was performed and the subtypes characterized for genomic aberrations, clinicopathological associations and patient survival.

FINDINGS

MPNSTs were classified into two transcriptomic subtypes defined primarily by immune signatures and proliferative processes. "Immune active" MPNSTs (44%) had sustained immune signals relative to neurofibromas, were more frequently low-grade (P = 0.01) and had favourable prognostic associations in a multivariable model of disease-specific survival with clinicopathological factors (hazard ratio 0.25, P = 0.003). "Immune deficient" MPNSTs were more aggressive and characterized by proliferative signatures, high genomic complexity, aberrant TP53 and PRC2 loss, as well as high relative expression of several potential actionable targets (EGFR, ERBB2, EZH2, KIF11, PLK1, RRM2). Integrated gene-wise analyses suggested a DNA copy number-basis for proliferative transcriptomic signatures in particular, and the tumour copy number burden further stratified the transcriptomic subtypes according to patient prognosis (P < 0.01).

INTERPRETATION

Approximately half of MPNSTs belong to an "immune deficient" transcriptomic subtype associated with an aggressive disease course, PRC2 loss and expression of several potential therapeutic targets, providing a rationale for molecularly-guided intervention trials.

FUNDING

Research grants from non-profit organizations, as stated in the Acknowledgements.

Data Mining of Microarray Datasets in Translational Neuroscience

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

TissueSpace: a web tool for rank-based transcriptome representation and its applications in molecular medicine

BACKGROUND

Cross-platform or cross-experiment transcriptome data is hard to compare as the original gene expression values from different platforms cannot be compared directly. The inherent gene expression ranking information is rarely utilized.

OBJECTIVE

Use of reduced vector to represent transcriptome data independent of platforms.

METHODS

Thus, we turned the expression profile into a rank vector, where a higher expression has a higher rank value, then applied Latent semantic analysis (LSA) to get compact and continuous 100-dimensional vector representations for samples.

RESULTS

Results showed that the reconstructed vector has a precision of 96.7% in recovering tissue labels from an independent dataset. A user-friendly tool TissueSpace was developed, which provides users the following functionalities: (1) convert different gene ID types to Ensembl gene IDs; (2) project any human transcriptome profile to get vector representation for downstream analysis; (3) functional enrichment for each of the 100-dimensional vector features. Case studies for its applications in human common diseases indicate its usefulness.

CONCLUSIONS

TissueSpace could be used to generate testable hypotheses for translational medicine. The TissueSpace tool is available at http://bioinformatics.fafu.edu.cn/tissuespace/ .

What's Wrong in a Jump? Prediction and Validation of Splice Site Variants

Alternative splicing (AS) is a crucial process to enhance gene expression driving organism development. Interestingly, more than 95% of human genes undergo AS, producing multiple protein isoforms from the same transcript. Any alteration (e.g., nucleotide substitutions, insertions, and deletions) involving consensus splicing regulatory sequences in a specific gene may result in the production of aberrant and not properly working proteins. In this review, we introduce the key steps of splicing mechanism and describe all different types of genomic variants affecting this process (splicing variants in acceptor/donor sites or branch point or polypyrimidine tract, exonic, and deep intronic changes). Then, we provide an updated approach to improve splice variants detection. First, we review the main computational tools, including the recent Machine Learning-based algorithms, for the prediction of splice site variants, in order to characterize how a genomic variant interferes with splicing process. Next, we report the experimental methods to validate the predictive analyses are defined, distinguishing between methods testing RNA (transcriptomics analysis) or proteins (proteomics experiments). For both prediction and validation steps, benefits and weaknesses of each tool/procedure are accurately reported, as well as suggestions on which approaches are more suitable in diagnostic rather than in clinical research.

The expressed mutational landscape of microsatellite stable colorectal cancers

Background

Colorectal cancer is the 2nd leading cause of cancer-related deaths with few patients benefiting from biomarker-guided therapy. Mutation expression is essential for accurate interpretation of mutations as biomarkers, but surprisingly, little has been done to analyze somatic cancer mutations on the expression level. We report a large-scale analysis of allele-specific mutation expression.

Methods

Whole-exome and total RNA sequencing was performed on 137 samples from 121 microsatellite stable colorectal cancers, including multiregional samples of primary and metastatic tumors from 4 patients. Data were integrated with allele-specific resolution. Results were validated in an independent set of 241 colon cancers. Therapeutic associations were explored by pharmacogenomic profiling of 15 cell lines or patient-derived organoids.

Results

The median proportion of expressed mutations per tumor was 34%. Cancer-critical mutations had the highest expression frequency (gene-wise mean of 58%), independent of frequent allelic imbalance. Systematic deviation from the general pattern of expression levels according to allelic frequencies was detected, including preferential expression of mutated alleles dependent on the mutation type and target gene. Translational relevance was suggested by correlations of KRAS/NRAS or TP53 mutation expression levels with downstream oncogenic signatures (p < 0.03), overall survival among patients with stage II and III cancer (KRAS/NRAS: hazard ratio 6.1, p = 0.0070), and targeted drug sensitivity. The latter was demonstrated for EGFR and MDM2 inhibition in pre-clinical models.

Conclusions

Only a subset of mutations in microsatellite stable colorectal cancers were expressed, and the “expressed mutation dose” may provide an opportunity for more fine-tuned biomarker interpretations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00955-2.

Metastatic heterogeneity of the consensus molecular subtypes of colorectal cancer

Gene expression-based subtypes of colorectal cancer have clinical relevance, but the representativeness of primary tumors and the consensus molecular subtypes (CMS) for metastatic cancers is not well known. We investigated the metastatic heterogeneity of CMS. The best approach to subtype translation was delineated by comparisons of transcriptomic profiles from 317 primary tumors and 295 liver metastases, including multi-metastatic samples from 45 patients and 14 primary-metastasis sets. Associations were validated in an external data set (n = 618). Projection of metastases onto principal components of primary tumors showed that metastases were depleted of CMS1-immune/CMS3-metabolic signals, enriched for CMS4-mesenchymal/stromal signals, and heavily influenced by the microenvironment. The tailored CMS classifier (available in an updated version of the R package CMScaller) therefore implemented an approach to regress out the liver tissue background. The majority of classified metastases were either CMS2 or CMS4. Nonetheless, subtype switching and inter-metastatic CMS heterogeneity were frequent and increased with sampling intensity. Poor-prognostic value of CMS1/3 metastases was consistent in the context of intra-patient tumor heterogeneity.

Upregulating KTN1 promotes Hepatocellular Carcinoma progression

Background: Hepatocellular carcinoma (HCC) presents a common malignant tumor worldwide. Although kinectin 1 (KTN1) is the most frequently identified antigen in HCC tissues, the detailed roles of KTN1 in HCC remain unknown. This study seeks to clarify the expression status and clinical value of KTN1 in HCC and to explore the complicated biological functions of KTN1 and its underlying mechanisms.

Methods: In-house reverse transcription quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression of KTN1 in HCC tissues. External gene microarrays and RNA-sequencing datasets were downloaded to confirm the expression patterns of KTN1. The prognostic ability of KTN1 in HCC was assessed by a Kaplan-Meier curve and a hazard ratio forest plot. The CRISPR/Cas9 gene-editing system was used to knock out KTN1 in Huh7 cells, which was verified by PCR-Sanger sequencing and western blotting. Assays of cell migration, invasion, viability, cell cycle, and apoptosis were conducted to explore the biological functions. RNA sequencing was performed to quantitatively analyze the functional deregulation in KTN1-knockout cells compared to Huh7-wild-type cells. Upregulated genes that co-expressed with KTN1 were identified from HCC tissues and were functionally annotated.

Results: KTN1 expression was increased in HCC tissues (standardized mean difference [SMD] = 0.20 [0.04, 0.37]). High KTN1 expression was significantly correlated with poorer prognosis of HCC patients, and KTN1 may be an independent risk factor for HCC (pooled HRs = 1.31 [1.05, 1.64]). After KTN1-knockout, the viability, migration, and invasion ability of HCC cells were inhibited. The proportion of HCC cells in the G0-G1 phases increased after KTN1 knockout, which also elevated the apoptosis rates in HCC cells. Several cascades, including innate immune response, chemical carcinogenesis, and positive regulation of transcription by RNA polymerase II, were dramatically changed after KTN1 knockout. KTN1 primarily participated in the cell cycle, DNA replication, and microRNAs in cancer pathways in HCC tissues.

Conclusion: Upregulation of KTN1 served as a promising prognosticator in HCC patients. KTN1 promotes the occurrence and deterioration of HCC by mediating cell survival, migration, invasion, cell cycle activation, and apoptotic inhibition. KTN1 may be a therapeutic target in HCC patients.

Exploring and modelling colon cancer inter-tumour heterogeneity: opportunities and challenges

Colon cancer inter-tumour heterogeneity is installed on multiple levels, ranging from (epi)genetic driver events to signalling pathway rewiring reflected by differential gene expression patterns. Although the existence of heterogeneity in colon cancer has been recognised for a longer period of time, it is sparingly incorporated as a determining factor in current clinical practice. Here we describe how unsupervised gene expression-based classification efforts, amongst which the consensus molecular subtypes (CMS), can stratify patients in biological subgroups associated with distinct disease outcome and responses to therapy. We will discuss what is needed to extend these subtyping efforts to the clinic and we will argue that preclinical models recapitulate CMS subtypes and can be of vital use to increase our understanding of treatment response and resistance and to discover novel targets for therapy.