Molecular-Subtype-Specific Biomarkers Improve Prediction of Prognosis in Colorectal Cancer

Exploring the biology of ctDNA release in colorectal cancer

BACKGROUND

Circulating tumor DNA (ctDNA) has emerged as a promising tool for early cancer detection and minimal residual disease monitoring. However, the biology underlying ctDNA release and its variation across cancer types and histologies remains poorly understood. This study investigated the biology behind ctDNA shedding in colorectal cancer.

METHODS

The study included a local cohort of 747 stage I-III colorectal cancer patients. All patients had ctDNA measurement prior to treatment and extensive clinical data. Primary tumor RNA sequencing and whole exome sequencing was performed in 95 and 652 patients respectively. Additionally, the study evaluated 89 non-small cell lung cancer patients from the TRACERx cohort, comprising primary tumor RNA sequencing and ctDNA measurement.

RESULTS

We found tumor size and proliferative capacity to be key factors associated with ctDNA shedding in colorectal cancer. Furthermore, we found that the secretory and CMS3 colorectal cancer subtypes exhibited lower ctDNA shedding, while microsatellite instability (MSI) tumors had higher levels of ctDNA. Mutational analysis did not reveal any genes or pathways associated with ctDNA shedding in colorectal cancer. A comparison of transcriptomic profiles across multiple cancer types demonstrated that colorectal cancer and lung squamous cell carcinoma tumors shared a high-proliferative ctDNA shedding phenotype, while lung adenocarcinoma tumors displayed a distinct low-proliferative subgroup. Additionally, proliferation levels correlated with ctDNA detection sensitivity across multiple cancer types.

CONCLUSION

These findings suggest that tumor size and proliferative capacity are drivers of ctDNA release in colorectal cancer and provide insights into the biology of ctDNA shedding on a pan-cancer level.

Identification of adipocyte infiltration-related gene subtypes for predicting colorectal cancer prognosis and responses of immunotherapy/chemotherapy

Colorectal cancer (CRC) is a prevalent and aggressive malignancy characterized by a complex tumor microenvironment (TME). Given the variations in the level of adipocyte infiltration in TME, the prognosis may differ among CRC patients. Thus, there is an urgent need to establish a reliable method for identifying adipocyte subtypes in CRC in order to elucidate the impact of adipocyte infiltration on CRC treatment and prognosis. Herein, 144 adipocyte-infiltration-related genes (AIRGs) were identified as predictive markers for the immune-associated features and prognosis of CRC patients. Based on the 144 genes, the unsupervised clustering algorithm identified two distinct clusters of CRC patients with variations in molecular and signaling pathways, clinicopathological characteristics and responses to CRC chemotherapy and immunotherapy. Furthermore, an AIRG prognostic signature was constructed and validated in independent datasets. Overall, this study developed a prognostic signature based on AIRGs in CRC, which may contribute to the development of personalized treatment strategies and enhance prognostic prediction for CRC patients.

Overexpression of TSPAN8 in consensus molecular subtype 3 colorectal cancer

BACKGROUND

Recently, consensus molecular subtypes (CMSs) have been proposed as a robust transcriptome-based classification system for colorectal cancer (CRC). Tetraspanins (TSPANs) are transmembrane proteins. They have been associated with the development of numerous malignancies, including CRC, through their role as "master organizers" for multi-molecular membrane complexes. No previous study has investigated the correlation between TSPANs and CMS classification. Herein, we investigated the expression of TSPANs in patient-derived primary CRC tissues and their CMS classifications.

METHODS

RNA samples were derived from primary CRC tissues (n = 100 patients diagnosed with colorectal adenocarcinoma) and subjected to RNA sequencing for transcriptome-based CMS classification and TSPAN-relevant analyses. Immunohistochemistry (IHC) and immunofluorescence (IF) stains were conducted to observe the protein expression level. To evaluate the relative biological pathways, gene-set enrichment analysis was performed.

RESULTS

Of the highly expressed TSPAN genes in CRC tissues (TSPAN8, TSPAN29, and TSPAN30), TSPAN8 was notably overexpressed in CMS3-classified primary tissues. The overexpression of TSPAN8 protein in CMS3 CRC was also observed by IHC and IF staining. As a result of gene-set enrichment analysis, TSPAN8 may potentially play a role in organizing signaling complexes for kinase-based metabolic deregulation in CMS3 CRC.

CONCLUSIONS

The present study reports the overexpression of TSPAN8 in CMS3 CRC. This study proposes TSPAN8 as a subtype-specific biomarker for CMS3 CRC. This finding provides a foundation for future CMS-based studies of CRC, a complex disease and the second leading cause of cancer mortality worldwide.

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.

How ceramides affect the development of colon cancer: from normal colon to carcinoma

The integrity of the colon and the development of colon cancer depend on the sphingolipid balance in colon epithelial cells. In this review, we summarize the current knowledge on how ceramides and their complex derivatives influence normal colon development and colon cancer development. Ceramides, glucosylceramides and sphingomyelin are essential membrane components and, due to their biophysical properties, can influence the activation of membrane proteins, affecting protein-protein interactions and downstream signalling pathways. Here, we review the cellular mechanisms known to be affected by ceramides and their effects on colon development. We also describe which ceramides are deregulated during colorectal carcinogenesis, the molecular mechanisms involved in ceramide deregulation and how this affects carcinogenesis. Finally, we review new methods that are now state of the art for studying lipid-protein interactions in the physiological environment.

Molecular pathological classification of colorectal cancer-an update

Colorectal cancer (CRC) has a broad range of molecular alterations with two major mechanisms of genomic instability (chromosomal instability and microsatellite instability) and has been subclassified into 4 consensus molecular subtypes (CMS) based on bulk RNA sequence data. Here, we update the molecular pathological classification of CRC with an overview of more recent bulk and single-cell RNA data analysis for development of transcriptional classifiers and risk stratification methods, taking into account the marked inter-tumoural and intra-tumoural heterogeneity of CRC. The importance of the stromal and immune components or tumour microenvironment (TME) to prognosis has emerged from these analyses. Attempts to remove the contribution of the tumour microenvironment and reveal neoplastic-specific transcriptional traits involved identification of the CRC intrinsic subtypes (CRIS). The use of immunohistochemistry and digital pathology to implement classification systems are evolving fields. Conventional adenoma versus serrated polyp pathway transcriptomic analysis and characterisation of canonical LGR5+ crypt base columnar stem cell versus ANXA1+ regenerative stem cell phenotypes emerged as key properties for improved understanding of transcriptional signals involved in molecular subclassification of colorectal cancers. Recently, classification by three pathway-derived subtypes (PDS1-3) has been developed, revealing a continuum of intrinsic biology associated with biological, stem cell, histopathological, and clinical attributes.

Hypoxia induced deregulation of sphingolipids in colon cancer is a prognostic marker for patient outcome

Sphingolipids are important for the physicochemical properties of cellular membranes and deregulated in tumors. In human colon cancer tissue ceramide synthase (CerS) 4 and CerS5 are reduced which correlates with a reduced survival probability of late-stage colon cancer patients. Both enzymes are reduced after hypoxia in advanced colorectal cancer (CRC) cells (HCT-116, SW620) but not in non-metastatic CRC cells (SW480, Caco-2). Downregulation of CerS4 or CerS5 in advanced CRC cells enhanced tumor formation in nude mice and organoid growth in vitro. This was accompanied by an enhanced proliferation rate and metabolic changes leading to a shift towards the Warburg effect. In contrast, CerS4 or CerS5 depletion in Caco-2 cells reduced tumor growth in vivo. Lipidomic and proteomic analysis of membrane fractions revealed significant changes in tumor-promoting cellular pathways and cellular transporters. This study identifies CerS4 and CerS5 as prognostic markers for advanced colon cancer patients and provides a comprehensive overview about the associated cellular metabolic changes. We propose that the expression level of CerS4 and CerS5 in colon tumors could serve as a basis for decision-making for personalized treatment of advanced colon cancer patients. Trial registration: The study was accredited by the study board of the Deutsche Krebsgesellschaft (Registration No: St-D203, 2017/06/30, retrospectively registered).

Unraveling TIMP1: a multifaceted biomarker in colorectal cancer

Background: The pathogenic genes of colorectal cancer (CRC) have not yet been fully elucidated, and there is currently a lack of effective therapeutic targets. This study used bioinformatics methods to explore and experimentally validate the most valuable biomarkers for colorectal cancer and further investigate their potential as targets. Methods: We analyzed differentially expressed genes (DEGs) based on the Gene Expression Omnibus (GEO) dataset and screened out hub genes. ROC curve and univariate Cox analysis of The Cancer Genome Atlas (TCGA) dataset revealed the most diagnostically and prognostically valuable genes. Immunohistochemistry (IHC) experiments were then conducted to validate the expression level of these selected genes in colorectal cancer. Gene set enrichment analysis (GSEA) was performed to evaluate the enriched signaling pathways associated with the gene. Using the CIBERSORT algorithm in R software, we analyzed the immune infiltrating cell abundance in both high and low gene expression groups and examined the gene's correlation with immune cells and immune checkpoints. Additionally, we performed drug sensitivity analysis utilizing the DepMap database, and explored the correlation between gene expression levels and ferroptosis based on the The Cancer Genome Atlas dataset. Results: The study identified a total of 159 DEGs, including 7 hub genes: SPP1, MMP1, CXCL8, CXCL1, TIMP1, MMP3, and CXCL10. Further analysis revealed TIMP1 as the most valuable diagnostic and prognostic biomarker for colorectal cancer, with IHC experiments verifying its high expression. Additionally, GSEA results showed that the high TIMP1 expression group was involved in many cancer signaling pathways. Analysis of the TCGA database revealed a positive correlation between TIMP1 expression and infiltration of macrophages (M0, M1, M2) and neutrophils, as well as the expression of immune checkpoint genes, including CTLA-4 and HAVCR2. Drug sensitivity analysis, conducted using the DepMap database, revealed that colorectal cancer cell lines exhibiting elevated levels of TIMP1 expression were more responsive to certain drugs, such as CC-90003, Pitavastatin, Atuveciclib, and CT7001, compared to those with low levels of TIMP1. Furthermore, TIMP1 expression was positively correlated with that of ferroptosis-related genes, such as GPX4 and HSPA5. Conclusion: TIMP1 can be used as a biomarker for colorectal cancer and is associated with the immunological microenvironment, drug sensitivity, and ferroptosis inhibition in this disease.

An immunogenic cell death-related classification predicts prognosis and response to immunotherapy in kidney renal clear cell carcinoma

Introduction

Immunogenic cell death (ICD) is a form of regulated cell death that activates an adaptive immune response in an immunocompetent host and is particularly sensitive to antigens from tumor cells. Kidney clear cell carcinoma (KIRC) is an immunogenic tumor with extensive tumor heterogeneity. However, no reliable predictive biomarkers have been identified to reflect the immune microenvironment and therapeutic response of KIRC.

Methods

Therefore, we used the CIBERSORT and ESTIMATE algorithms to define three ICD clusters based on the expression of ICD-related genes in 661 KIRC patients. Subsequently, we identified three different ICD gene clusters based on the overlap of differentially expressed genes (DEGs) within the ICD clusters. In addition, principal component analysis (PCA) was performed to calculate the ICD scores.

Results

The results showed that patients with reduced ICD scores had a poorer prognosis and reduced transcript levels of immune checkpoint genes regulated with T cell differentiation. Furthermore, the ICD score was negatively correlated with the tumor mutation burden (TMB) value of KICD. patients with higher ICD scores showed clinical benefits and advantages of immunotherapy, indicating that the ICD score is an accurate and valid predictor to assess the effect of immunotherapy.

Discussion

Overall, our study presents a comprehensive KICD immune-related ICD landscape that can provide guidance for current immunotherapy and predict patient prognosis to help physicians make judgments about the patient's disease and treatment modalities, and can guide current research on immunotherapy strategies for KICD.

A tumor microenvironment-based classification of gastric cancer for more effective diagnosis and treatment

With approximately one million diagnosed cases and over 700,000 deaths recorded annually, gastric cancer (GC) is the third most common cause of cancer-related deaths worldwide. GC is a heterogeneous tumor. Thus, optimal management requires biomarkers of prognosis, treatment selection, and treatment response. The Cancer Genome Atlas program sub-classified GC into molecular subtypes, providing a framework for treatment personalization using traditional chemotherapies or biologics. Here, we report a comprehensive study of GC vascular and immune tumor microenvironment (TME)-based on stage and molecular subtypes of the disease and their correlation with outcomes. Using tissues and blood circulating biomarkers and a molecular classification, we identified cancer cell and tumor archetypes, which show that the TME evolves with the disease stage and is a major determinant of prognosis. Moreover, our TME-based subtyping strategy allowed the identification of archetype-specific prognostic biomarkers such as CDH1-mutant GC and circulating IL-6 that provided information beyond and independent of TMN staging, MSI status, and consensus molecular subtyping. The results show that integrating molecular subtyping with TME-specific biomarkers could contribute to improved patient prognostication and may provide a basis for treatment stratification, including for contemporary anti-angiogenesis and immunotherapy approaches.

Exploring the Role of Circulating Cell-Free RNA in the Development of Colorectal Cancer

Circulating tumor RNA (ctRNA) has recently emerged as a novel and attractive liquid biomarker. CtRNA is capable of providing important information about the expression of a variety of target genes noninvasively, without the need for biopsies, through the use of circulating RNA sequencing. The overexpression of cancer-specific transcripts increases the tumor-derived RNA signal, which overcomes limitations due to low quantities of circulating tumor DNA (ctDNA). The purpose of this work is to present an up-to-date review of current knowledge regarding ctRNAs and their status as biomarkers to address the diagnosis, prognosis, prediction, and drug resistance of colorectal cancer. The final section of the article discusses the practical aspects involved in analyzing plasma ctRNA, including storage and isolation, detection technologies, and their limitations in clinical applications.

N6-methyladenosine related gene expression signatures for predicting the overall survival and immune responses of patients with colorectal cancer

N6-methyladenosine (m6A) modification has been demonstrated to exhibit a crucial prognostic effect on colorectal cancer (CRC). Nonetheless, potential mechanism of m6A in survival rate and immunotherapeutic response remains unknown. Here we investigated the genes associated with m6A regulators and developed a risk score for predicting the overall survival (OS) of CRC patients. RNA-seq transcriptomic profiling data of COAD/READ samples were obtained from The Cancer Genome Atlas (TCGA) database. Absolute Shrinkage and Selection Operator (LASSO)- Cox regression analysis was conducted to identify the m6A-related gene expression signatures and the selected genes were inputted into stepwise regression to develop a prognostic risk score in TCGA, and its predictive performance of CRC survival was further validated in Gene Expression Omnibus (GEO) datasets. According to our results, the risk score comprising 18 m6A-related mRNAs was significantly associated with CRC survival in both TCGA and GEO datasets. And the stratified analysis also confirmed that high-risk score acted as a poor factor in different age, sex, T stage, and tumour, node, metastasis (TNM) stages. The m6A-related prognostic score in combination with clinical characteristics yielded time-dependent area under the receiver operating characteristic curve (AUCs) of 0.85 (95%CI: 0.79-0.91), 0.84 (95%CI: 0.79-0.90) and 0.80 (95%CI: 0.71-0.88) for the prediction of the 1-, 3-, 5-year OS of CRC in TCGA cohort. Furthermore, mutation of oncogenes occurred more frequently in the high-risk group and the composition of immune cells in tumour microenvironment (TME) was significantly distinct between the low- and high-risk groups. The low-risk group had a lower microsatellite instability (MSI) score, T-cell exclusion score and dysfunction score, implying that low-risk patients may have a better immunotherapy response than high-risk patients. In summary, a prognostic risk score derived from m6A-related gene expression signatures could serve as a potential prognostic predictor for CRC survival and indicator for predicting immunotherapy response in CRC patients.

A Novel Molecular Analysis Approach in Colorectal Cancer Suggests New Treatment Opportunities

Colorectal cancer (CRC) is a molecular and clinically heterogeneous disease. In 2015, the Colorectal Cancer Subtyping Consortium classified CRC into four consensus molecular subtypes (CMS), but these CMS have had little impact on clinical practice. The purpose of this study is to deepen the molecular characterization of CRC. A novel approach, based on probabilistic graphical models (PGM) and sparse k-means-consensus cluster layer analyses, was applied in order to functionally characterize CRC tumors. First, PGM was used to functionally characterize CRC, and then sparse k-means-consensus cluster was used to explore layers of biological information and establish classifications. To this aim, gene expression and clinical data of 805 CRC samples from three databases were analyzed. Three different layers based on biological features were identified: adhesion, immune, and molecular. The adhesion layer divided patients into high and low adhesion groups, with prognostic value. The immune layer divided patients into immune-high and immune-low groups, according to the expression of immune-related genes. The molecular layer established four molecular groups related to stem cells, metabolism, the Wnt signaling pathway, and extracellular functions. Immune-high patients, with higher expression of immune-related genes and genes involved in the viral mimicry response, may benefit from immunotherapy and viral mimicry-related therapies. Additionally, several possible therapeutic targets have been identified in each molecular group. Therefore, this improved CRC classification could be useful in searching for new therapeutic targets and specific therapeutic strategies in CRC disease.

Identification of Aging and Young Subtypes for Predicting Colorectal Cancer Prognosis and Immunotherapy Responses

Colorectal cancer (CRC) is critically related to aging and severely threatens human lives. To better explore the effects of aging on CRC progression and therapy outcome, a reliable aging subtypes identification of CRC is urgently desired. Here, 28 aging-related genes associated with the CRC prognosis were selected by univariate Cox analyses. Based on these 28 genes, CRC patients were divided into the aging subtype and young subtype by non-negative matrix factorization clustering. Aging subtype and young subtype of CRC were identified with distinct molecular features and clinical prognosis. The aging subtype was characterized by upregulation of senescence-associated secretory phenotype, higher frequencies of TP53 and immune checkpoint molecules, and high sensitivity to protein kinase and angiogenesis inhibitors. Furthermore, 14 genes were selected by LASSO penalized Cox regression analyses for aging-related risk signature construction. The constructed aging risk signature exhibited good prediction and the nomogram showed robust discrimination power over the traditional CRC staging system. In conclusion, this study successfully established aging subtype and young subtype of CRC, which is helpful to identify patients with aging characteristics to evaluate prognosis and treatment outcomes. Introducing aging-based subtypes into clinical concern and patient prognostication provides new opportunities for personalized CRC treatment.

Transposon-activated POU5F1B promotes colorectal cancer growth and metastasis

The treatment of colorectal cancer (CRC) is an unmet medical need in absence of early diagnosis. Here, upon characterizing cancer-specific transposable element-driven transpochimeric gene transcripts (TcGTs) produced by this tumor in the SYSCOL cohort, we find that expression of the hominid-restricted retrogene POU5F1B through aberrant activation of a primate-specific endogenous retroviral promoter is a strong negative prognostic biomarker. Correlating this observation, we demonstrate that POU5F1B fosters the proliferation and metastatic potential of CRC cells. We further determine that POU5F1B, in spite of its phylogenetic relationship with the POU5F1/OCT4 transcription factor, is a membrane-enriched protein that associates with protein kinases and known targets or interactors as well as with cytoskeleton-related molecules, and induces intracellular signaling events and the release of trans-acting factors involved in cell growth and cell adhesion. As POU5F1B is an apparently non-essential gene only lowly expressed in normal tissues, and as POU5F1B-containing TcGTs are detected in other tumors besides CRC, our data provide interesting leads for the development of cancer therapies.

Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer

The consensus molecular subtype (CMS) classification of colorectal cancer is based on bulk transcriptomics. The underlying epithelial cell diversity remains unclear. We analyzed 373,058 single-cell transcriptomes from 63 patients, focusing on 49,155 epithelial cells. We identified a pervasive genetic and transcriptomic dichotomy of malignant cells, based on distinct gene expression, DNA copy number and gene regulatory network. We recapitulated these subtypes in bulk transcriptomes from 3,614 patients. The two intrinsic subtypes, iCMS2 and iCMS3, refine CMS. iCMS3 comprises microsatellite unstable (MSI-H) cancers and one-third of microsatellite-stable (MSS) tumors. iCMS3 MSS cancers are transcriptomically more similar to MSI-H cancers than to other MSS cancers. CMS4 cancers had either iCMS2 or iCMS3 epithelium; the latter had the worst prognosis. We defined the intrinsic epithelial axis of colorectal cancer and propose a refined ‘IMF’ classification with five subtypes, combining intrinsic epithelial subtype (I), microsatellite instability status (M) and fibrosis (F).

A single-cell transcriptomic analysis of 63 patients with colorectal cancer classifies tumor cells into two epithelial subtypes. An improved tumor classification based on epithelial subtype, microsatellite stability and fibrosis reveals differences in pathway activation and metastasis.

The Immune Cell Infiltration Patterns and Characterization Score in Bladder Cancer to Identify Prognosis

Background: Bladder cancer (BLCA) is among the most frequent types of cancer. Patients with BLCA have a significant recurrence rate and a poor post-surgery survival rate. Recent research has found a link between tumor immune cell infiltration (ICI) and the prognosis of BLCA patients. However, the ICI’s picture of BLCA remains unclear.

Methods: Common gene expression data were obtained by combining the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) expression databases. Two computational algorithms were proposed to unravel the ICI landscape of BLCA patients. The R package “limma” was applied to find differentially expressed genes (DEGs). ICI patterns were defined by the unsupervised clustering method. Principal-component analysis (PCA) was used to calculate the ICI score. In addition, the combined ICI score and tumor burden mutation (TMB) were utilized to assess BLCA patients’ prognosis. The predictive value of ICI scores was verified by different clinical characteristics.

Results: A total of 569 common gene expression data were retrieved from TCGA and GEO cohorts. CD8⁺ T cells were found to have a substantial positive connection with activated memory CD4⁺ T cells and immune score. On the contrary, CD8⁺ T cells were found to have a substantial negative connection with macrophages M0. Thirty-eight DEGs were selected. Two ICI patterns were defined by the unsupervised clustering method. Patients of BLCA were separated into two groups. The high ICI score group exhibited a better outcome than the low ICI score one (p < 0.001). Finally, the group with a high tumor mutation burden (TMB) as well as a high ICI score had the best outcome. (p < 0.001).

Conclusions: Combining TMB and ICI scores resulted in a more accurate survival prediction, suggesting that ICI scores could be used as a prognostic marker for BLCA patients.

Identification of Ligand-Receptor Pairs Associated With Tumour Characteristics in Clear Cell Renal Cell Carcinoma

The tumour microenvironment (TME) of clear cell renal cell carcinoma (ccRCC) comprises multiple cell types, which promote tumour progression and modulate drug resistance and immune cell infiltrations via ligand-receptor (LR) interactions. However, the interactions, expression patterns, and clinical relevance of LR in the TME in ccRCC are insufficiently characterised. This study characterises the complex composition of the TME in ccRCC by analysing the single-cell sequencing (scRNA-seq) data of patients with ccRCC from the Gene expression omnibus database. On analysing the scRNA-seq data combined with the cancer genome atlas kidney renal clear cell carcinoma (TCGA-KIRC) dataset, 46 LR-pairs were identified that were significantly correlated and had prognostic values. Furthermore, a new molecular subtyping model was proposed based on these 46 LR-pairs. Molecular subtyping was performed in two ccRCC cohorts, revealing significant differences in prognosis between the subtypes of the two ccRCC cohorts. Different molecular subtypes exhibited different clinicopathological features, mutational, pathway, and immune signatures. Finally, the LR.score model that was constructed using ten essential LR-pairs that were identified based on LASSO Cox regression analysis revealed that the model could accurately predict the prognosis of patients with ccRCC. In addition, the differential expression of ten LR-pairs in tumour and normal cell lines was identified. Further functional experiments showed that CX3CL1 can exert anti-tumorigenic role in ccRCC cell line. Altogether, the effects of immunotherapy were connected to LR.scores, indicating that potential medications targeting these LR-pairs could contribute to the clinical benefit of immunotherapy. Therefore, this study identifies LR-pairs that could be effective biomarkers and predictors for molecular subtyping and immunotherapy effects in ccRCC. Targeting LR-pairs provides a new direction for immunotherapy regimens and prognostic evaluations in ccRCC.

Verification of genetic differences and immune cell infiltration subtypes in the neuroblastoma tumour microenvironment during immunotherapy

Background

Improved understanding of the tumour microenvironment (TME) has enabled remarkable advancements in research on cancer progression in the past few years. It is crucial to understand the nature and function of the TME because precise treatment strategies, including immunotherapy, for managing specific cancers have received widespread attention. The immune infiltrative profiles of neuroblastoma (NB) have not yet been completely illustrated. The purpose of this research was to analyse tumour immune cell infiltration (ICI) in the microenvironment of NB.

Methods

We applied the CIBERSORT and ESTIMATE algorithms to evaluate the ICI status of 438 NB samples. Three ICI models were selected, and ICI scores were acquired. Subgroups with high ICI scores determined based on the presence of immune activation signalling pathways had better overall survival.

Results

Genes involved in the immunosuppressive heparan sulphate glycosaminoglycan biosynthesis signalling pathway were markedly enriched in the low ICI score subgroup. It was inferred that patients with high ICI NB subtypes were more likely to respond to immunotherapy and have a better prognosis than those of patients with low ICI NB subtypes.

Conclusion

Notably, our ICI data not only provide a new clinical and theoretical basis for mining NB prognostic markers related to the microenvironment but also offer new ideas for the development of NB precision immunotherapy methods.

Integrated Analysis of Necroptosis-Related Genes for Prognosis, Immune Microenvironment Infiltration, and Drug Sensitivity in Colon Cancer

Background

Necroptosis, is intimately linked to tumor development and prognosis and has been considered as a target for anticancer therapy. However, the role of necroptosis-related genes (NRGs) in colon cancer is unclear.

Methods

In the present study, we screened 76 NRGs from previous studies and described the landscape of transcriptomic and genetic variation of NRGs in colon cancer (CC) patient samples. Molecular subtypes of necroptosis in colon cancer were identified by clustering analysis, and these molecular subtypes were linked to patient prognosis and TME cell infiltration characteristics. Then, the NRS-score for predicting overall survival (OS) was built based on the TCGA database and validated in the GSE39582 cohort for its predictive power in CC patients. Besides, the ESTIMATE and CIBERSORT algorithms were applied to explore the relationship between NRS-score and tumor immune microenvironment.

Results

We identified two molecular subtypes associated with necroptosis in CC, which have diverse prognosis and immune microenvironment characteristics. Based on the differentially expressed genes between the two molecular subtypes, we further developed a necroptosis risk score signature, referred to as NRS-score. High NRS-score was associated with poor prognosis in CC through immunosuppressive microenvironment and immune escape mechanisms. The nomogram based on NRS-score showed excellent ability to predict prognosis. In addition, NRS-score presented a positive correlation with tumor mutational burden (TMB) and immune checkpoint blockade (ICB) expression and was closely correlated with multiple anticancer agent susceptibility.

Conclusion

This work revealed a close relationship between necroptosis and the prognosis and immune microenvironment of colon cancer. The NRS-score based on the 8-gene signature may be used to predict the sensitivity of immunotherapy and chemotherapy in colon cancer patients, and provides a foundation for future studies targeting necroptosis and its immune microenvironment.

Establishment of a Dynamic Nomogram for Predicting the Risk of Lymph Node Metastasis in T1 Stage Colorectal Cancer

Background

Accurate prediction of the risk of lymph node metastasis in patients with stage T1 colorectal cancer is crucial for the formulation of treatment plans for additional surgery and lymph node dissection after endoscopic resection. The purpose of this study was to establish a predictive model for evaluating the risk of LNM in patients with stage T1 colorectal cancer.

Methods

The clinicopathological and imaging data of 179 patients with T1 stage colorectal cancer who underwent radical resection of colorectal cancer were collected. LASSO regression and a random forest algorithm were used to screen the important risk factors for LNM, and a multivariate logistic regression equation and dynamic nomogram were constructed. The C index, Calibration curve, and area under the ROC curve were used to evaluate the discriminant and prediction ability of the nomogram. The net reclassification index (NRI), comprehensive discriminant improvement index (IDI), and clinical decision curve (DCA) were compared with traditional ESMO criteria to evaluate the accuracy, net benefit, and clinical practicability of the model.

Results

The probability of lymph node metastasis in patients with T1 colorectal cancer was 11.17% (20/179). Multivariate analysis showed that the independent risk factors for LNM in T1 colorectal cancer were submucosal invasion depth, histological grade, CEA, lymphovascular invasion, and imaging results. The dynamic nomogram model constructed with independent risk factors has good discrimination and prediction capabilities. The C index was 0.914, the corrected C index was 0.890, the area under the ROC curve was 0.914, and the accuracy, sensitivity, and specificity were 93.3, 80.0, and 91.8%, respectively. The NRI, IDI, and DCA show that this model is superior to the ESMO standard.

Conclusion

This study establishes a dynamic nomogram that can effectively predict the risk of lymph node metastasis in patients with stage T1 colorectal cancer, which will provide certain help for the formulation of subsequent treatment plans for patients with stage T1 CRC after endoscopic resection.

Characteristics of the Immune Cell Infiltration Landscape in Gastric Cancer to Assistant Immunotherapy

Background: Gastric cancer (GC) was usually associated with poor prognosis and invalid therapeutical response to immunotherapy due to biological heterogeneity. It is urgent to screen reliable indices especially immunotherapy-associated parameters that can predict the therapeutic responses to immunotherapy of GC patients.

Methods: Gene expression profile of 854 GC patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets (GSE84433) with their corresponding clinical and somatic mutation data. Based on immune cell infiltration (ICI) levels, molecular clustering classification was performed to identify subtypes and ICI scores in GC patients. After functional enrichment analysis of subtypes, we further explored the correlation between ICI scores and Tumor Mutation Burden (TMB) and the significance in clinical immunotherapy response.

Results: Three subtypes were identified based on ICI scores with distinct immunological and prognostic characteristics. The ICI-cluster C, associated with better outcomes, was characterized by significantly higher stromal and immune scores, T lymphocytes infiltration and up-regulation of PD-L1. ICI scores were identified through using principal component analysis (PCA) and the low ICI scores were consistent with the increased TMB and the immune-activating signaling pathways. Contrarily, the high-ICI score cluster was involved in the immunosuppressive pathways, such as TGF-beta, MAPK and WNT signaling pathways, which might be responsible for poor prognosis of GC. External immunotherapy and chemotherapy cohorts validated the patients with lower ICI scores exhibited significant therapeutic responses and clinical benefits.

Conclusion: This study elucidated that ICI score could sever as an effective prognostic and predictive indicator for immunotherapy in GC. These findings indicated that the systematic assessment of tumor ICI landscapes and identification of ICI scores have crucial clinical implications and facilitate tailoring optimal immunotherapeutic strategies.

Colorectal Cancer Cell Differentiation Trajectory Predicts Patient Immunotherapy Response and Prognosis

Objectives

This study aimed to investigate the differentiation state and clinical significance of colorectal cancer cells, as well as to predict the immune response and prognosis of patients based on differentiation-related genes of colorectal cancer.

Introduction

Colorectal cancer cells exhibit different differentiation states under the influence of the tumor microenvironment, which determines the cell fates.

Methods

We combined single-cell sequencing (scRNA-seq) data from The Cancer Genome Atlas source with extensive transcriptome data from the Gene Expression Omnibus database. We obtained colorectal cancer differentiation-related genes using cell trajectory analysis and developed a colorectal cancer differentiation-related gene based molecular typing and prognostic model to predict the immune response and prognosis of patients with colorectal cancer.

Results

We identified 5 distinct cell differentiation subsets and 620 colorectal cancer differentiation-related genes. Colorectal cancer differentiation-related genes were significantly associated with metabolism, angiogenesis, and immunity. We separated patients into 3 subtypes based on colorectal cancer differentiation-related gene expression in the tumor and found differences among the different subtypes in immune infiltration status, immune checkpoint gene expression, clinicopathological features, and overall survival. Immunotherapeutic interventions involving a highly expressed immune checkpoint blockade may be selectively effective in the corresponding cancer subtypes. We built a risk score prediction model (5-year AUC: .729) consisting of the 4 most important predictors of survival (TIMP1, MMP1, LGALS4, and ITLN1). Finally, we generated and validated a nomogram consisting of the risk score and clinicopathological variables.

Conclusion

This study highlights the significance of genes involved in cell differentiation for clinical prognosis and immunotherapy in patients and provides prospective therapeutic targets for colorectal cancer.

Predictors of colorectal cancer survival using cox regression and random survival forests models based on gene expression data

Understanding and identifying the markers and clinical information that are associated with colorectal cancer (CRC) patient survival is needed for early detection and diagnosis. In this work, we aimed to build a simple model using Cox proportional hazards (PH) and random survival forest (RSF) and find a robust signature for predicting CRC overall survival. We used stepwise regression to develop Cox PH model to analyse 54 common differentially expressed genes from three mutations. RSF is applied using log-rank and log-rank-score based on 5000 survival trees, and therefore, variables important obtained to find the genes that are most influential for CRC survival. We compared the predictive performance of the Cox PH model and RSF for early CRC detection and diagnosis. The results indicate that SLC9A8, IER5, ARSJ, ANKRD27, and PIPOX genes were significantly associated with the CRC overall survival. In addition, age, sex, and stages are also affecting the CRC overall survival. The RSF model using log-rank is better than log-rank-score, while log-rank-score needed more trees to stabilize. Overall, the imputation of missing values enhanced the model’s predictive performance. In addition, Cox PH predictive performance was better than RSF.

Intra-Tumor Heterogeneity of Colorectal Cancer Necessitates the Multi-Regional Sequencing for Comprehensive Mutational Profiling

Background

The panorama and details of quantitative intratumor heterogeneity have not been fully investigated in colorectal cancer (CRC) patients with solitary lesion without distal metastasis, and its influences on sequencing interpretation and therapeutic strategies have not been explored.

Methods

Cancer tissues and matched blood from 70 sporadic CRC patients were collected and were divided into two cohorts. Four individual tissue biopsies were obtained from each of the 47 patients (multi-sample cohort). One random cancer tissue biopsy was obtained from each of the rest 23 patients (single-sample cohort). A 10 mL of blood was collected from all patients and the circulating cell-free DNA (cfDNA) was extracted. A 605-gene panel was used for targeted sequencing with tissue and paired blood.

Results

Mutational landscape revealed significantly higher mutational frequency in APC, CARD11 and CSMD3 in multi-sample cohort than single-sample cohort (P<0.05). The number of mutations and the ratio of trunk, shared and branch mutations showed extensive heterogeneity in multi-sample cohort, and the percentage of trunk mutations in major driver genes, including APC, TP53 and KRAS, was higher than 70%. A total of 929 mutations were detected in tissue/blood in multi-sample group, with 921(99.1%) from tissue and 472(50.8%) from blood (464 common mutations,49.9%). In contrast, 394 mutations were detected in tissue/blood in single-sample group, with 231 (58.6%) from tissue and 219 (55.6%) from blood (56 common mutations, 11.9%). The number of mutations of major driver genes detected in tissue was higher than that in blood in the multi-sample cohort, while it was similar in the single-sample group. Quantification analysis revealed differential correlation between tissue and blood VAF in trunk, shared and branch mutations. Meanwhile, VAF of trunk mutations was significantly higher than shared mutations and branch mutations. VAF exhibited significant differences among distinct stages, locations, differentiation and sex status.

Conclusion

Characteristic extensive heterogeneity was revealed for solitary CRC without distal metastasis. Multi-regional biopsy was necessary for comprehensive mutation detection in CRC.

Personalized Immunotherapy in Colorectal Cancers: Where Do We Stand?

Colorectal cancer (CRC) is the second leading cause of cancer death in the world. Immunotherapy using monoclonal antibodies, immune-checkpoint inhibitors, adoptive cell therapy, and cancer vaccines has raised great hopes for treating poor prognosis metastatic CRCs that are resistant to the conventional therapies. However, high inter-tumor and intra-tumor heterogeneity hinder the success of immunotherapy in CRC. Patients with a similar tumor phenotype respond differently to the same immunotherapy regimen. Mutation-based classification, molecular subtyping, and immunoscoring of CRCs facilitated the multi-aspect grouping of CRC patients and improved immunotherapy. Personalized immunotherapy using tumor-specific neoantigens provides the opportunity to consider each patient as an independent group deserving of individualized immunotherapy. In the recent decade, the development of sequencing and multi-omics techniques has helped us classify patients more precisely. The expansion of such advanced techniques along with the neoantigen-based immunotherapy could herald a new era in treating heterogeneous tumors such as CRC. In this review article, we provided the latest findings in immunotherapy of CRC. We elaborated on the heterogeneity of CRC patients as a bottleneck of CRC immunotherapy and reviewed the latest advances in personalized immunotherapy to overcome CRC heterogeneity.

Pyroptosis-related gene mediated modification patterns and immune cell infiltration landscapes in cutaneous melanoma to aid immunotherapy

Tumor occurrence, infiltration, and metastasis are significantly affected by the tumor microenvironment (TME). Increasing evidence has elucidated TME's clinical significance in prognostic assessment and immunotherapy efficacy. Nonetheless, no studies have reported the potential pyroptosis-related genes (PRGs) function in TME immune cell infiltration. In this study, we systematically analyzed different PRG modification patterns in 685 cutaneous melanoma (CM) cases. We comprehensively explored the relationship between these PRG modification patterns and TME cell infiltration characteristics. Then, we used principal component analysis to construct a pyroptosis scoring system to quantify the PRG modification patterns in each CM patient. Three different PRG modification patterns were identified. Pyroptosis score was confirmed as an independent prognostic factor for CM patients. High pyroptosis score was characterized by high immunophenscore and more lymphocytes infiltration, such as T, B, and NK cells - indicating a strong ability to monitor and clear tumors, which may be responsible for the advantageous survival. Three independent cohorts that received immunotherapy confirmed the significant therapeutic efficacy and clinical benefit in high pyroptosis scores patients. This study revealed that the PRG modification patterns have a crucial effect on the CM complex and diverse microenvironment. Pyroptosis scores might serve as credible predictors of immunotherapy response and prognostic assessment. This provides a new direction for personalized immunotherapy strategies and appropriate immunotherapy candidates screening.

Identifying Molecular Biomarkers for Diseases With Machine Learning Based on Integrative Omics

Molecular biomarkers are certain molecules or set of molecules that can be of help for diagnosis or prognosis of diseases or disorders. In the past decades, thanks to the advances in high-throughput technologies, a huge amount of molecular 'omics' data, e.g., transcriptomics and proteomics, have been accumulated. The availability of these omics data makes it possible to screen biomarkers for diseases or disorders. Accordingly, a number of computational approaches have been developed to identify biomarkers by exploring the omics data. In this review, we present a comprehensive survey on the recent progress of identification of molecular biomarkers with machine learning approaches. Specifically, we categorize the machine learning approaches into supervised, un-supervised and recommendation approaches, where the biomarkers including single genes, gene sets and small gene networks. In addition, we further discuss potential problems underlying bio-medical data that may pose challenges for machine learning, and provide possible directions for future biomarker identification.

Everolimus and plicamycin specifically target chemoresistant colorectal cancer cells of the CMS4 subtype

Colorectal cancers (CRC) can be classified into four consensus molecular subtypes (CMS), among which CMS1 has the best prognosis, contrasting with CMS4 that has the worst outcome. CMS4 CRC is notoriously resistant against therapeutic interventions, as demonstrated by preclinical studies and retrospective clinical observations. Here, we report the finding that two clinically employed agents, everolimus (EVE) and plicamycin (PLI), efficiently target the prototypic CMS4 cell line MDST8. As compared to the prototypic CMS1 cell line LoVo, MDST8 cells treated with EVE or PLI demonstrated stronger cytostatic and cytotoxic effects, increased signs of apoptosis and autophagy, as well as a more pronounced inhibition of DNA-to-RNA transcription and RNA-to-protein translation. Moreover, nontoxic doses of EVE and PLI induced the shrinkage of MDST8 tumors in mice, yet had only minor tumor growth-reducing effects on LoVo tumors. Altogether, these results suggest that EVE and PLI should be evaluated for their clinical activity against CMS4 CRC.

A novel immune subtype classification of ER-positive, PR-negative and HER2-negative breast cancer based on the genomic and transcriptomic landscape

BACKGROUND

The diversity and plasticity behind ER+/PR-/HER2- breast cancer have not been widely explored. It is essential to identify heterogeneous microenvironment phenotypes and investigate specific genomic events driving the formation of these phenotypes.

METHODS

Based on the immune-related gene expression profiles of 411 ER+/PR-/HER2- breast cancers in the METABRIC cohort, we used consensus clustering to identify heterogeneous immune subtypes and assessed their reproducibility in an independent meta-cohort including 135 patients collected from GEO database. We further analyzed the differences of cellular and molecular characteristics, and potential immune escape mechanism among immune subtypes. In addition, we constructed a transcriptional trajectory to visualize the distribution of individual patient.

RESULTS

Our analysis identified and validated five reproducible immune subtypes with distinct cellular and molecular characteristics, potential immune escape mechanisms, genomic drivers, as well as clinical outcomes. An immune-cold subtype, with the least amount of lymphocyte infiltration, had a poorer prognosis. By contrast, an immune-hot subtype, which demonstrated the highest infiltration of CD8+ T cells, DCs and NK cells, and elevated IFN-γ response, had a comparatively favorable prognosis. Other subtypes showed more diverse gene expression and immune infiltration patterns with distinct clinical outcomes. Finally, our analysis revealed a complex immune landscape consisting of both discrete cluster and continuous spectrum.

CONCLUSION

Overall, this study revealed five heterogeneous immune subtypes among ER+/PR-/HER2- breast cancer, also provided important implications for clinical translations.

Characterization of the immune cell infiltration landscape in bladder cancer to aid immunotherapy

The tumor microenvironment (TME) is composed mainly of tumor cells, tumorinfiltrating immune cells, and matrix components. Recent clinical studies have indicated that tumor immune cell infiltration (ICI) is related to the sensitivity of immunotherapy and the prognosis of patients with bladder cancer (BC). Nevertheless, up to now, the landscape of immune infiltration in BC has not been clearly defined. Here we present two algorithms to reveal the landscape of ICI in 277 cases of BC. Two kinds of ICI patterns were established, and ICI scores were based on the analysis of the main components. In sub-types with high ICI scores, we found highly expressed immunecheckpoint and activated transforming growth factor b and WNT signal pathways. These might be the cause of poor prognosis. A low ICI score indicated a better prognosis. Our study showed that ICI scores in immunotherapy could be a valid biomarker for the prognosis of patients and a predictive indicator. The evaluation of ICI patterns of a larger cohort of samples would expand our cognition of TME, and the present study might guide the strategies of immunotherapy for patients with BC.

Identification of Distinct Molecular Patterns and a Four-Gene Signature in Colon Cancer Based on Invasion-Related Genes

Background

The pathological stage of colon cancer cannot accurately predict recurrence, and to date, no gene expression characteristics have been demonstrated to be reliable for prognostic stratification in clinical practice, perhaps because colon cancer is a heterogeneous disease. The purpose was to establish a comprehensive molecular classification and prognostic marker for colon cancer based on invasion-related expression profiling.

Methods

From the Gene Expression Omnibus (GEO) database, we collected two microarray datasets of colon cancer samples, and another dataset was obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) further underwent univariate analysis, least absolute shrinkage, selection operator (LASSO) regression analysis, and multivariate Cox survival analysis to screen prognosis-associated feature genes, which were further verified with test datasets.

Results

Two molecular subtypes (C1 and C2) were identified based on invasion-related genes in the colon cancer samples in TCGA training dataset, and C2 had a good prognosis. Moreover, C1 was more sensitive to immunotherapy. A total of 1,514 invasion-related genes, specifically 124 downregulated genes and 1,390 upregulated genes in C1 and C2, were identified as DEGs. A four-gene prognostic signature was identified and validated, and colon cancer patients were stratified into a high-risk group and a low-risk group. Multivariate regression analyses and a nomogram indicated that the four-gene signature developed in this study was an independent predictive factor and had a relatively good predictive capability when adjusting for other clinical factors.

Conclusion

This research provided novel insights into the mechanisms underlying invasion and offered a novel biomarker of a poor prognosis in colon cancer patients.

Immune cell infiltration-associated signature in colon cancer and its prognostic implications

Tumor immune cell infiltration (ICI) has been reported in various studies to be correlated with tumor diagnosis, clinical treatment sensitivity and prognosis. It is an important direction to study the characteristics of immune cell infiltration and develop new prognostic markers to improve the treatment of colon cancer. In this paper, we systematically analyzed the ICI characteristics and obtained three ICI clusters. Then, the ICI scores were constructed and its prognostic implications were discussed. From the results, the ICI score patterns were linked to a great survival difference (p<0.001). A high ICI score was characterized by a higher fraction of plasma cells, CD8⁺ T cells, memory resting CD4⁺ T cells, monocytes, eosinophils and dendritic cells, which had better prognosis. Macrophages and neutrophils were increased in low ICI score patients with decreased overall survival. Immune checkpoint molecules (PDCD1, CD274, LAG3, IDO1, CTLA-4, TIGHT and HAVCR2) were found to be significantly overexpressed in the low ICI score subgroup. In addition, we also studied the correlation between the tumor mutation burden (TMB) and ICI score. This study indicated the ICI score could serve as a potential prognostic biomarker for colon cancer patients’ immunotherapy.

Analyzing the characteristics of immune cell infiltration in lung adenocarcinoma via bioinformatics to predict the effect of immunotherapy

Recent studies have shown that tumor immune cell infiltration (ICI) is associated with immunotherapy sensitivity and the prognosis of lung adenocarcinoma (LUAD). However, the immunoinfiltrative landscape of LUAD has not been elucidated. We propose two computational algorithms to unravel the ICI landscape to evaluate the efficacy of immunotherapy in LUAD patients. The raw data of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were analyzed. After merging these datasets and removing the batch differences, we used the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm to obtain the immune cell content of all the samples. The unsupervised consistency clustering algorithm was used to analyze the ICI subtypes, and three subgroups were obtained. In addition, the unsupervised consistency clustering algorithm was used to analyze the differentially expressed genes (DEGs) of the ICI subtypes and obtain three ICI gene clusters. Finally, the ICI score was determined by using principal component analysis (PCA) for the gene signature. The ICI score of LUAD patients ranged from - 32.26 to 12.89 and represents the prognosis and the response to immunotherapy. High ICI scores were characterized by the T cell receptor signaling pathway, B cell receptor signaling pathway, and natural killer cell-mediated cytotoxicity, suggesting that some immune cells were activated and had increased activity, which may be the cause of the better prognosis for patients with high ICI scores. Additionally, patients with higher ICI scores showed a significant immune therapeutic advantage and clinical benefit. This study shows that the ICI score may be a potent prognostic biomarker and predictor of therapy with immune checkpoint inhibitors.

Prognostic value of tumor immune cell infiltration patterns in colon adenocarcinoma based on systematic bioinformatics analysis

Background

Although immunotherapy for colon cancer has made promising progress, only a few patients currently benefit from it. A recent study revealed that infiltrating immune cells are highly relevant to tumor prognosis and influence the expression of immune-related genes. However, the characterization of immune cell infiltration (ICI) has not yet been comprehensively analyzed and quantified in colon adenocarcinoma (COAD).

Methods

The multiomic data of COAD samples were downloaded from TCGA. ESTIMATE algorithm, ssGSEA method and CIBERSORT analysis were conducted to estimate the subpopulations of infiltrating immune cells. COAD subtypes based on ICI pattern were identified by consensus clustering then principal-component analysis was performed to obtain ICI scores to quantify the ICI patterns in individual tumors. Kaplan–Meier analysis was employed to validate prognostic value. Gene set enrichment analysis (GSEA) was applied for functional annotation. Finally, the mutation data was analyzed by employing “maftools” package.

Results

Three bioinformatics algorithms were used to evaluate the ICI patterns from 538 patients with COAD. Two ICI subtypes were determined using consensus clustering, and the ICI score was constructed by performing principal component analysis. Our findings showed that a higher ICI score often indicated a more advanced tumor and worse prognosis. The high-ICI score subgroup had a higher stromal score and more M0 macrophages but fewer plasma cells and decreased CD8 T cell infiltration. In addition, patients with high ICI scores had significantly higher expression levels of HAVCR2 and PCDC1LG2. Real-time polymerase chain reaction (PCR) was conducted to determine the prognostic significances of ICI-related genes.

Conclusions

In conclusion, ICI score may be considered as an original and useful indicator for independent prognostic prediction and individual immune-related therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02048-x.

A quantitative score of immune cell infiltration predicts the prognosis in pancreatic ductal adenocarcinoma

Pancreatic Ductal Adenocarcinoma (PDAC) is characterized by an extensive and dense fibrous stroma, which plays an active role in tumor growth and metastasis. Despite the growing importance of the tumor microenvironment in PDAC prognosis, the immune cell infiltration landscape of PDAC has not been elucidated. In this study, we applied a credible computational algorithm to comprehensively estimate the immune cell infiltration (ICI) patterns of 876 PDAC patients. Two ICI phenotypes were identified, and a ICIscore was constructed using ssGSEA algorithm. The ICIscore could significantly predict the prognosis and chemotherapy benefit of PDAC patients in both the discovery and the five validation cohorts. Multivariate cox analysis also identified the independent predictive role of the ICIscore in PDAC prognosis. A high ICIscore subtype was characterized by immune-active signaling pathways and anti-tumor immunity while a low ICIscore subtype was associated with tumor progressive signaling pathways. Four immunotherapy cohorts further supported the use of the ICIscore as a prognostic biomarker for patients receiving immune checkpoint inhibitors in other cancer types. The ICIscore reveals a close relationship between the ICI environment and prognosis and may provide new treatment strategies for PDAC patients.

Clinical Value of Consensus Molecular Subtypes in Colorectal Cancer: A Systematic Review and Meta-Analysis

Background

The consensus molecular subtypes (CMSs) of colorectal cancer (CRC) capture tumor heterogeneity at the gene-expression level. Currently, a restricted number of molecular features are used to guide treatment for CRC. We summarize the evidence on the clinical value of the CMSs.

Methods

We systematically identified studies in Medline and Embase that evaluated the prognostic and predictive value of CMSs in CRC patients. A random-effect meta-analysis was performed on prognostic data. Predictive data were summarized.

Results

In local disease, CMS4 tumors were associated with worse overall survival (OS) compared with CMS1 (hazard ratio [HR] = 3.28, 95% confidence interval = 1.27 to 8.47) and CMS2 cancers (HR = 2.60, 95% confidence interval = 1.93 to 3.50). In metastatic disease, CMS1 consistently had worse survival than CMS2-4 (OS HR range = 0.33-0.55; progression-free survival HR range = 0.53-0.89). Adjuvant chemotherapy in stage II and III CRC was most beneficial for OS in CMS2 and CMS3 (HR range = 0.16-0.45) and not effective in CMS4 tumors. In metastatic CMS4 cancers, an irinotecan-based regimen improved outcome compared with oxaliplatin (HR range = 0.31-0.72). The addition of bevacizumab seemed beneficial in CMS1, and anti-epidermal growth factor receptor therapy improved outcome for KRAS wild-type CMS2 patients.

Conclusions

The CMS classification holds clear potential for clinical use in predicting both prognosis and response to systemic therapy, which seems to be independent of the classifier used. Prospective studies are warranted to support implementation of the CMS taxonomy in clinical practice.

Characterization of the Immune Cell Infiltration Profile in Pancreatic Carcinoma to Aid in Immunotherapy

The tumor microenvironment (TME) is comprised of tumor cells, infiltrating immune cells, and stroma. Multiple reports suggest that the immune cell infiltration (ICI) in TME is strongly associated with responsiveness to immunotherapy and prognosis of certain cancers. Thus far, the ICI profile of pancreatic carcinoma (PC) remains unclear. Here, we employed two algorithms to characterize the ICI profile of PC patients. Based on our results, we identified 2 ICI patterns and calculated the ICI score by using principal component analysis. Furthermore, we revealed that patients with low ICI scores had a better prognosis, compared to high ICI scores. Moreover, we discovered that a low tumor mutation burden (TMB) offered better overall survival (OS), relative to high TMB. In this study, a high ICI score referred to elevated PD-L1/TGF-β levels, increased activation of cell cycle pathway and DNA repair pathway, as well as reduced expression of immune-activation-related genes. We also demonstrated that three metabolic pathways were suppressed in the low ICI score group. These data may explain why a high ICI score equates to a poor prognosis. Based on our analysis, the ICI score can be used as an effective predictor of PC prognosis. Hence, establishing an ICI profile, based on a large patient population, will not only enhance our knowledge of TME but also aid in the development of immunotherapies specific to PC.

Transcriptome integration analysis and specific diagnosis model construction for Hodgkin's lymphoma, diffuse large B-cell lymphoma, and mantle cell lymphoma

Transcriptome differences between Hodgkin's lymphoma (HL), diffuse large B-cell lymphoma (DLBCL), and mantle cell lymphoma (MCL), which are all derived from B cell, remained unclear. This study aimed to construct lymphoma-specific diagnostic models by screening lymphoma marker genes. Transcriptome data of HL, DLBCL, and MCL were obtained from public databases. Lymphoma marker genes were screened by comparing cases and controls as well as the intergroup differences among lymphomas. A total of 9 HL marker genes, 7 DLBCL marker genes, and 4 MCL marker genes were screened in this study. Most HL marker genes were upregulated, whereas DLBCL and MCL marker genes were downregulated compared to controls. The optimal HL-specific diagnostic model contains one marker gene (MYH2) with an AUC of 0.901. The optimal DLBCL-specific diagnostic model contains 7 marker genes (LIPF, CCDC144B, PRO2964, PHF1, SFTPA2, NTS, and HP) with an AUC of 0.951. The optimal MCL-specific diagnostic model contains 3 marker genes (IGLV3-19, IGKV4-1, and PRB3) with an AUC of 0.843. The present study reveals the transcriptome data-based differences between HL, DLBCL, and MCL, when combined with other clinical markers, may help the clinical diagnosis and prognosis.

Metal-Organic Framework Hybrids Aid Metabolic Profiling for Colorectal Cancer

Colorectal cancer (CRC) is the third most common fatal cancer worldwide, accounting for ≈10% of cancer-related mortality. Metabolic shift occurs from the very early stage during the development of CRC, which is of significant etiological and diagnostic importance toward precision medicine. Here, an advanced molecular tool to characterize the metabolic alterations in CRC, based on metal-organic framework (MOF) hybrids is reported. Consuming only 500 nL of plasma without any sample pretreatment, MOF hybrids yield direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds. A diagnostic prediction model by a machine learning algorithm is constructed, to discriminate CRC patients from normal controls with an average area under the curve of 0.947 for the discovery cohort and 0.912 for the independent validation cohort. In addition, CRC-specific metabolic signature consisting of 34 potential biomarkers, based on the aforementioned diagnostic model is identified. The results advance the design of nanomaterial-based platforms for metabolic analysis and establish a new liquid biopsy tool for CRC screening compatible with the current clinical workflow in practice.

Plasmonic Alloys Reveal a Distinct Metabolic Phenotype of Early Gastric Cancer

Gastric cancer (GC) is a multifactorial process, accompanied by alterations in metabolic pathways. Non-invasive metabolic profiling facilitates GC diagnosis at early stage leading to an improved prognostic outcome. Herein, mesoporous PdPtAu alloys are designed to characterize the metabolic profiles in human blood. The elemental composition is optimized with heterogeneous surface plasmonic resonance, offering preferred charge transfer for photoinduced desorption/ionization and enhanced photothermal conversion for thermally driven desorption. The surface structure of PdPtAu is further tuned with controlled mesopores, accommodating metabolites only, rather than large interfering compounds. Consequently, the optimized PdPtAu alloy yields direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds, consuming 500 nL of native plasma. A distinct metabolic phenotype is revealed for early GC by sparse learning, resulting in precise GC diagnosis with an area under the curve of 0.942. It is envisioned that the plasmonic alloy will open up a new era of minimally invasive blood analysis to improve the surveillance of cancer patients in the clinical setting.

Proteomic Analyses of Fibroblast- and Serum-Derived Exosomes Identify QSOX1 as a Marker for Non-invasive Detection of Colorectal Cancer

Simple Summary

Early diagnosis of colorectal cancer (CRC) is crucial to improve patient outcomes. The tumour microenvironment immediately adapts to malignant transformations, including the activation of fibroblasts in the connective tissue nearby. In this study, we investigated fibroblast activity-related protein secretion via extracellular vesicles (EVs). QSOX1, a protein identified to be significantly reduced in activated fibroblasts and derived EVs, was also found to be significantly reduced in circulating blood plasma EVs of CRC patients as compared to control patients. Hence, blood plasma EV-associated QSOX1 represents a promising platform for diagnostic CRC screening.

Abstract

The treatment of colorectal cancer (CRC) has improved during the last decades, but methods for crucial early diagnosis are yet to be developed. The influence of the tumour microenvironment on liquid biopsies for early cancer diagnostics are gaining growing interest, especially with emphasis on exosomes (EXO), a subgroup of extracellular vesicles (EVs). In this study, we established paired cancer-associated (CAFs) and normal fibroblasts (NF) from 13 CRC patients and investigated activation status-related protein abundance in derived EXOs. Immunohistochemical staining of matched patient tissue was performed and an independent test cohort of CRC patient plasma-derived EXOs was assessed by ELISA. A total of 11 differentially abundant EV proteins were identified between NFs and CAFs. In plasma EXOs, the CAF-EXO enriched protein EDIL3 was elevated, while the NF-EXO enriched protein QSOX1 was diminished compared to whole plasma. Both markers were significantly reduced in patient-matched CRC tissue compared to healthy colon tissue. In an independent test cohort, a significantly reduced protein abundance of QSOX1 was observed in plasma EXOs from CRC patients compared to controls and diagnostic ROC curve analysis revealed an AUC of 0.904. In conclusion, EXO-associated QSOX1 is a promising novel marker for early diagnosis and non-invasive risk stratification in CRC.

Proteomic Analyses of Fibroblast- and Serum-Derived Exosomes Identify QSOX1 as a Marker for Non-invasive Detection of Colorectal Cancer

The treatment of colorectal cancer (CRC) has improved during the last decades, but methods for crucial early diagnosis are yet to be developed. The influence of the tumour microenvironment on liquid biopsies for early cancer diagnostics are gaining growing interest, especially with emphasis on exosomes (EXO), a subgroup of extracellular vesicles (EVs). In this study, we established paired cancer-associated (CAFs) and normal fibroblasts (NF) from 13 CRC patients and investigated activation status-related protein abundance in derived EXOs. Immunohistochemical staining of matched patient tissue was performed and an independent test cohort of CRC patient plasma-derived EXOs was assessed by ELISA. A total of 11 differentially abundant EV proteins were identified between NFs and CAFs. In plasma EXOs, the CAF-EXO enriched protein EDIL3 was elevated, while the NF-EXO enriched protein QSOX1 was diminished compared to whole plasma. Both markers were significantly reduced in patient-matched CRC tissue compared to healthy colon tissue. In an independent test cohort, a significantly reduced protein abundance of QSOX1 was observed in plasma EXOs from CRC patients compared to controls and diagnostic ROC curve analysis revealed an AUC of 0.904. In conclusion, EXO-associated QSOX1 is a promising novel marker for early diagnosis and non-invasive risk stratification in CRC.

MethCORR infers gene expression from DNA methylation and allows molecular analysis of ten common cancer types using fresh-frozen and formalin-fixed paraffin-embedded tumor samples

Background

Transcriptional analysis is widely used to study the molecular biology of cancer and hold great biomarker potential for clinical patient stratification. Yet, accurate transcriptional profiling requires RNA of a high quality, which often cannot be retrieved from formalin-fixed, paraffin-embedded (FFPE) tumor tissue that is routinely collected and archived in clinical departments. To overcome this roadblock to clinical testing, we previously developed MethCORR, a method that infers gene expression from DNA methylation data, which is robustly retrieved from FFPE tissue. MethCORR was originally developed for colorectal cancer and with this study, we aim to: (1) extend the MethCORR method to 10 additional cancer types and (2) to illustrate that the inferred gene expression is accurate and clinically informative.

Results

Regression models to infer gene expression information from DNA methylation were developed for ten common cancer types using matched RNA sequencing and DNA methylation profiles (HumanMethylation450 BeadChip) from The Cancer Genome Atlas Project. Robust and accurate gene expression profiles were inferred for all cancer types: on average, the expression of 11,000 genes was modeled with good accuracy and an intra-sample correlation of R² = 0.90 between inferred and measured gene expression was observed. Molecular pathway analysis and transcriptional subtyping were performed for breast, prostate, and lung cancer samples to illustrate the general usability of the inferred gene expression profiles: overall, a high correlation of r = 0.96 (Pearson) in pathway enrichment scores and a 76% correspondence in molecular subtype calls were observed when using measured and inferred gene expression as input. Finally, inferred expression from FFPE tissue correlated better with RNA sequencing data from matched fresh-frozen tissue than did RNA sequencing data from FFPE tissue (P < 0.0001; Wilcoxon rank-sum test).

Conclusions

In all cancers investigated, MethCORR enabled DNA methylation-based transcriptional analysis, thus enabling future analysis of cancer in situations where high-quality DNA, but not RNA, is available. Here, we provide the framework and resources for MethCORR modeling of ten common cancer types, thereby widely expanding the possibilities for transcriptional studies of archival FFPE material.

Transcriptomic and proteomic intra-tumor heterogeneity of colorectal cancer varies depending on tumor location within the colorectum

Background

Intra-tumor heterogeneity (ITH) of colorectal cancer (CRC) complicates molecular tumor classification, such as transcriptional subtyping. Differences in cellular states, biopsy cell composition, and tumor microenvironment may all lead to ITH. Here we analyze ITH at the transcriptomic and proteomic levels to ascertain whether subtype discordance between multiregional biopsies reflects relevant biological ITH or lack of classifier robustness. Further, we study the impact of tumor location on ITH.

Methods

Multiregional biopsies from stage II and III CRC tumors were analyzed by RNA sequencing (41 biopsies, 14 tumors) and multiplex immune protein analysis (89 biopsies, 29 tumors). CRC subtyping was performed using consensus molecular subtypes (CMS), CRC intrinsic subtypes (CRIS), and TUMOR types. ITH-scores and network maps were defined to determine the origin of heterogeneity. A validation cohort was used with one biopsy per tumor (162 tumors).

Results

Overall, inter-tumor transcriptional variation exceeded ITH, and subtyping calls were frequently concordant between multiregional biopsies. Still, some tumors had high transcriptional ITH and were classified discordantly. Subtyping of proximal MSS tumors were discordant for 50% of the tumors, this ITH was related to differences in the microenvironment. Subtyping of distal MSS tumors were less discordant, here the ITH was more cancer-cell related. The subtype discordancy reflected actual molecular ITH within the tumors. The relevance of the subtypes was reflected at protein level where several inflammation markers were significantly increased in immune related transcriptional subtypes, which was verified in an independent cohort (Wilcoxon rank sum test; p<0.05). Unsupervised hierarchical clustering of the protein data identified large ITH at protein level; as the multiregional biopsies clustered together for only 9 out of 29 tumors.

Conclusion

Our transcriptomic and proteomic analyses show that the tumor location along the colorectum influence the ITH of CRC, which again influence the concordance of subtyping.

Identification of NEO1 as a prognostic biomarker and its effects on the progression of colorectal cancer

Background

Due to the high morbidity and poor clinical outcomes, early predictive and prognostic biomarker identification is desiderated in colorectal cancer (CRC). As a homologue of the Deleted in Colorectal Cancer (DCC) gene, the role of Neogenin-1 (NEO1) in CRC remained unveiled. This study was designed to probe into the effects and potential function of NEO1 in CRC.

Methods

Online databases, Gene Set Enrichment Analysis (GSEA), quantitative real-time PCR and western blotting were used to evaluate NEO1 expression in colorectal cancer tissues. Survival analysis was performed to predict the prognosis of CRC patients based on NEO1 expression level. Then, cell proliferation was detected by colony formation and Cell Counting Kit 8 (CCK-8) assays. CRC cell migration and invasion were examined by transwell assays. Finally, we utilized the Gene Set Variation Analysis (GSVA) and GSEA to dig the potential mechanisms of NEO1 in CRC.

Results

Oncomine database and The Cancer Genome Atlas (TCGA) database showed that NEO1 was down-regulated in CRC. Further results validated that NEO1 mRNA and protein expression were both significantly lower in CRC tumor tissues than in the adjacent tissues in our clinical samples. NEO1 expression was decreased with the progression of CRC. Survival and other clinical characteristic analyses exhibited that low NEO1 expression was related with poor prognosis. A gain-of-function study showed that overexpression of NEO1 restrained proliferation, migration and invasion of CRC cells while a loss-of-function showed the opposite effects. Finally, functional pathway enrichment analysis revealed that NEO1 low expression samples were enriched in inflammation-related signaling pathways, EMT and angiogenesis.

Conclusion

A tumor suppressor gene NEO1 was identified and verified to be correlated with the prognosis and progression of CRC, which could serve as a prognostic biomarker for CRC patients.

Neoadjuvant Chemotherapy without Radiation in Colorectal Cancer

In colon cancer, primary surgery followed by postoperative chemotherapy represents the standard of care. In rectal cancer, the standard of care is preoperative radiotherapy or chemoradiation, which significantly reduces local recurrence but has no impact on subsequent metastatic disease or overall survival. The administration of neoadjuvant chemotherapy (NACT) before surgery can increase the chance of a curative resection and improves long-term outcomes in patients with liver metastases. Hence, NACT is being explored in both primary rectal and colon cancers as an alternative strategy to shrink the tumor, facilitate a curative resection, and simultaneously counter the risk of metastases. Yet, this lack of clarity regarding the precise aims of NACT (downstaging, maximizing response, or improving survival) is hindering progress. The appropriate cytotoxic agents, the optimal regimen, the number of cycles, or duration of NACT prior to surgery or in the postoperative setting remains undefined. Several potential strategies for integrating NACT are discussed with their advantages and disadvantages.

Characterization of the Immune Cell Infiltration Landscape in Head and Neck Squamous Cell Carcinoma to Aid Immunotherapy

The tumor microenvironment (TME) chiefly consists of tumor cells and tumor-infiltrating immune cells admixed with the stromal component. A recent clinical trial has shown that the tumor immune cell infiltration (ICI) is correlated with the sensitivity to immunotherapy and the head and neck squamous cell carcinoma (HNSC) prognosis. However, to date, the immune infiltrative landscape of HNSC has not yet been elucidated. Herein, we proposed two computational algorithms to unravel the ICI landscape of 1,029 HNSC patients. Three ICI patterns were defined, and the ICI scores were determined by using principal-component analysis. A high ICI score was characterized by an increased tumor mutation burden (TMB) and the immune-activating signaling pathways. Activation of transforming growth factor-β (TGF-β) and WNT signaling pathways were observed in low ICI score subtypes, indicating T cell suppression, and may be responsible for poor prognosis. Two immunotherapy cohorts confirmed patients with higher ICI scores demonstrated significant therapeutic advantages and clinical benefits. This study demonstrated that the ICI scores serve as an effective prognostic biomarker and predictive indicator for immunotherapy. Evaluating the ICI patterns of a larger cohort of samples will extend our understanding of TME, and it may provide directions to the current research investigations on immunotherapeutic strategies for HNSC.

Molecular Pathogenesis and Classification of Colorectal Carcinoma

Purpose of review

Molecular pathways in colorectal carcinogenesis involve several complex genetic and epigenetic modulations that cause normal colonic mucosa to metamorphose into a benign polyp and subsequently into a malignant tumor. Our purpose is to recapitulate historical and recent genomic research in order to augment the understanding of colorectal cancer pathogenesis.

Recent Findings

In 2015, the molecular classification for colorectal cancers was unified into one system with four distinct groups, also called as consensus molecular subtypes. This led to an enhanced understanding of molecular and immune signatures which has implications on predicting the clinical behavior as well as response to different therapeutic agents.

Summary

In this review, we expound on the current literature as well as draw on our own experience to present the important molecular pathogenesis pathways, key genetic mutations, differences in pathogenesis of left versus right sided tumors as well as the molecular classification of colorectal cancers.