PreMSIm: An R package for predicting microsatellite instability from the expression profiling of a gene panel in cancer

Accelerated Aging and Microsatellite Instability in Recessive Dystrophic Epidermolysis Bullosa-Associated Cutaneous Squamous Cell Carcinoma

Recessive dystrophic epidermolysis bullosa (RDEB) is a severely debilitating disorder caused by pathogenic variants in COL7A1 and is characterized by extreme skin fragility, chronic inflammation, and fibrosis. A majority of patients with RDEB develop squamous cell carcinoma, a highly aggressive skin cancer with limited treatment options currently available. In this study, we utilized an approach leveraging whole-genome sequencing and RNA sequencing across 3 different tissues in a single patient with RDEB to gain insight into possible mechanisms of RDEB-associated squamous cell carcinoma progression and to identify potential therapeutic options. As a result, we identified PLK-1 as a possible candidate for targeted therapy and discovered microsatellite instability and accelerated aging as factors potentially contributing to the aggressive nature and early onset of RDEB squamous cell carcinoma. By integrating multitissue genomic and transcriptomic analyses in a single patient, we demonstrate the promise of bridging the gap between genomic research and clinical applications for developing tailored therapies for patients with rare genetic disorders such as RDEB.

The crosstalk between metabolic reprogramming and epithelial-mesenchymal transition and their synergistic roles in distant metastasis in breast cancer

BACKGROUND

Metabolic reprogramming (MR) and epithelial-mesenchymal transition (EMT) are crucial phenomena involved in the distant metastasis of breast cancer (BRCA). This study aims to assess the risk of distant metastasis in BRCA patients based on MR and EMT processes and investigate their underlying mechanisms.

METHODS

Gene sets related to EMT and MR were downloaded. MR-related genes (MRG) and EMT-related genes (ERG) were obtained. Principal Component Analysis method was used to define the EMT Potential Index (EPI) and MR Potential Index (MPI) to quantify the EMT and MR levels in each tumor tissue. A linear scoring model, the Metastasis Score, was derived using the union of MRGs and ERGs to evaluate the risk of distant metastasis/recurrence in BRCA. The Metastasis Score was then validated in multiple datasets. Additionally, our study explored the underlying mechanism of the Metastasis Score and its association with tumor immunity, focusing on HPRT1 gene expression in breast cancer tissues of transfer and untransferred groups using experimental methods.

RESULTS

A total of 59 MRGs and 30 ERGs were identified in the present study. Stratifying the dataset based on EPI and MPI revealed significantly lower survival rates (P < .05) in the MPI_high and EPI_high groups. Kaplan-Meier analysis indicated the lowest survival rate in the EPI-high + MPI-high group. The Metastasis Score demonstrated its ability to distinguish prognoses in GSE2034, GSE17705, and TCGA-BRCA datasets. Additionally, differences in mutated genes were found between the high- and the low-Metastasis Score groups, displaying significant associations with immune cell infiltration and anti-tumor immune status. Notably, the 13 genes included in the Metastasis Score showed a strong association with prognosis and tumor immunity. Immunohistochemistry and western blot results revealed high expression of the HPRT1 gene in the transfer group.

CONCLUSION

This study established the Metastasis Score as a reliable tool for evaluating the risk of distant metastasis/recurrence in BRCA patients. Additionally, we identified key genes involved in MR and EMT crosstalk, offering valuable insights into their roles in tumor immunity and other relevant aspects.

Multi-omics Analysis Identifies Hypoxia Subtypes and S100A2 as an Immunosuppressive Factor in Cervical Cancer

Cervical cancer is a common gynecological oncology. Growing evidence indicates hypoxia plays an important role in tumor progression and immunity. However, no study has examined the hypoxia landscape in cervical cancer. In this study, using hierarchical clustering, we identified three hypoxia subtypes in cervical cancer samples from The Cancer Genome Atlas dataset according to formerly described hypoxia-related genes. The overall survival time, hypoxic features, genomics, and immunological characteristics of these subtypes existed distinct differences. We also created a hypoxia score by principle component analysis for dimension reduction. The hypoxiaScore was an effective prognostic biomarker validated by GSE44001 and was associated with immunotherapy response. Furthermore, combined with single-cell RNA-sequence (scRNA-seq) and experiments, S100A2 was identified as an immunosuppressive factor induced by hypoxia and regulated expression of PD-L1. S100A2 also served as an oncogene promoting the proliferation and migration of cervical cancer cells. These findings depicted a new hypoxia-based classification and identified S100A2 as a potential therapeutic target for cervical cancer, thereby advancing the understanding of immunotherapy resistance mechanisms and cervical cancer genetic markers.

MSI-XGNN: an explainable GNN computational framework integrating transcription- and methylation-level biomarkers for microsatellite instability detection

Microsatellite instability (MSI) is a hypermutator phenotype caused by DNA mismatch repair deficiency. MSI has been reported in various human cancers, particularly colorectal, gastric and endometrial cancers. MSI is a promising biomarker for cancer prognosis and immune checkpoint blockade immunotherapy. Several computational methods have been developed for MSI detection using DNA- or RNA-based approaches based on next-generation sequencing. Epigenetic mechanisms, such as DNA methylation, regulate gene expression and play critical roles in the development and progression of cancer. We here developed MSI-XGNN, a new computational framework for predicting MSI status using bulk RNA-sequencing and DNA methylation data. MSI-XGNN is an explainable deep learning model that combines a graph neural network (GNN) model to extract features from the gene-methylation probe network with a CatBoost model to classify MSI status. MSI-XGNN, which requires tumor-only samples, exhibited comparable performance with two well-known methods that require tumor-normal paired sequencing data, MSIsensor and MANTIS and better performance than several other tools. MSI-XGNN also showed good generalizability on independent validation datasets. MSI-XGNN identified six MSI markers consisting of four methylation probes (EPM2AIP1|MLH1:cg14598950, EPM2AIP1|MLH1:cg27331401, LNP1:cg05428436 and TSC22D2:cg15048832) and two genes (RPL22L1 and MSH4) constituting the optimal feature subset. All six markers were significantly associated with beneficial tumor microenvironment characteristics for immunotherapy, such as tumor mutation burden, neoantigens and immune checkpoint molecules such as programmed cell death-1 and cytotoxic T-lymphocyte antigen-4. Overall, our study provides a powerful and explainable deep learning model for predicting MSI status and identifying MSI markers that can potentially be used for clinical MSI evaluation.

Long-read sequencing reveals the landscape of aberrant alternative splicing and novel therapeutic target in colorectal cancer

BACKGROUND

Alternative splicing complexity plays a vital role in carcinogenesis and cancer progression. Improved understanding of novel splicing events and the underlying regulatory mechanisms may contribute new insights into developing new therapeutic strategies for colorectal cancer (CRC).

METHODS

Here, we combined long-read sequencing technology with short-read RNA-seq methods to investigate the transcriptome complexity in CRC. By using experiment assays, we explored the function of newly identified splicing isoform TIMP1 Δ4-5. Moreover, a CRISPR/dCasRx-based strategy to induce the TIMP1 exon 4-5 exclusion was introduced to inhibit neoplasm growth.

RESULTS

A total of 90,703 transcripts were identified, of which > 62% were novel compared with current transcriptome annotations. These novel transcripts were more likely to be sample specific, expressed at relatively lower levels with more exons, and oncogenes displayed a characteristic to generate more transcripts in CRC. Clinical outcome data analysis showed that 1472 differentially expressed alternative splicing events (DEAS) were tightly associated with CRC patients' prognosis, and many novel isoforms were likely to be important determinants for patient survival. Among these, newly identified splicing isoform TIMP1 Δ4-5 was significantly downregulated in CRC. Further in vitro and in vivo assays demonstrated that ectopic expression of TIMP1 Δ4-5 significantly suppresses tumor cell growth and metastasis. Serine/arginine-rich splicing factor 1 (SRSF1) acts as a onco-splicing regulator through sustaining the inclusion of TIMP1 exon 4-5. Furthermore, CRISPR/dCasRx-based strategies designed to induce TIMP1 exon 4-5 exclusion have the potential to restrain the CRC growth.

CONCLUSIONS

This data provides a rich resource for deeper studies of gastrointestinal malignancies. Newly identified splicing isoform TIMP1 Δ4-5 plays an important role in mediating CRC progression and may be a potential therapy target in CRC.

Evaluating the use of machine learning in endometrial cancer: a systematic review

OBJECTIVE

To review the literature on machine learning in endometrial cancer, report the most commonly used algorithms, and compare performance with traditional prediction models.

METHODS

This is a systematic review of the literature from January 1985 to March 2021 on the use of machine learning in endometrial cancer. An extensive search of electronic databases was conducted. Four independent reviewers screened studies initially by title then full text. Quality was assessed using the MINORS (Methodological Index for Non-Randomized Studies) criteria. P values were derived using the Pearson's Χ2 test in JMP 15.0.

RESULTS

Among 4295 articles screened, 30 studies on machine learning in endometrial cancer were included. The most frequent applications were in patient datasets (33.3%, n=10), pre-operative diagnostics (30%, n=9), genomics (23.3%, n=7), and serum biomarkers (13.3%, n=4). The most commonly used models were neural networks (n=10, 33.3%) and support vector machine (n=6, 20%).The number of publications on machine learning in endometrial cancer increased from 1 in 2010 to 29 in 2021.Eight studies compared machine learning with traditional statistics. Among patient dataset studies, two machine learning models (20%) performed similarly to logistic regression (accuracy: 0.85 vs 0.82, p=0.16). Machine learning algorithms performed similarly to detect endometrial cancer based on MRI (accuracy: 0.87 vs 0.82, p=0.24) while outperforming traditional methods in predicting extra-uterine disease in one serum biomarker study (accuracy: 0.81 vs 0.61). For survival outcomes, one study compared machine learning with Kaplan-Meier and reported no difference in concordance index (83.8% vs 83.1%).

CONCLUSION

Although machine learning is an innovative and emerging technology, performance is similar to that of traditional regression models in endometrial cancer. More studies are needed to assess its role in endometrial cancer.

PROSPERO REGISTRATION NUMBER

CRD42021269565.

Novel prognostic gene signature for pancreatic ductal adenocarcinoma based on hypoxia

BACKGROUND

Currently, there is lack of marker to accurately assess the prognosis of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC). This study aims to establish a hypoxia-related risk scoring model that can effectively predict the prognosis and chemotherapy outcomes of PDAC patients.

METHODS

Using unsupervised consensus clustering algorithms, we comprehensively analyzed The Cancer Genome Atlas (TCGA) data to identify two distinct hypoxia clusters and used the weighted gene co-expression network analysis (WGCNA) to examine gene sets significantly associated with these hypoxia clusters. Then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were used to construct a signature and its efficacy was evaluated using the International Cancer Genome Consortium (ICGC) PDAC cohort. Further, the correlation between the risk scores obtained from the signature and carious clinical, pathological, immunophenotype, and immunoinfiltration factors as well as the differences in immunotherapy potential and response to common chemotherapy drugs between high-risk and low-risk groups were evaluated.

RESULTS

From a total of 8 significantly related modules and 4423 genes, 5 hypoxia-related signature genes were identified to construct a risk model. Further analysis revealed that the overall survival rate (OS) of patients in the low-risk group was significantly higher than the high-risk group. Univariate and multivariate Cox regression analysis showed that the risk scoring signature was an independent factor for prognosis prediction. Analysis of immunocyte infiltration and immunophenotype showed that the immune score and the anticancer immune response in the high-risk were significantly lower than that in the low-risk group.

CONCLUSION

The constructed hypoxia-associated prognostic signature demonstrated could be used as a potential risk classifier for PDAC.

Cuproptosis-related risk score based on machine learning algorithm predicts prognosis and characterizes tumor microenvironment in head and neck squamous carcinomas

Cuproptosis is a recently discovered type of programmed cell death that shows significant potential in the diagnosis and treatment of cancer. It has important significance in the prognosis of HSNC. This study aims to construct a cuproptosis-related prognostic model and risk score through new data analysis methods such as machine learning algorithms for the prognosis analysis of HSNC. Protein-protein interaction network and machine learning methods were employed to identify hub genes that were used to construct a TreeGradientBoosting model for predicting overall survival. The relationship between the risk scores obtained from the model and features such as tumor microenvironment (TME) and tumor immunity was explored. The C-indexes of the TreeGradientBoosting model in the training and validation cohorts were 0.776 and 0.848, respectively. The nomogram based on risk scores and clinical features showed good performance, and distinguished the TME and immunity between high-risk and low-risk groups. The cuproptosis-associated risk score can be used to predict prognoses, TME, and tumor immunity of HNSC patients.

Mitochondrial DNA methylation is a predictor of immunotherapy response and prognosis in breast cancer: scRNA-seq and bulk-seq data insights

Background

Alterations in Mitochondrial DNA methylation (MTDM) exist in many tumors, but their role in breast cancer (BC) development remains unclear.

Methods

We analyzed BC patient data by combining scRNA-seq and bulk sequencing. Weighted co-expression network analysis (WGCNA) of TCGA data identified mitochondrial DNA methylation (MTDM)-associated genes in BC. COX regression and LASSO regression were used to build prognostic models. The biological function of MTDM was assessed using various methods, such as signaling pathway enrichment analysis, copynumber karyotyping analysis, and quantitative analysis of the cell proliferation rate. We also evaluated MTDM-mediated alterations in the immune microenvironment using immune microenvironment, microsatellite instability, mutation, unsupervised clustering, malignant cell subtype differentiation, immune cell subtype differentiation, and cell-communication signature analyses. Finally, we performed cellular experiments to validate the role of the MTDM-associated prognostic gene NCAPD3 in BC.

Results

In this study, MTDM-associated prognostic models divided BC patients into high/low MTDM groups in TCGA/GEO datasets. The difference in survival time between the two groups was statistically significant (P<0.001). We found that high MTDM status was positively correlated with tumor cell proliferation. We analyzed the immune microenvironment and found that low-MTDM group had higher immune checkpoint gene expression/immune cell infiltration, which could lead to potential benefits from immunotherapy. In contrast, the high MTDM group had higher proliferation rates and levels of CD8+T cell exhaustion, which may be related to the secretion of GDF15 by malignant breast epithelial cells with a high MTDM status. Cellular experiments validated the role of the MTDM-associated prognostic gene NCAPD3 (the gene most positively correlated with epithelial malignant cell proliferation in the model) in BC. Knockdown of NCAPD3 significantly reduced the activity and proliferation of MDA-MB-231 and BCAP-37 cells, and significantly reduced their migration ability of BCAP-37 cell line.

Conclusion

This study presented a holistic evaluation of the multifaceted roles of MTDM in BC. The analysis of MTDM levels not only enables the prediction of response to immunotherapy but also serves as an accurate prognostic indicator for patients with BC. These insightful discoveries provide novel perspectives on tumor immunity and have the potentially to revolutionize the diagnosis and treatment of BC.

Exploring PANoptosis in breast cancer based on scRNA-seq and bulk-seq

Background

PANoptosis, a cell death pathway involving pyroptosis, apoptosis, and necroptosis, is pivotal in the development of malignancy. However, in the field of breast cancer, the interaction between PANoptosis and tumor cells has not been thoroughly explored.

Methods

We downloaded breast cancer data and GSE176078 single-cell sequencing dataset from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to obtain PANoptosis-associated genes. To construct prognostic models, COX and LASSO regression was used to identify PANoptosis-associated genes with prognostic value. Finally, immune infiltration analysis and differential analysis of biological functions were performed.

Results

Risk grouping was performed according to the prognostic model constructed by COX regression and LASSO regression. The low-risk group showed a better prognosis (P < 0.05) and possessed higher levels of immune infiltration and expression of immune checkpoint-related genes. In addition, the lower the risk score, the higher the degree of microsatellite instability (MSI). Meanwhile, radixin (RDX), the gene with the highest hazard ratio (HR) value among PANoptosis prognosis-related genes, was explicitly expressed in artery Iendothelial cells (ECs) and was widely involved in signaling pathways such as immune response and cell proliferation, possessing rich biological functions.

Conclusion

We demonstrated the potential of PANoptosis-based molecular clustering and prognostic features in predicting the survival of breast cancer patients. Furthermore, this study has led to a deeper understanding of the role of PANoptosis in breast cancer and has the potential to provide new directions for immunotherapy of breast cancer.

Integrated clinical genomic analysis reveals xenobiotic metabolic genes are downregulated in meningiomas of current smokers

INTRODUCTION

Meningiomas are the most common primary intracranial tumor. Recently, various genetic classification systems for meningioma have been described. We sought to identify clinical drivers of different molecular changes in meningioma. As such, clinical and genomic consequences of smoking in patients with meningiomas remain unexplored.

METHODS

88 tumor samples were analyzed in this study. Whole exome sequencing (WES) was used to assess somatic mutation burden. RNA sequencing data was used to identify differentially expressed genes (DEG) and genes sets (GSEA).

RESULTS

Fifty-seven patients had no history of smoking, twenty-two were past smokers, and nine were current smokers. The clinical data showed no major differences in natural history across smoking status. WES revealed absence of AKT1 mutation rate in current or past smokers compared to non-smokers (p = 0.046). Current smokers had increased mutation rate in NOTCH2 compared to past and never smokers (p < 0.05). Mutational signature from current and past smokers showed disrupted DNA mismatch repair (cosine-similarity = 0.759 and 0.783). DEG analysis revealed the xenobiotic metabolic genes UGT2A1 and UGT2A2 were both significantly downregulated in current smokers compared to past (Log2FC = - 3.97, padj = 0.0347 and Log2FC = - 4.18, padj = 0.0304) and never smokers (Log2FC = - 3.86, padj = 0.0235 and Log2FC = - 4.20, padj = 0.0149). GSEA analysis of current smokers showed downregulation of xenobiotic metabolism and enrichment for G2M checkpoint, E2F targets, and mitotic spindle compared to past and never smokers (FDR < 25% each).

CONCLUSION

In this study, we conducted a comparative analysis of meningioma patients based on their smoking history, examining both their clinical trajectories and molecular changes. Meningiomas from current smokers were more likely to harbor NOTCH2 mutations, and AKT1 mutations were absent in current or past smokers. Moreover, both current and past smokers exhibited a mutational signature associated with DNA mismatch repair. Meningiomas from current smokers demonstrate downregulation of xenobiotic metabolic enzymes UGT2A1 and UGT2A2, which are downregulated in other smoking related cancers. Furthermore, current smokers exhibited downregulation xenobiotic metabolic gene sets, as well as enrichment in gene sets related to mitotic spindle, E2F targets, and G2M checkpoint, which are hallmark pathways involved in cell division and DNA replication control. In aggregate, our results demonstrate novel alterations in meningioma molecular biology in response to systemic carcinogens.

Cancer-specific functional profiling in microsatellite-unstable (MSI) colon and endometrial cancers using combined differentially expressed genes and biclustering analysis

Microsatellite-unstable (MSI) cancers have distinct genetic and clinical features from microsatellite-stable cancers, but the molecular functional differences between MSI cancers originating from different tissues or organs have not been well studied because the application of usual differentially expressed gene (DEG) analysis is error-prone, producing too many noncancer-specific normally functioning genes. To maximize therapeutic efficacy, biomarkers reflecting cancer-specific differences between MSI cancers of different tissue origins should be identified. To identify functional differences between MSI colon and endometrial cancers, we combined DEG analysis and biclustering instead of DEG analysis alone and refined functionally relevant biclusters reflecting genuine functional differences between the 2 tumors. Specifically, using The Cancer Genome Atlas and genome-tissue expression as data sources, gene ontology (GO) enrichment tests were performed after routinely identifying DEGs between the 2 tumors with the exclusion of DEGs identified in their normal counterparts. Cancer-specific biclusters and associated enriched GO terms were obtained by biclustering with enrichment tests for the preferences for cancer type (either colon or endometrium) and GO enrichment tests for each cancer-specific bicluster, respectively. A novel childness score was developed to select functionally relevant biclusters among cancer-specific biclusters based on the extent to which the enriched GO terms of the biclusters tended to be child terms of the enriched GO terms in DEGs. The selected biclusters were tested using survival analysis to validate their clinical significance. We performed multiple sequential analyses to produce functionally relevant biclusters from the RNA sequencing data of MSI colon and endometrial cancer samples and their normal counterparts. We identified 3066 cancer-specific DEGs. Biclustering analysis revealed 153 biclusters and 41 cancer-specific biclusters were selected using Fisher exact test. A mean childness score over 0.6 was applied as the threshold and yielded 8 functionally relevant biclusters from cancer-specific biclusters. Functional differences appear to include gland cavitation and the TGF-β receptor, G protein, and cytokine pathways. In the survival analysis, 6 of the 8 functionally relevant biclusters were statistically significant. By attenuating noise and applying a synergistic contribution of DEG results, we refined candidate biomarkers to complement tissue-specific features of MSI tumors.

BRD4 inhibition impairs DNA mismatch repair, induces mismatch repair mutation signatures and creates therapeutic vulnerability to immune checkpoint blockade in MMR-proficient tumors

BACKGROUND

Mismatch repair deficiency (dMMR) is a well-recognized biomarker for response to immune checkpoint blockade (ICB). Strategies to convert MMR-proficient (pMMR) to dMMR phenotype with the goal of sensitizing tumors to ICB are highly sought. The combination of bromodomain containing 4 (BRD4) inhibition and ICB provides a promising antitumor effect. However, the mechanisms underlying remain unknown. Here, we identify that BRD4 inhibition induces a persistent dMMR phenotype in cancers.

METHODS

We confirmed the correlation between BRD4 and mismatch repair (MMR) by the bioinformatic analysis on The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium data, and the statistical analysis on immunohistochemistry (IHC) scores of ovarian cancer specimens. The MMR genes (MLH1,MSH2,MSH6,PMS2) were measured by quantitative reverse transcription PCR, western blot, and IHC. The MMR status was confirmed by whole exome sequencing, RNA sequencing, MMR assay and hypoxanthine-guanine phosphoribosyl transferase gene mutation assay. The BRD4i AZD5153 resistant models were induced both in vitro and in vivo. The transcriptional effects of BRD4 on MMR genes were investigated by chromatin immunoprecipitation among cell lines and data from the Cistrome Data Browser. The therapeutic response to ICB was testified in vivo. The tumor immune microenvironment markers, such as CD4, CD8, TIM-3, FOXP3, were measured by flow cytometry.

RESULTS

We identified the positive correlation between BRD4 and MMR genes in transcriptional and translational aspects. Also, the inhibition of BRD4 transcriptionally reduced MMR genes expression, resulting in dMMR status and elevated mutation loads. Furthermore, prolonged exposure to AZD5153 promoted a persistent dMMR signature both in vitro and in vivo, enhancing tumor immunogenicity, and increased sensitivity to α-programmed death ligand-1 therapy despite the acquired drug resistance.

CONCLUSIONS

We demonstrated that BRD4 inhibition suppressed expression of genes critical to MMR, dampened MMR, and increased dMMR mutation signatures both in vitro and in vivo, sensitizing pMMR tumors to ICB. Importantly, even in BRD4 inhibitors (BRD4i)-resistant tumor models, the effects of BRD4i on MMR function were maintained rendering tumors sensitive to ICB. Together, these data identified a strategy to induce dMMR in pMMR tumors and further, indicated that BRD4i sensitive and resistant tumors could benefit from immunotherapy.

APC mutation correlated with poor response of immunotherapy in colon cancer

OBJECTIVE

APC (adenomatous polyposis coli) gene mutation is a central initialization in colon cancer tumorigenesis. However, the connection between APC gene mutation and immunotherapy efficacy for colon cancer remains unknown. This study aimed to explore the impact of APC mutation on immunotherapy efficacy for colon cancer.

METHODS

Colon cancer data from The Cancer Genome Atlas (TCGA) and Memorial Sloan Kettering Cancer Center (MSKCC) were used for the combined analysis. Survival analysis was performed to evaluate the association between APC mutation and immunotherapy efficacy in colon cancer patients. The expressions of immune check point molecules, tumor mutation burden (TMB), CpG methylation level, tumor purity (TP), microsatellite instability (MSI) status and tumor-infiltrating lymphocyte (TIL) in the two APC status were compared to evaluate the associations between APC mutation and immunotherapy efficacy indicators. Gene set enrichment analysis (GSEA) was performed to identify signaling pathways related to APC mutation.

RESULTS

APC was the most frequently mutated gene in colon cancer. The survival analysis demonstrated that APC mutation was correlated with a worse immunotherapy outcome. APC mutation was associated with lower TMB, lower expression of immune check point molecules (PD-1/PD-L1/PD-L2), higher TP, lower MSI-High proportion and less CD8 + T cells and follicular helper T cells infiltration. GSEA indicated that APC mutation up-regulated mismatch repair pathway, which may play a negative role in evoking an antitumor immune response.

CONCLUSION

APC mutation is associated with worse immunotherapy outcome and inhibition of antitumor immunity. It can be used as a negative biomarker to predict immunotherapy response.

MSIMEP: Predicting microsatellite instability from microarray DNA methylation tumor profiles

Deficiency in DNA MMR activity results in tumors with a hypermutator phenotype, termed microsatellite instability (MSI). Beyond its utility in Lynch syndrome screening algorithms, today MSI has gained importance as predictive biomarker for various anti-PD-1 therapies across many different tumor types. Over the past years, many computational methods have emerged to infer MSI using either DNA- or RNA-based approaches. Considering this together with the fact that MSI-high tumors frequently exhibit a hypermethylated phenotype, herein we developed and validated MSIMEP, a computational tool for predicting MSI status from microarray DNA methylation tumor profiles of colorectal cancer samples. We demonstrated that MSIMEP optimized and reduced models have high performance in predicting MSI in different colorectal cancer cohorts. Moreover, we tested its consistency in other tumor types with high prevalence of MSI such as gastric and endometrial cancers. Finally, we demonstrated better performance of both MSIMEP models vis-à-vis a MLH1 promoter methylation-based one in colorectal cancer.

Microsatellite instability assessment is instrumental for Predictive, Preventive and Personalised Medicine: status quo and outlook

A form of genomic alteration called microsatellite instability (MSI) occurs in a class of tandem repeats (TRs) called microsatellites (MSs) or short tandem repeats (STRs) due to the failure of a post-replicative DNA mismatch repair (MMR) system. Traditionally, the strategies for determining MSI events have been low-throughput procedures that typically require assessment of tumours as well as healthy samples. On the other hand, recent large-scale pan-tumour studies have consistently highlighted the potential of massively parallel sequencing (MPS) on the MSI scale. As a result of recent innovations, minimally invasive methods show a high potential to be integrated into the clinical routine and delivery of adapted medical care to all patients. Along with advances in sequencing technologies and their ever-increasing cost-effectiveness, they may bring about a new era of Predictive, Preventive and Personalised Medicine (3PM). In this paper, we offered a comprehensive analysis of high-throughput strategies and computational tools for the calling and assessment of MSI events, including whole-genome, whole-exome and targeted sequencing approaches. We also discussed in detail the detection of MSI status by current MPS blood-based methods and we hypothesised how they may contribute to the shift from conventional medicine to predictive diagnosis, targeted prevention and personalised medical services. Increasing the efficacy of patient stratification based on MSI status is crucial for tailored decision-making. Contextually, this paper highlights drawbacks both at the technical level and those embedded deeper in cellular/molecular processes and future applications in routine clinical testing.

Cell Pair Algorithm-Based Immune Infiltrating Cell Signature for Improving Outcomes and Treatment Responses in Patients with Hepatocellular Carcinoma

BACKGROUND

Immune interactions play important roles in the regulation of T cells' cytotoxic function, further impacting the anti-tumor efficacy of immunotherapy. A comprehensive analysis of immune cell types in HCC and immune-cell-related signatures predicting prognosis and monitoring immunotherapy efficacy is still absent.

METHODS

More than 1,300 hepatocellular carcinomas (HCC) patients were collected from public databases and included in the present study. The ssGSEA algorithm was applied to calculate the infiltration level of 28 immunocyte subpopulations. A cell pair algorithm was applied to construct an immune-cell-related prognostic index (ICRPI). Survival analyses were performed to measure the survival difference across ICRPI risk groups. Spearman's correlation analyses were used for the relevance assessment. A Wilcoxon test was used to measure the expression level's differences.

RESULTS

In this study, 28 immune subpopulations were retrieved, and 374 immune cell pairs (ICPs) were established, 38 of which were picked out by the least absolute shrinkage and selection operator (LASSO) algorithm. By using the selected ICPs, the ICRPI was constructed and validated to play crucial roles in survival stratification and dynamic monitoring of immunotherapy effect. We also explored several candidate drugs targeting ICRPI. A composite ICRPI and clinical prognostic index (ICPI) was then constructed, which achieved a more accurate estimation of HCC's survival and is a better choice for prognosis predictions in HCC.

CONCLUSIONS

In conclusion, we constructed and validated ICRPI based on the cell pair algorithm in this study, which might provide some novel insights for increasing the survival estimation and clinical response to immune therapy for individual HCC patients and contribute to the personalized precision immunotherapy strategy of HCC.

Microsatellite instability-related prognostic risk score (MSI-pRS) defines a subset of lung squamous cell carcinoma (LUSC) patients with genomic instability and poor clinical outcome

Background: Lung squamous cell carcinoma (LUSC) shares less typical onco-drivers and target resistance, but a high overall mutation rate and marked genomic complexity. Mismatch repair (MMR) deficiency leads to microsatellite instability (MSI) and genomic instability. MSI is not an ideal option for prognosis of LUSC, whereas its function deserves exploration. Method: MSI status was classified by MMR proteins using unsupervised clustering in the TCGA-LUSC dataset. The MSI score of each sample was determined by gene set variation analysis. Intersections of the differential expression genes and differential methylation probes were classified into functional modules by weighted gene co-expression network analysis. Least absolute shrinkage and selection operator regression and stepwise gene selection were performed for model downscaling. Results: Compared with the MSI-low (MSI-L) phenotype, MSI-high (MSI-H) displayed higher genomic instability. The MSI score was decreased from MSI-H to normal samples (MSI-H > MSI-L > normal). A total of 843 genes activated by hypomethylation and 430 genes silenced by hypermethylation in MSI-H tumors were classified into six functional modules. CCDC68, LYSMD1, RPS7, and CDK20 were used to construct MSI-related prognostic risk score (MSI-pRS). Low MSI-pRS was a protective prognostic factor in all cohorts (HR = 0.46, 0.47, 0.37; p-value = 7.57e-06, 0.009, 0.021). The model contains tumor stage, age, and MSI-pRS that showed good discrimination and calibration. Decision curve analyses indicated that microsatellite instability-related prognostic risk score added extra value to the prognosis. A low MSI-pRS was negatively correlated with genomic instability. LUSC with low MSI-pRS was associated with increased genomic instability and cold immunophenotype. Conclusion: MSI-pRS is a promising prognostic biomarker in LUSC as the substitute of MSI. Moreover, we first declared that LYSMD1 contributed to genomic instability of LUSC. Our findings provided new insights in the biomarker finder of LUSC.

Hybrid Metabolic Activity-Related Prognostic Model and Its Effect on Tumor in Renal Cell Carcinoma

Background

Tumor cells with a hybrid metabolic state, in which glycolysis and oxidative phosphorylation (OXPHOS) can be used, usually have a strong ability to adapt to different stress environments due to their metabolic plasticity. However, few studies on tumor cells with this phenotype have been conducted in the field of renal cell carcinoma (RCC).

Methods

The metabolic pathway (glycolysis, OXPHOS) related gene sets were obtained from the Molecular Signatures Database (V7.5.1). The gene expression matrix, clinical information, and mutation data were obtained by Perl programming language (5.32.0) mining, the Cancer Genome Atlas and International Cancer Genome Consortium database. Gene Set Enrichment Analysis (GSEA) software (4.0.3) was utilised to analyse glycolysis-related gene sets. Analysis of survival, immune infiltration, mutation, etc. was performed using the R programming language (4.1.0).

Results

Eight genes that are highly associated with glycolysis and OXHPOS were used to construct the cox proportional hazards model, and risk scores were calculated based on this to predict the prognosis of clear cell RCC patients and to classify patients into risk groups. Gene Ontology, the Kyoto Encyclopaedia of Genes and Genomes, and GSEA were analysed according to the differential genes to investigate the signal pathways related to the hybrid metabolic state. Immunoinfiltration analysis revealed that CD8+T cells, M2 macrophages, etc., had significant differences in infiltration. In addition, the analysis of mutation data showed significant differences in the number of mutations of PBRM1, SETD2, and BAP1 between groups. Cell experiments demonstrated that the DLD gene expression was abnormally high in various tumor cells and is associated with the strong migration ability of RCC.

Conclusions

We successfully constructed a risk score system based on glycolysis and OXPHOS-related genes to predict the prognosis of RCC patients. Bioinformatics analysis and cell experiments also revealed the effect of the hybrid metabolic activity on the migration ability and immune activity of RCC and the possible therapeutic targets for patients.

Novel T-cell signature based on cell pair algorithm predicts survival and immunotherapy response for patients with bladder urothelial carcinoma

Background

T-cell–T-cell interactions play important roles in the regulation of T-cells’ cytotoxic function, further impacting the anti-tumor efficacy of immunotherapy. There is a lack of comprehensive studies of T-cell types in bladder urothelial carcinoma (BLCA) and T-cell-related signatures for predicting prognosis and monitoring immunotherapy efficacy.

Methods

More than 3,400 BLCA patients were collected and used in the present study. The ssGSEA algorithm was applied to calculate the infiltration level of 19 T-cell types. A cell pair algorithm was applied to construct a T-cell-related prognostic index (TCRPI). Survival analysis was performed to measure the survival difference across TCRPI-risk groups. Spearman’s correlation analysis was used for relevance assessment. The Wilcox test was used to measure the expression level difference.

Results

Nineteen T-cell types were collected; 171 T-cell pairs (TCPs) were established, of which 26 were picked out by the least absolute shrinkage and selection operator (LASSO) analysis. Based on these TCPs, the TCRPI was constructed and validated to play crucial roles in survival stratification and the dynamic monitoring of immunotherapy effects. We also explored several candidate drugs targeting TCRPI. A composite TCRPI and clinical prognostic index (CTCPI) was then constructed, which achieved a more accurate estimation of BLCA’s survival and was therefore a better choice for prognosis prediction in BLCA.

Conclusions

All in all, we constructed and validated TCRPI based on cell pair algorithms in this study, which might put forward some new insights to increase the survival estimation and clinical response to immune therapy for individual BLCA patients and contribute to the personalized precision immunotherapy strategy of BLCA.

Exploring the multidimensional heterogeneities of glioblastoma multiforme based on sample-specific edge perturbation in gene interaction network

Glioblastoma multiforme (GBM) is the most malignant brain cancer with great heterogeneities in many aspects, such as prognosis, clinicopathological features, immune landscapes, and immunotherapeutic responses. Considering that gene interaction network is relatively stable in a healthy state but widely perturbed in cancers, we sought to explore the multidimensional heterogeneities of GBM through evaluating the degree of network perturbations. The gene interaction network perturbations of GBM samples (TCGA cohort) and normal samples (GTEx database) were characterized by edge perturbations, which were quantized through evaluating the change in relative gene expression value. An unsupervised consensus clustering analysis was performed to identify edge perturbation-based clusters of GBM samples. Results revealed that the edge perturbation of GBM samples was stronger than that of normal samples. Four edge perturbation-based clusters of GBM samples were identified and showed prominent heterogeneities in prognosis, clinicopathological features, somatic genomic alterations, immune landscapes, and immunotherapeutic responses. In addition, a sample-specific perturbation of gene interaction score (SPGIScore) was constructed based on the differently expressed genes (DEGs) among four clusters, and exhibited a robust ability to predict prognosis. In conclusion, the bioinformatics approach based on sample-specific edge perturbation in gene interaction network provided a new perspective to understanding the multidimensional heterogeneities of GBM.

Screening and identification of immune-related genes for immunotherapy and prognostic assessment in colorectal cancer patients

Background

Increasing evidence indicates that the immune microenvironment plays a key role in the genesis and progression of colorectal cancer (CRC). This study aimed to establish an immune-related gene (IRG) signature and determine its clinical prognostic value in patients with CRC.

Methods

The RNA sequencing and associated clinical data of CRC were downloaded from The Cancer Genome Atlas (TCGA) database. We then screened for differentially expressed IRGs by intersecting with IRGs obtained from the Immunology Database and Analysis Portal. Functional enrichment analyses were carried out to determine the potential biological functions and pathways of the IRGs. We also explored the specific molecular mechanisms of the IRGs by constructing regulatory networks. Prognostic IRGs were obtained by LASSO regression analysis, and subsequently, gene models were constructed in the TCGA dataset to confirm the predictive capacity of these IRGs. Finally, we used the TIMER tool to assess the immune properties of prognostic IRGs and correlate them with immune cells.

Results

We identified 409 differentially expressed IRGs in patients with CRC. Kyoto Encyclopaedia of Genes and Genomes and Gene Ontology enrichment analyses suggested that these differentially expressed IRGs were significantly related to 102 cancer signalling pathways and various biological functions. Based on the prediction and interaction results, we obtained 59 TF–IRG, 48 miRNA–IRG, and 214 drug–IRG interaction networks for CRC. Four prognostic genes (POMC, TNFRSF19, FGF2, and SCG2) were developed by integrating 47 survival-related IRGs and 42 characteristic CRC genes. The results of gene model showed that patients in the low risk group had better survival outcomes compared to those in the high risk group. The expression of POMC, TNFRSF19, FGF2, and SCG2 was significantly correlated with immune cells.

Conclusion

This study identified some valid IRGs, and these findings can provide strong evidence for precision immunotherapy in patients with CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01329-2.

A Pan-Cancer Analysis Reveals the Prognostic and Immunotherapeutic Value of Stanniocalcin-2 (STC2)

Background: Stanniocalcin-2 (STC2) is a secreted glycoprotein which plays an important role in regulating the homeostasis of calcium, glucose homeostasis, and phosphorus metastasis. Accumulating evidence suggests that STC2 is implicated in cancer mechanisms. However, the effects of STC2 on cancer development and progression across pan-cancer are not yet completely known.

Methods: Data were downloaded from The Cancer Genome Atlas database to obtain differentially expressed genes significantly associated with prognosis (key genes). A gene was selected for subsequent correlation studies by integrating the significance of prognosis and the time-dependent ROC curve. Gene expression of different tumor types was analyzed based on the UCSC XENA website. Furthermore, our study investigated the correlation of STC2 expression between prognosis, immune cell infiltration, immune checkpoint genes (ICGs), mismatch repair genes (MMRs), tumor mutation burden (TMB), microsatellite instability (MSI), and drug sensitivity in various malignant tumors. Gene set enrichment analysis (GSEA) was conducted for correlated genes of STC2 to explore potential mechanisms.

Results: A total of 3,429 differentially expressed genes and 397 prognosis-related genes were identified from the TCGA database. Twenty-six key genes were found by crossing the former and the latter, and the highest risk gene, STC2, was selected for subsequent correlation studies. STC2 had good diagnostic performance for HNSCC, and was closely related to the survival status and clinicopathological stage of HNSCC patients. In pan-cancer analysis, STC2 was upregulated in 20 cancers and downregulated in seven cancers. STC2 overexpression was overall negatively correlated with overall survival, disease-free survival, disease-specific survival, and progress-free survival. STC2 was profoundly correlated with the tumor immune microenvironment, including immune cell infiltration, ICGs, MMRs, TMB, and MSI. Moreover, STC2 was significantly negatively correlated with the sensitivity or resistance of multiple drugs.

Conclusion: STC2 was a potential prognostic biomarker for pan-cancer and a new immunotherapy target.

Detection and Localization of Solid Tumors Utilizing the Cancer-Type-Specific Mutational Signatures

Accurate detection and location of tumor lesions are essential for improving the diagnosis and personalized cancer therapy. However, the diagnosis of lesions with fuzzy histology is mainly dependent on experiences and with low accuracy and efficiency. Here, we developed a logistic regression model based on mutational signatures (MS) for each cancer type to trace the tumor origin. We observed MS could distinguish cancer from inflammation and healthy individuals. By collecting extensive datasets of samples from ten tumor types in the training cohort (5,001 samples) and independent testing cohort (2,580 samples), cancer-type-specific MS patterns (CTS-MS) were identified and had a robust performance in distinguishing different types of primary and metastatic solid tumors (AUC:0.76 ∼ 0.93). Moreover, we validated our model in an Asian population and found that the AUC of our model in predicting the tumor origin of the Asian population was higher than 0.7. The metastatic tumor lesions inherited the MS pattern of the primary tumor, suggesting the capability of MS in identifying the tissue-of-origin for metastatic cancers. Furthermore, we distinguished breast cancer and prostate cancer with 90% accuracy by combining somatic mutations and CTS-MS from cfDNA, indicating that the CTS-MS could improve the accuracy of cancer-type prediction by cfDNA. In summary, our study demonstrated that MS was a novel reliable biomarker for diagnosing solid tumors and provided new insights into predicting tissue-of-origin.

Tumor microenvironment-aware, single-transcriptome prediction of microsatellite instability in colorectal cancer using meta-analysis

Detecting microsatellite instability (MSI) in colorectal cancers (CRCs) is essential because it is the determinant of treatment strategies, including immunotherapy and chemotherapy. Yet, no attempt has been made to exploit transcriptomic profile and tumor microenvironment (TME) of it to unveil MSI status in CRC. Hence, we developed a novel TME-aware, single-transcriptome predictor of MSI for CRC, called MAP (Microsatellite instability Absolute single sample Predictor). MAP was developed utilizing recursive feature elimination-random forest with 466 CRC samples from The Cancer Genome Atlas, and its performance was validated in independent cohorts, including 1118 samples. MAP showed robustness and predictive power in predicting MSI status in CRC. Additional advantages for MAP were demonstrated through comparative analysis with existing MSI classifier and other cancer types. Our novel approach will provide access to untouched vast amounts of publicly available transcriptomic data and widen the door for MSI CRC research and be useful for gaining insights to help with translational medicine.

A Pan-Cancer Analysis on the Systematic Correlation of MutS Homolog 2 (MSH2) to a Malignant Tumor

MutS homolog 2 (MSH2) is a crucial participant in human DNA repair, and lots of the studies functionally associated with it were begun with hereditary nonpolyposis colorectal cancer (HNPCC). MSH2 has also been reported to take part in the progresses of various tumors' formation. With the help of GTEx, CCLE, and TCGA pan-cancer databases, the analysis of MSH2 gene distribution in both tumor tissues and normal control tissues was carried out. Kaplan-Meyer survival plots and COX regression analysis were conducted for the assessment into the MSH2's impact on tumor patients' clinical prognosis. In an investigation to the association of MSH2 expression with immune infiltration level of various tumors and a similar study on tumor immune neoantigens, microsatellite instability was subsequently taken. It was found that high expression of MSH2 is prevalent in most cancers. MSH2's efficacy on clinical prognosis as well as immune infiltration in tumor patients revealed a fact that expression of MSH2 in prostate adenocarcinoma (PRAD), brain lower-grade glioma (LGG), breast-invasive carcinoma (BRCA), and head and neck squamous cell carcinoma (HNSC) posed a significant correlation with the immune cell infiltration level of patients. Likewise as above, MSH2's expression comes in a similar trend with tumor immune neoantigens and microsatellite instability. MSH2's expression in the majority of tumors is a direct factor to the activation of tumor-associated pathways as well as immune-associated pathways. MSH2's early screening or even therapeutic target role for sarcoma (SARC) diagnosis is contributing to the efficiency of early screening and overall survival in SARC patients.

System Analysis of Adaptor-Related Protein Complex 1 Subunit Mu 2 (AP1M2) on Malignant Tumors: A Pan-Cancer Analysis

Objective

To identify new tumor marker genes available for early tumor screening, differentially expressed gene profiles of multiple tumors were compared using Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), and The Cancer Genome Atlas (TCGA) databases. As AP1M2 was highly and differentially expressed in invasive breast carcinoma, the purpose of this study was to explore the association of AP1M2 gene with the survival, immune invasion, and tumor neoantigens of patients on a pan-cancer basis.

Methods

The expression and distribution of AP1M2 gene in tumor tissues and the corresponding normal control tissues were analyzed using the pan-cancer databases GTEx, CCLE, and TCGA. Kaplan-Meyer survival plots and proportional hazards model (COX) were employed to evaluate actions of AP1M2 on the clinical prognosis of tumor patients. Subsequently, the association of AP1M2 expression with immune invasion in different tumor types was explored. Simultaneously, the investigation of the interrelationship of AP1M2 and tumor neoantigens of the immune system, unstable microsatellite, DNA repair genes, and DNA methyltransferases were explored, and the mutation frequency of AP1M2 gene in diverse tumors was studied. Several tumor types were analyzed using gene-set enrichment analysis (GSEA).

Results

AP1M2 was abundantly expressed in a wide range of cancers, and its expression level was positively correlated with the outcome of tumor victims. Through a study on AP1M2 action on clinical prognosis and immune infiltration in tumor patients, AP1M2 expression in breast-infiltrating carcinoma was found to be highly associated with patients' overall survival and infiltration levels of macrophages, dendritic cells, T cells (CD4+ and CD8+), and B cells. Also, AP1M2 expression was positively correlated with tumor immune neoantigens and microsatellite instability in breast invasive carcinoma. The effect of AP1M2 on tumors was analyzed by GSEA, and findings demonstrated that AP1M2 expression levels in most tumors influenced the activation of tumor-associated pathways and immune-associated pathways.

Conclusions

These findings suggest that AP1M2 expression levels are significantly correlated to patients' outcomes and levels of immune infiltration in most cancer types, including T cells (CD8+ and CD4+), macrophages, neutrophils, and dendritic cells (DCs), particularly in breast cancer. The results indicate that AP1M2 may influence the tumor environment of invasive breast cancer patients and it may be a target contributing to early screening and treatment for breast cancer, helping improve the efficiency of early screening and overall survival rate in invasive breast cancer patients.

Pan-Cancer Analysis Reveals the Multidimensional Expression and Prognostic and Immunologic Roles of VSTM2L in Cancer

Immunotherapy can improve survival in a variety of cancers by modulating the interaction between tumors and the tumor immune microenvironment (TIME). V-set and transmembrane domain containing 2 like (VSTM2L) regulates interleukin (IL)-4 signaling pathway—which involves immune-related factors—and has been linked to some cancers. However, the expression profile and prognostic significance of VSTM2L in different cancers as well as its relationship to the TIME are not known. This study investigated the pan-cancer expression profile, prognostic value, and immunologic relevance of VSTM2L. VSTM2L expression in different cancers was analyzed using the Cancer Cell Line Encyclopedia (CCLE), Human Protein Atlas (HPA), Tumor Immune Estimation Resource (TIMER), The Cancer Genome Atlas (TCGA), and Genotype–Tissue Expression (GTEx) portal. We examined the association between VSTM2L expression and clinical outcomes by Kaplan–Meier and Cox regression analyses using TCGA and Kaplan–Meier Plotter, and the results were validated in a Gene Expression Omnibus cohort. The correlations between VSTM2L expression and immune cell infiltration, immunomodulators, tumor mutation burden (TMB), microsatellite instability (MSI), and immune and stromal scores across cancers were analyzed using TCGA, TIMER, and Tumor–Immune System Interactions and Drugbank databases (TISIDB). The results showed that VSTM2L expression varied across cancers and its aberrant expression was associated with clinical outcomes: upregulation of VSTM2L was positively associated with advanced stage and reduced overall survival (OS), disease-specific survival (DSS), progression-free interval (PFI), and disease-free interval (DFI) in stomach adenocarcinoma (STAD); and its upregulation was associated with early-stage disease and improved OS, DSS, PFI, and DFI in kidney renal papillary cell carcinoma (KIRP). VSTM2L expression level was correlated with immune cell infiltration, expression of immunomodulators, TMB, MSI, and immune and stromal scores in multiple cancers. In conclusion, VSTM2L has prognostic value in various cancers and can predict both poor (STAD) and good (KIRP) outcomes. The relationship between VSTM2L expression and immune markers suggests a role in modulating the TIME.

Systematic Pan-Cancer Analysis of KIF23 and a Prediction Model Based on KIF23 in Clear Cell Renal Cell Carcinoma (ccRCC)

Purpose

This study aims to carry out a pan-cancer analysis of kinesin family member 23 (KIF23) and construct a predictive model for the prognosis of clear cell renal cell carcinoma (ccRCC) patients.

Methods

We evaluated the differential expression of KIF23 in pan-cancer by The Cancer Genome Atlas (TCGA) and Oncomine database. Then, the correlation between KIF23 with prognosis, clinical grade, stage, immune subtype, tumor mutation burden (TMB), microsatellite instability (MSI) and immune microenvironment was explored by TCGA, an integrated repository portal for tumor-immune system interactions (TISIDB) and cBioPortal. Subsequently, we screened out ferroptosis-related genes (FRGs) related to KIF23 and constructed a risk score model. Univariate Cox analysis was used to determine independent prognostic factors for ccRCC overall survival (OS), and a nomogram was established. Furthermore, gene set enrichment analysis (GSEA) was applied to study the biological functions and pathways of KIF23. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was carried out to evaluate the expression of KIF23.

Results

KIF23 was highly expressed in most tumors. Further, KIF23 was strongly correlated with prognosis, clinical grade, stage, immune subtype, TMB, MSI and immune microenvironment in different tumors. We found that KIF23 was significantly associated with all aspects of ccRCC. Then, 8 FRGs were identified to construct a risk score model together with KIF23. And a prognostic nomogram prediction model of OS was established. After GSEA analysis, cell cycle, condensed chromosome and other physiological processes were screened out. Finally, qRT-PCR verified the high expression of KIF23 in ccRCC cell lines than normal kidney cell line.

Conclusion

KIF23 may act as a pivotal part in occurrence and progression of different tumors. In ccRCC, KIF23 can be a great prognostic biomarker, and the nomogram based on KIF23 may contribute to better treatment plans for ccRCC patients.

Image-based assessment of extracellular mucin-to-tumor area predicts consensus molecular subtypes (CMS) in colorectal cancer

The backbone of all colorectal cancer classifications including the consensus molecular subtypes (CMS) highlights microsatellite instability (MSI) as a key molecular pathway. Although mucinous histology (generally defined as >50% extracellular mucin-to-tumor area) is a "typical" feature of MSI, it is not limited to this subgroup. Here, we investigate the association of CMS classification and mucin-to-tumor area quantified using a deep learning algorithm, and  the expression of specific mucins in predicting CMS groups and clinical outcome. A weakly supervised segmentation method was developed to quantify extracellular mucin-to-tumor area in H&E images. Performance was compared to two pathologists' scores, then applied to two cohorts: (1) TCGA (n = 871 slides/412 patients) used for mucin-CMS group correlation and (2) Bern (n = 775 slides/517 patients) for histopathological correlations and next-generation Tissue Microarray construction. TCGA and CPTAC (n = 85 patients) were used to further validate mucin detection and CMS classification by gene and protein expression analysis for MUC2, MUC4, MUC5AC and MUC5B. An excellent inter-observer agreement between pathologists' scores and the algorithm was obtained (ICC = 0.92). In TCGA, mucinous tumors were predominantly CMS1 (25.7%), CMS3 (24.6%) and CMS4 (16.2%). Average mucin in CMS2 was 1.8%, indicating negligible amounts. RNA and protein expression of MUC2, MUC4, MUC5AC and MUC5B were low-to-absent in CMS2. MUC5AC protein expression correlated with aggressive tumor features (e.g., distant metastases (p = 0.0334), BRAF mutation (p < 0.0001), mismatch repair-deficiency (p < 0.0001), and unfavorable 5-year overall survival (44% versus 65% for positive/negative staining). MUC2 expression showed the opposite trend, correlating with less lymphatic (p = 0.0096) and venous vessel invasion (p = 0.0023), no impact on survival.The absence of mucin-expressing tumors in CMS2 provides an important phenotype-genotype correlation. Together with MSI, mucinous histology may help predict CMS classification using only histopathology and should be considered in future image classifiers of molecular subtypes.

Experimental and Meta-Analytic Validation of RNA Sequencing Signatures for Predicting Status of Microsatellite Instability

Microsatellite instability (MSI) is an important diagnostic and prognostic cancer biomarker. In colorectal, cervical, ovarian, and gastric cancers, it can guide the prescription of chemotherapy and immunotherapy. In laboratory diagnostics of susceptible tumors, MSI is routinely detected by the size of marker polymerase chain reaction products encompassing frequent microsatellite expansion regions. Alternatively, MSI status is screened indirectly by immunohistochemical interrogation of microsatellite binding proteins. RNA sequencing (RNAseq) profiling is an emerging source of data for a wide spectrum of cancer biomarkers. Recently, three RNAseq-based gene signatures were deduced for establishing MSI status in tumor samples. They had 25, 15, and 14 gene products with only one common gene. However, they were developed and tested on the incomplete literature of The Cancer Genome Atlas (TCGA) sampling and never validated experimentally on independent RNAseq samples. In this study, we, for the first time, systematically validated these three RNAseq MSI signatures on the literature colorectal cancer (CRC) (n = 619), endometrial carcinoma (n = 533), gastric cancer (n = 380), uterine carcinosarcoma (n = 55), and esophageal cancer (n = 83) samples and on the set of experimental CRC RNAseq samples (n = 23) for tumors with known MSI status. We found that all three signatures performed well with area under the curve (AUC) ranges of 0.94–1 for the experimental CRCs and 0.94–1 for the TCGA CRC, esophageal cancer, and uterine carcinosarcoma samples. However, for the TCGA endometrial carcinoma and gastric cancer samples, only two signatures were effective with AUC 0.91–0.97, whereas the third signature showed a significantly lower AUC of 0.69–0.88. Software for calculating these MSI signatures using RNAseq data is included.

Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains the most lethal type of cancer. The 5-year survival rate for patients with early-stage diagnosis can be as high as 20%, suggesting that early diagnosis plays a pivotal role in the prognostic improvement of PDAC cases. In the medical field, the broad availability of biomedical data has led to the advent of the "big data" era. To overcome this deadly disease, how to fully exploit big data is a new challenge in the era of precision medicine. Artificial intelligence (AI) is the ability of a machine to learn and display intelligence to solve problems. AI can help to transform big data into clinically actionable insights more efficiently, reduce inevitable errors to improve diagnostic accuracy, and make real-time predictions. AI-based omics analyses will become the next alterative approach to overcome this poor-prognostic disease by discovering biomarkers for early detection, providing molecular/genomic subtyping, offering treatment guidance, and predicting recurrence and survival. Advances in AI may therefore improve PDAC survival outcomes in the near future. The present review mainly focuses on recent advances of AI in PDAC for clinicians. We believe that breakthroughs will soon emerge to fight this deadly disease using AI-navigated precision medicine.

Integrated Analysis of Copy Number Variation, Microsatellite Instability, and Tumor Mutation Burden Identifies an 11-Gene Signature Predicting Survival in Breast Cancer

Genetic variants such as copy number variation (CNV), microsatellite instability (MSI), and tumor mutation burden (TMB) have been reported to associate with the immune microenvironment and prognosis of patients with breast cancer. In this study, we performed an integrated analysis of CNV, MSI, and TMB data obtained from The Cancer Genome Atlas, thereby generating two genetic variants-related subgroups. We characterized the differences between the two subgroups in terms of prognosis, MSI burden, TMB, CNV, mutation landscape, and immune landscape. We found that cluster 2 was marked by a worse prognosis and lower TMB. According to these groupings, we identified 130 differentially expressed genes, which were subjected to univariate and least absolute shrinkage and selection operator-penalized multivariate modeling. Consequently, we constructed an 11-gene signature risk model called the genomic variation-related prognostic risk model (GVRM). Using ROC analysis and a calibration plot, we estimated the prognostic prediction of this GVRM. We confirmed the predictive efficiency of this GVRM by validating it in another independent International Cancer Genome Consortium cohort. Our results conclude that an 11-gene signature developed by integrated analysis of CNV, MSI, and TMB has a high potential to predict breast cancer prognosis, which provided a strong rationale for further investigating molecular mechanisms and guiding clinical decision-making in breast cancer.

Identification of gastric cancer subtypes based on pathway clustering

Gastric cancer (GC) is highly heterogeneous in the stromal and immune microenvironment, genome instability (GI), and oncogenic signatures. However, a classification of GC by combining these features remains lacking. Using the consensus clustering algorithm, we clustered GCs based on the activities of 15 pathways associated with immune, DNA repair, oncogenic, and stromal signatures in three GC datasets. We identified three GC subtypes: immunity-deprived (ImD), stroma-enriched (StE), and immunity-enriched (ImE). ImD showed low immune infiltration, high DNA damage repair activity, high tumor aneuploidy level, high intratumor heterogeneity (ITH), and frequent TP53 mutations. StE displayed high stromal signatures, low DNA damage repair activity, genomic stability, low ITH, and poor prognosis. ImE had strong immune infiltration, high DNA damage repair activity, high tumor mutation burden, prevalence of microsatellite instability, frequent ARID1A mutations, elevated PD-L1 expression, and favorable prognosis. Based on the expression levels of four genes (TAP2, SERPINB5, LTBP1, and LAMC1) in immune, DNA repair, oncogenic, and stromal pathways, we developed a prognostic model (IDOScore). The IDOScore was an adverse prognostic factor and correlated inversely with immunotherapy response in cancer. Our identification of new GC subtypes provides novel insights into tumor biology and has potential clinical implications for the management of GCs.

Tryptophan 2, 3‑dioxygenase promotes proliferation, migration and invasion of ovarian cancer cells

Tryptophan 2,3-dioxygenase (TDO2) is a key rate-limiting enzyme in the kynurenine pathway and promotes tumor growth and escape from immune surveillance in different types of cancer. The present study aimed to investigate whether TDO2 serves a role in the development of ovarian cancer. Reverse transcription-quantitative PCR and western blotting were used to detect the expression of TDO2 in different cell lines. The effects of TDO2 overexpression, TDO2 knockdown and TDO2 inhibitor on ovarian cancer cell proliferation, migration and invasion were determined by MTS, colony formation and Transwell assays. The expression of TDO2 in ovarian cancer tissues, normal ovarian tissues and fallopian tube tissues were analyzed using the gene expression data from The Cancer Genome Atlas and Genotype-Tissue Expression project. Immune cell infiltration in cancer tissues was evaluated using the single sample gene set enrichment analysis algorithm. The present study found that Ras^V12-mediated oncogenic transformation was accompanied by the upregulation of TDO2. In addition, it was demonstrated that TDO2 was upregulated in ovarian cancer tissues compared with normal ovarian tissues. TDO2 overexpression promoted proliferation, migration and invasion of ovarian cancer cells, whereas TDO2 knockdown repressed these phenotypes. Treatment with LM10, a TDO2 inhibitor, also repressed the proliferation, migration and invasion of ovarian cancer cells. The present study indicated that TDO2 can be used as a new target for the treatment of ovarian cancer.

Detection of Microsatellite Instability: State of the Art and Future Applications in Circulating Tumour DNA (ctDNA)

Simple Summary

Microsatellite instability (MSI) is a molecular fingerprint for defects in the mismatch repair system (dMMR) and is associated with higher risks of cancers. MSI/dMMR tumours are characterized by the accumulation of mutations throughout the genome, and particularly in microsatellite (MS) DNA repeat sequences. MSI stands as a major biomarker for familial cancer risk assessment, cancer prognosis, and therapeutic choices. Standard-of-care classification of MSI/dMMR tumours is most frequently achieved using immunohistochemistry or PCR-based assay directed against a set of five MS regions. However, novel molecular methods based on tumour tissue or plasma samples have been developed and could enter in the future trends of MSI testing. Here, we provide insights into these emerging approaches and discuss their advantages and limitations.

Abstract

Microsatellite instability (MSI) is a molecular scar resulting from a defective mismatch repair system (dMMR) and associated with various malignancies. MSI tumours are characterized by the accumulation of mutations throughout the genome and particularly clustered in highly repetitive microsatellite (MS) regions. MSI/dMMR status is routinely assessed in solid tumours for the initial screening of Lynch syndrome, the evaluation of cancer prognosis, and treatment decision-making. Currently, pentaplex PCR-based methods and MMR immunohistochemistry on tumour tissue samples are the standard diagnostic methods for MSI/dMMR. Other tissue methods such as next-generation sequencing or real-time PCR-based systems have emerged and represent viable alternatives to standard MSI testing in specific settings. The evolution of the standard molecular techniques has offered the opportunity to extend MSI determination to liquid biopsy based on the analysis of cell-free DNA (cfDNA) in plasma. This review aims at synthetizing the standard and emerging techniques used on tumour tissue samples for MSI/dMMR determination. We also provide insights into the MSI molecular techniques compatible with liquid biopsy and the potential clinical consequences for patients with solid cancers.

Zic Family Member 2 (ZIC2): a Potential Diagnostic and Prognostic Biomarker for Pan-Cancer

Background: As a transcription factor, Zinc finger protein ZIC2 can interact with various DNAs and proteins. Current studies have shown that ZIC2 plays an oncogene role in various cancers. In this study, we systematically characterize the prevalence and predictive value of ZIC2 expression across multiple cancer types. Methods: We mined several public databases, including Oncomine, the Cancer Genome Atlas (TCGA), cBioPortal, Kaplan-Meier Plotter and PrognoScan to evaluated the differentially expressed ZIC2 between tumor samples and normal control samples in pan-cancner, and then explored the association between ZIC2 expression and patient survival, prognosis and clinicopathologic stage. We also analyzed the relationship between tumor mutation burden (TMB), microsatellite instability (MSI), tumor microenvironment, tumor- and immune-related genes and ZIC2 expression. Finally, we explored the potential signaling pathway mechanism through gene set enrichment analysis (GSEA). Results: ZIC2 expression was higher in most cancer tissues compared with adjacent normal tissues. High ZIC2 expression was associated with worse prognosis and a higher clinicopathologic stage. ZIC2 expression was strongly associated with the TMB, MSI, tumor microenvironment and tumor- and immune-related genes. The GSEA revealed that multiple tumor- and immune-related pathways were differentially enriched in ZIC2 high or low expression phenotype. Conclusion: ZIC2 expression may be a potential prognostic molecular biomarker of poor survival in pan-cancer and may act as an oncogene with a strong effect in the processes of tumorigenesis and progression.

Multi-omics characterization and validation of MSI-related molecular features across multiple malignancies

HEADINGS AIMS

To establish a microsatellite instability (MSI) predictive model in pan-cancer and compare the multi-omics characterization of MSI-related molecular features.

MATERIALS AND METHODS

We established a 15-gene signature for predicting MSI status and performed a systematic assessment of MSI-related molecular features including gene and miRNA expression, DNA methylation, and somatic mutation, in approximately 10,000 patients across 30 cancer types from The Cancer Genome Atlas, Gene Expression Omnibus database, and our institution. Then we identified common MSI-associated dysregulated molecular features across six cancers and explored their mutual interfering relationships and the drug sensitivity.

KEY FINDINGS

we demonstrated the model's high prediction performance and found the samples with high-MSI were mainly distributed in six cancers: BRCA, COAD, LUAD, LIHC, STAD, and UCEC. We found RPL22L1 was up-regulated in the high-MSI group of 5/6 cancer types. CYP27A1 and RAI2 were down-regulated in 4/6 cancer types. More than 20 miRNAs and 39 DMGs were found up-regulated in MSI-H at least three cancers. We discovered some drugs, including OSI-027 and AZD8055 had a higher sensitivity in the high MSI-score group. Functional enrichment analysis revealed the correlation between MSI score and APM score, HLA score, or glycolysis score. The complicated regulatory mechanism of tumor MSI status in multiple dimensions was explored by an integrated analysis of the correlations among MSI-related genes, miRNAs, methylation, and drug response data.

SIGNIFICANCE

Our pan-cancer study provides a valuable predictive model and a comprehensive atlas of tumor MSI, which may guide more precise and personalized therapeutic strategies for tumor patients.

Heat Shock Protein 90 Family Isoforms as Prognostic Biomarkers and Their Correlations with Immune Infiltration in Breast Cancer

Background

The heat shock protein 90 (HSP90s) family is composed of molecular chaperones composed of four isoforms in humans, which has been widely reported as unregulated in various kinds of cancers. Nevertheless, the role of each HSP90s isoform in prognosis and immune infiltration in distinct subtypes of breast cancer (BRAC) remains unclear.

Methods

Public online databases including the Oncomine, UALCAN, Kaplan-Meier Plotter, Tumor IMmune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA), GeneMANIA, and Database for Annotation, Visualization, and Integrated Discovery (DAVID) were integrated to perform bioinformatic analyses and to explore the possible associations among HSP90s gene expression, prognosis, and immune infiltration in BRAC.

Results

The mRNA expression of all HSP90s members was elevated in distinct clinical stages and subtypes of BRAC, compared with the normal breast tissue (P < 0.05). Overexpressed HSP90AA1 was associated with poor prognosis, particularly, both short overall survival (OS) and release-free survival (RFS) in Basal-like BRAC patients; overexpressed HSP90AB1 and HSP90B1 were both associated with poor RFS in Luminal A BRAC patients, while overexpressed TRAP1 was associated with favorable RFS in Luminal A BRAC patients. Moreover, HSP90s gene expression in BRAC showed correlations with the infiltration of CD8+ T cells, neutrophils, macrophages, and dendritic cells (DCs), as well as the activation of tumor-associated macrophages (TAMs), DCs, and CD4+ helper T (Th) cells. The underlying mechanisms of HSP90s modulating tumor-infiltrating immune cells (TIICs) might be related with their functions in antigen processing and presentation, major histocompatibility complex (MHC) binding, and assisting client proteins.

Conclusion

This study demonstrated that HSP90s family genes were overexpressed and might be serve as prognostic biomarkers in subtypes of BRAC. It might be a novel breakthrough point of BRAC treatment to regulate immune infiltration in BRAC microenvironment for more effective anticancer immunity through pharmacological intervention of HSP90s.

The Molecular Effects of a High Fat Diet on Endometrial Tumour Biology

We sought to validate the BDII/Han rat model as a model for diet-induced obesity in endometrial cancer (EC) and determine if transcriptomic changes induced by a high fat diet (HFD) in an EC rat model can be used to identify novel biomarkers in human EC. Nineteen BDII/Han rats were included. Group A (n = 7) were given ad lib access to a normal calorie, normal chow diet (NCD) while Group B (n = 12) were given ad lib access to a calorie rich HFD for 15 months. RNAseq was performed on endometrial tumours from both groups. The top-ranking differentially expressed genes (DEGs) were examined in the human EC using The Cancer Genome Atlas (TCGA) to assess if the BDII/Han rat model is an appropriate model for human obesity-induced carcinogenesis. Weight gain in HFD rats was double the weight gain of NCD rats (50 g vs. 25 g). The incidence of cancer was similar in both groups (4/7-57% vs. 4/12-33%; p = 0.37). All tumours were equivalent to a Stage 1A, Grade 2 human endometrioid carcinoma. A total of 368 DEGs were identified between the tumours in the HFD group compared to the NCD group. We identified two upstream regulators of the DEGs, mir-33 and Brd4, and a pathway analysis identified downstream enrichment of the colorectal cancer metastasis and ovarian cancer metastasis pathways. Top-ranking DEGs included Tex14, A2M, Hmgcs2, Adamts5, Pdk4, Crabp2, Capn12, Npw, Idi1 and Gpt. A2M expression was decreased in HFD tumours. Consistent with these findings, we found a significant negative correlation between A2M mRNA expression levels and BMI in the TCGA cohort (Spearman's Rho = -0.263, p < 0.001). A2M expression was associated with improved overall survival (HR = 0.45, 95% CI 0.23-0.9, p = 0.024). Crabp2 expression was increased in HFD tumours. In human EC, CRABP2 expression was associated with reduced overall survival (HR = 3.554, 95% CI 1.875-6.753, p < 0.001). Diet-induced obesity can alter EC transcriptomic profiles. The BDII/Han rat model is a suitable model of diet-induced obesity in endometrial cancer and can be used to identify clinically relevant biomarkers in human EC.