Digital analysis and epigenetic regulation of the signature of rejection in colorectal cancer

The interferon regulatory factor 6 promotes cisplatin sensitivity in colorectal cancer

Colorectal cancer (CRC) is one of the most common malignancies and causes of cancer-related mortality worldwide. Cell proliferation and tumor metastasis as well as chemoresistance are correlated with poor survival of CRC. The interferon regulatory factor 6 (IRF6) is functioned as a tumor suppressor gene in several cancers and is associated with risk of CRC. We explored the role of IRF6 in CRC in the present study. The protein expressions of IRF6 in human CRC tissues, normal para-carcinoma tissue and liver metastases from CRC were measured. Cell proliferation, chemotherapeutic sensitivity, cell apoptosis, migration and invasion including the related markers along with IRF6 expression were explored. Our results indicated that IRF6 expression in CRC and liver metastasis were lower than normal tissues, which were correlated positively with E-cadherin and negatively with Ki67 expression in CRC tissue. IRF6 promoted CRC cell sensitivity to cisplatin to suppress cell proliferation, migration and invasion as well as aggravate cell apoptosis. Our study suggested that IRF6 may enhance chemotherapeutic sensitivity of cisplatin mediated by affecting cell proliferation, migration and invasion along with apoptosis through regulating E-cadherin and Ki67, while the identified molecular mechanisms remain to be further explored.

Identification of Lactate-Related Gene Signature for Prediction of Progression and Immunotherapeutic Response in Skin Cutaneous Melanoma

Skin cutaneous melanoma (SKCM) is a skin cancer type characterized by a high degree of immune cell infiltration. The potential function of lactate, a main metabolic product in the tumor microenvironment (TME) of SKCM, remains unclear. In this study, we systemically analyzed the predictive value of lactate-related genes (LRGs) for prognosis and response to immune checkpoint inhibitors (ICIs) in SKCM patients included from The Cancer Genome Atlas (TCGA) database. Cluster 3, by consensus clustering for 61 LRGs, manifested a worse clinical outcome, attributed to the overexpression of malignancy marks. In addition, we created a prognostic prediction model for high- and low-risk patients and verified its performance in a validation cohort, GSE65904. Between TME and the risk model, we found a negative relation of the immunocyte infiltration levels with patients’ risk scores. The low-risk cases had higher ICI expression and could benefit better from ICIs relative to the high-risk cases. Thus, the lactate-related prognosis risk signature may comprehensively provide a basis for future investigations on immunotherapeutic treatment for SKCM.

Clinical Significance of a Novel Tumor Progression-Associated Immune Signature in Colorectal Adenocarcinoma

Background

Some colorectal adenocarcinoma (CRC) patients are susceptible to recurrence, and they rapidly progress to advanced cancer stages and have a poor prognosis. There is an urgent need for efficient screening criteria to identify patients who tend to relapse in order to treat them earlier and more systematically.

Methods

We identified two groups of patients with significantly different outcomes by unsupervised cluster analysis of GSE39582 based on 101 significantly differentially expressed immune genes. To develop an accurate and specific signature based on immune-related genes to predict the recurrence of CRC, a multivariate Cox risk regression model was constructed with a training cohort composed of 519 CRC samples. The model was then validated using 129, 292, and 446 samples in the real-time quantitative reverse transcription PCR (qRT-PCR), test, and validation cohorts, respectively.

Results

This classification system can also be used to predict the prognosis in clinical subgroups and patients with different mutation states. Four independent datasets, including qRT-PCR and The Cancer Genome Atlas (TCGA), demonstrated that they can also be used to accurately predict the overall survival of CRC patients. Further analysis suggested that high-risk patients were characterized by worse effects of chemotherapy and immunotherapy, as well as lower immune scores. Ultimately, the signature was identified as an independent prognostic factor.

Conclusion

The signature can accurately predict recurrence and overall survival in patients with CRC and may serve as a powerful prognostic tool to further optimize cancer immunotherapy.

Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature

Head and neck tumors are the sixth most common neoplasms. Multiomics integrates multiple dimensions of clinical, pathologic, radiological, and biological data and has the potential for tumor diagnosis and analysis. Deep learning (DL), a type of artificial intelligence (AI), is applied in medical image analysis. Among the DL techniques, the convolution neural network (CNN) is used for image segmentation, detection, and classification and in computer-aided diagnosis. Here, we reviewed multiomics image analysis of head and neck tumors using CNN and other DL neural networks. We also evaluated its application in early tumor detection, classification, prognosis/metastasis prediction, and the signing out of the reports. Finally, we highlighted the challenges and potential of these techniques.

A Systemic Review on the Regulatory Roles of miR-34a in Gastrointestinal Cancer

MicroRNAs (miRNAs) are a class of endogenous non-coding single-stranded small-molecule RNAs that regulate gene expression by repressing target messenger RNA (mRNA) translation or degrading mRNA. miR-34a is one of the most important miRNAs participating in various physiological and pathological processes. miR-34a is abnormally expressed in a variety of tumors. The roles of miR-34a in gastrointestinal cancer (GIC) draw lots of attention. Numerous studies have demonstrated that dysregulated miR-34a is closely related to the proliferation, differentiation, migration, and invasion of tumor cells, as well as the diagnosis, prognosis, treatment, and chemo-resistance of tumors. Thus, we systematically reviewed the abnormal expression and regulatory roles of miR-34a in GICs including esophageal cancer (EC), gastric cancer (GC), colorectal cancer (CRC), hepatocellular carcinoma (HCC), pancreatic cancer (PC), and gallbladder cancer (GBC). It may provide a profile of versatile roles of miR-34a in GICs.

A Digital Pathology-Based Shotgun-Proteomics Approach to Biomarker Discovery in Colorectal Cancer

Background

Biomarkers in colorectal cancer are scarce, especially for patients with Stage 2 disease. The aim of our study was to identify potential prognostic biomarkers from colorectal cancers using a novel combination of approaches, whereby digital pathology is coupled to shotgun proteomics followed by validation of candidates by immunohistochemistry (IHC) using digital image analysis (DIA).

Methods and Results

Tissue cores were punched from formalin-fixed paraffin-embedded colorectal cancers from patients with Stage 2 and 3 disease (n = 26, each). Protein extraction and liquid chromatography-mass spectrometry (MS) followed by analysis using three different methods were performed. Fold changes were evaluated. The candidate biomarker was validated by IHC on a series of 413 colorectal cancers from surgically treated patients using a next-generation tissue microarray. DIA was performed by using a pan-cytokeratin serial alignment and quantifying staining within the tumor and normal tissue epithelium. Analysis was done in QuPath and Brightness_Max scores were used for statistical analysis and clinicopathological associations. MS identified 1947 proteins with at least two unique peptides. To reinforce the validity of the biomarker candidates, only proteins showing a significant (P < 0.05) fold-change using all three analysis methods were considered. Eight were identified, and of these, cathepsin B was selected for further validation. DIA revealed strong associations between higher cathepsin B expression and less aggressive tumor features, including tumor node metastasis stage and lymphatic vessel and venous vessel invasion (P < 0.001, all). Cathepsin B was associated with more favorable survival in univariate analysis only.

Conclusions

Our results present a novel approach to biomarker discovery that includes MS and digital pathology. Cathepsin B expression analyzed by DIA within the tumor epithelial compartment was identified as a strong feature of less aggressive tumor behavior and favorable outcome, a finding that should be further investigated on a more functional level.

Regulatory network reconstruction of five essential microRNAs for survival analysis in breast cancer by integrating miRNA and mRNA expression datasets

Although many of the genetic loci associated with breast cancer risk have been reported, there is a lack of systematic analysis of regulatory networks composed of different miRNAs and mRNAs on survival analysis in breast cancer. To reconstruct the microRNAs-genes regulatory network in breast cancer, we employed the expression data from The Cancer Genome Atlas (TCGA) related to five essential miRNAs including miR-21, miR-22, miR-210, miR-221, and miR-222, and their associated functional genomics data from the GEO database. Then, we performed an integration analysis to identify the essential target factors and interactions for the next survival analysis in breast cancer. Based on the results of our integrated analysis, we have identified significant common regulatory signatures including differentially expressed genes, enriched pathways, and transcriptional regulation such as interferon regulatory factors (IRFs) and signal transducer and activator of transcription 1 (STAT1). Finally, a reconstructed regulatory network of five miRNAs and 34 target factors was established and then applied to survival analysis in breast cancer. When we used expression data for individual miRNAs, only miR-21 and miR-22 were significantly associated with a survival change. However, we identified 45 significant miRNA-gene pairs that predict overall survival in breast cancer out of 170 one-on-one interactions in our reconstructed network covering all of five miRNAs, and several essential factors such as PSMB9, HLA-C, RARRES3, UBE2L6, and NMI. In our study, we reconstructed regulatory network of five essential microRNAs for survival analysis in breast cancer by integrating miRNA and mRNA expression datasets. These results may provide new insights into regulatory network-based precision medicine for breast cancer.

Precision immunoprofiling by image analysis and artificial intelligence

Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This requirement can be met by a combination of computational pathology and artificial intelligence. Here, we critically assess computational approaches supporting the development of a standardized methodology in the assessment of immune-oncology biomarkers, such as PD-L1 and immune cell infiltrates. We examine immunoprofiling through spatial analysis of tumor-immune cell interactions and multiplexing technologies as a predictor of patient response to cancer treatment. Further, we discuss how integrated bioinformatics can enable the amalgamation of complex morphological phenotypes with the multiomics datasets that drive precision medicine. We provide an outline to machine learning (ML) and artificial intelligence tools and illustrate fields of application in immune-oncology, such as pattern-recognition in large and complex datasets and deep learning approaches for survival analysis. Synergies of surgical pathology and computational analyses are expected to improve patient stratification in immuno-oncology. We propose that future clinical demands will be best met by (1) dedicated research at the interface of pathology and bioinformatics, supported by professional societies, and (2) the integration of data sciences and digital image analysis in the professional education of pathologists.

Prognostic values of distinct CBX family members in breast cancer

Chromobox (CBX) family proteins are canonical components in polycomb repressive complexes 1 (PRC1), with epigenetic regulatory function and transcriptionally repressing target genes via chromatin modification. A plethora of studies have highlighted the function specifications among CBX family members in various cancer, including lung cancer, colon cancer and breast cancer. Nevertheless, the functions and prognostic roles of distinct CBX family members in breast cancer (BC) remain elusive. In this study, we reported the prognostic values of CBX family members in patients with BC through analysis of a series of databases, including CCLE, ONCOMINE, Xena Public Data Hubs, and Kaplan-Meier plotter. It was found that the mRNA expression of CBX family members were noticeably higher in BC than normal counterparts. CBX2 was highly expressed in Basal-like and HER-2 subtypes, while CBX4 and CBX7 expressions were enriched in Luminal A and Luminal B subtypes of BC. Survival analysis revealed that CBX1, CBX2 and CBX3 mRNA high expression was correlated to worsen relapse-free survival (RFS) for all BC patients, while CBX4, CBX5, CBX6 and CBX7 high expression was correlated to better RFS in this setting. Noteworthily, CBX1 and CBX2 were associated with chemoresistance whereas CBX7 was associated with tamoxifen sensitivity, as well as chemosensitivity in breast tumors. Therefore, we propose that CBX1, CBX2 and CBX7 are potential targets for BC treatment. The results might be beneficial for better understanding the complexity and heterogeneity in the molecular underpinning of BC, and to develop tools to more accurately predict the prognosis of patients with BC.