Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system

A prognostic binary classifier comprised of five critical mRNAs stratified pancreatic cancer patients following resection

Background

Pancreatic cancer is characterized by an extremely poor prognosis, even following potentially curative resection. Classical prognostic markers such as histopathological or clinical parameters have limited predictive power. The present study aimed to establish a prognostic model combining mRNA expression data with histopathological and clinical data to better predict survival and stratify pancreatic cancer patients following resection. We pioneered three models in one study and systematically evaluated the clinical benefits of all three models.

Methods

To identify differentially expressed genes in pancreatic cancer, mRNA data from normal (GTEx database) and pancreatic cancer (TCGA database) tissues were used. Survival analysis was carried out to identify prognosis-relevant genes from the identified differentially expressed genes and LASSO regression was used to filter out hub genes. The risk score of several hub genes was calculated according to gene expression and coefficients. Validation was carried out using an independent set of GEO microarray data. Multivariate COX regression was used for identifying independent clinical and pathological risk factors related to patient's survival in the TCGA database and a prognostic model combining mRNA expression data with histopathological and clinical data was established. Another prognostic model using clinicopathological factors from the SEER database was conceived based on multivariate COX regression. NRI (net reclassification improvement) and IDI (integrated discrimination index) were used to compare the predictive capabilities of the different models.

Results

We identified 1589 differentially expressed genes (DEGs) through the comparison of normal and pancreatic cancer tissues, of whom 317 were associated with prognosis(p < 0.05). LASSO regression identified five hub genes, MYEOV, ANXA2P2, MET, CEP55, and KRT7, that were used for the five-mRNA-classifier prognostic model. The classifier could stratify patients into a short and long survival group: 5-year overall survival in the training set (TCGA, 6 % vs 52 %, p < 0.001), test set (TCGA, 18 % vs 55 %,p < 0.01) and external validation set (GEO, 0 % vs 25 %, p < 0.05). Sensitivity analysis showed that the mRNA model (model 1) was better than the clinicopathological no-mRNA model (model 2) in predicting 5-year survival in the TCGA database (AUC: 0.877 vs 0.718, z = 3.165, p < 0.01) and better than the multi-factor prognostic model (model 3) from the SEER database (AUC: 0.754, z = 2.637, p < 0.01). On predictive performance, model 1 improved model 2 (NRI = 0.084, z = 1.288, p = 0.198; IDI = 0.055, z = 1.041,p = 0.298) and model 3 (NRI = 0.167,z = 1.961,p = 0.05; IDI = 0.086, z = 1.427, p = 0.154).

Conclusion

The five-mRNA-classifier is a reliable and feasible instrument to predict the prognosis of pancreatic cancer patients following resection. It might help in patiens counseling and assist clinicians in providing individualized treatment for patients in different risk groups.

Hyaluronic Acid Interacting Molecules Mediated Crosstalk between Cancer Cells and Microenvironment from Primary Tumour to Distant Metastasis

Hyaluronic acid (HA) is a prominent component of the extracellular matrix, and its interactions with HA-interacting molecules (HAIMs) play a critical role in cancer development and disease progression. This review explores the multifaceted role of HAIMs in the context of cancer, focusing on their influence on disease progression by dissecting relevant cellular and molecular mechanisms in tumour cells and the tumour microenvironment. Cancer progression can be profoundly affected by the interactions between HA and HAIMs. They modulate critical processes such as cell adhesion, migration, invasion, and proliferation. The TME serves as a dynamic platform in which HAIMs contribute to the formation of a unique niche. The resulting changes in HA composition profoundly influence the biophysical properties of the TME. These modifications in the TME, in conjunction with HAIMs, impact angiogenesis, immune cell recruitment, and immune evasion. Therefore, understanding the intricate interplay between HAIMs and HA within the cancer context is essential for developing novel therapeutic strategies. Targeting these interactions offers promising avenues for cancer treatment, as they hold the potential to disrupt critical aspects of disease progression and the TME. Further research in this field is imperative for advancing our knowledge and the treatment of cancer.

Using machine learning methods to study the tumour microenvironment and its biomarkers in osteosarcoma metastasis

Background

The long-term prognosis for patients with osteosarcoma (OS) metastasis remains unfavourable, highlighting the urgent need for research that explores potential biomarkers using innovative methodologies.

Methods

This study explored potential biomarkers for OS metastasis by analysing data from the Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO) databases. The synthetic minority oversampling technique (SMOTE) was employed to tackle class imbalances, while genes were selected using four feature selection algorithms (Monte Carlo feature selection [MCFS], Borota, minimum-redundancy maximum-relevance [mRMR], and light gradient-boosting machine [LightGBM]) based on the gene expression matrix. Four machine learning (ML) algorithms (support vector machine [SVM], extreme gradient boosting [XGBoost], random forest [RF], and k-nearest neighbours [kNN]) were utilized to determine the optimal number of genes for building the model. Interpretable machine learning (IML) was applied to construct prediction networks, revealing potential relationships among the selected genes. Additionally, enrichment analysis, survival analysis, and immune infiltration were performed on the featured genes.

Results

In DS1, DS2, and DS3, the IML algorithm identified 53, 45, and 46 features, respectively. Using the merged gene set, we obtained a total of 79 interpretable prediction rules for OS metastasis. We subsequently conducted an in-depth investigation on 39 crucial molecules associated with predicting OS metastasis, elucidating their roles within the tumour microenvironment. Importantly, we found that certain genes act as both predictors and differentially expressed genes. Finally, our study unveiled statistically significant differences in survival between the high and low expression groups of TRIP4, S100A9, SELL and SLC11A1, and there was a certain correlation between these genes and 22 various immune cells.

Conclusions

The biomarkers discovered in this study hold significant implications for personalized therapies, potentially enhancing the clinical prognosis of patients with OS.

Incorporation of emergent symptoms and genetic covariates improves prediction of aromatase inhibitor therapy discontinuation

Objectives

Early discontinuation is common among breast cancer patients taking aromatase inhibitors (AIs). Although several predictors have been identified, it is unclear how to simultaneously consider multiple risk factors for an individual. We sought to develop a tool for prediction of AI discontinuation and to explore how predictive value of risk factors changes with time.

Materials and Methods

Survival machine learning was used to predict time-to-discontinuation of AIs in 181 women who enrolled in a prospective cohort. Models were evaluated via time-dependent area under the curve (AUC), c-index, and integrated Brier score. Feature importance was analysis was conducted via Shapley Additive Explanations (SHAP) and time-dependence of their predictive value was analyzed by time-dependent AUC. Personalized survival curves were constructed for risk communication.

Results

The best-performing model incorporated genetic risk factors and changes in patient-reported outcomes, achieving mean time-dependent AUC of 0.66, and AUC of 0.72 and 0.67 at 6- and 12-month cutoffs, respectively. The most significant features included variants in ESR1 and emergent symptoms. Predictive value of genetic risk factors was highest in the first year of treatment. Decrease in physical function was the strongest independent predictor at follow-up.

Discussion and Conclusion

Incorporation of genomic and 3-month follow-up data improved the ability of the models to identify the individuals at risk of AI discontinuation. Genetic risk factors were particularly important for predicting early discontinuers. This study provides insight into the complex nature of AI discontinuation and highlights the importance of incorporating genetic risk factors and emergent symptoms into prediction models.

MHC class III lymphocyte antigens 6 as endogenous immunotoxins: Unlocking immunotherapy in proficient mismatch repair colorectal cancer

A majority of cancers, including colorectal cancer (CRC) with intact DNA mismatch repair, exhibit a paralyzed antitumor immune response and resistance to immune checkpoint inhibitors. Members of MHC class III lymphocyte antigen 6G (LY6G) encode glycosylphosphatidylinositol (GPI) proteins anchored to the membrane. Snake venom neurotoxins and LY6G proteins share a three-finger (3F) folding domain. LY6 proteins such as LY6G6D are gaining a reputation as excellent tumor-associated antigens that can potently inhibit anti-tumor immunity in cancers with proficient mismatch repair. Thus, we called MHC class III LY6G endogenous immunotoxins. Since the discovery of LY6G6D as a tumor-associated antigen, T-cell engagers (TcEs) have been developed to simultaneously bind LY6G6D on cancer cells and CD3 on T cells, improving the treatment of metastatic solid tumors that are resistant to ICIs. We present a current understanding of how alterations in MHC class III genes inhibit antitumor immunity, and how these understandings can be turned into effective treatments for patients who are refractory to standard immunotherapy. This article is categorized under: Cancer > Genetics/Genomics/Epigenetics Cancer > Molecular and Cellular Physiology.

Identification of basement membrane-related signatures for estimating prognosis, immune infiltration landscape and drug candidates in pancreatic adenocarcinoma

Background: Pancreatic adenocarcinoma (PAAD) is a frequent digestive system cancer, which has high mortality and bad outcome. However, the role of basement membrane (BM)-related gene in assessing patient's outcome, microenvironment and treatment response remain unclear. Methods: Basement membrane (BM)-associated genes were detected by univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses using data from the TCGA databases. A risk score system was constructed to distinguish patients in the high- and low-risk groups. Prognostic gene distribution in various immune cell forms was explored through scRNA-seq. Immune cell infiltration was assessed using CIBERSORT and ESTIMATE. The IC50 of common chemotherapeutic drugs and useful molecule compounds were evaluated. The mRNA and protein expression of important signatures were validated utilizing GEPIA and HPA databases. Results: Compared to low risk PAAD patients, PAAD patients with high risk showed a significant much worse overall survival (OS). Risk score of BM-associated genes could estimate patient outcome well, and areas under the curve (AUC) of receiver operating characteristic (ROC) survival curve were 0.76, 0.85, and 0.85 at 1-, 3-, and 5-year. Clinical impact curve (CIC) curve demonstrated the clinical importance of risk score. scRNA-seq revealed that BM-related genes were mainly distributed in malignant cells. Significant variations existed in the immune microenvironment, immune checkpoint expression and chemotherapy response between the studied groups. Furthermore, the mRNA expression levels of FAM83A, LY6D, MET, MUC16, MYEOV, and WNT7A were elevated in PAAD tissues, while the protein expression patterns of LY6D, MET, MUC16, and WNT7A were higher in tumor sample. RO-90-7501, Scriptaid, TG-101348, XMD-892, and XMD-1150 may be valuable small molecule drugs to treat PAAD. Conclusions: In conclusion, we develop a novel BM-related gene signature provide new insights and targets for the diagnosis, outcome estimation, candidate drugs and therapy management of PAAD patients.

Bioinformatics analysis-based screening of circRNA gene with mainstream expression trend in colorectal cancer and construction of a coexpression regulatory network

OBJECTIVE

Since circRNA can be utilized as a potential diagnostic marker for cancer, to explore the regulatory mechanism of colorectal cancer (CRC) using bioinformatics, the public database of circRNA was mined.

METHODS

CRC differentially expressed miRNAs were screened in the Cancer Genome Atlas (TCGA) database, CRC differentially expressed circRNAs were searched in the Gene Expression Omnibus (GEO) database, the two databases were combined to identify CRC differentially expressed mRNAs, and a circRNA-miRNA‒mRNA regulatory network was constructed by combining a plurality of target prediction databases to identify key genes. The upstream circRNA and regulatory axis of the key genes were identified for gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis to explore the biological functions of circRNA in CRC using the regulatory axis.

RESULTS

After the screening of the GSE21815 dataset, a total of 22 differentially expressed circRNAs were obtained, with 12 upregulated and 10 downregulated genes. Similarly, the GSE126094 dataset yielded 104 differentially expressed circRNAs, comprising 56 upregulated and 48 downregulated genes. Among the differentially expressed circRNAs, five were identified, with VDAC3 and SETD2 showing downregulated expression, while RAD23B, RPPH1, and MYBL2 exhibited upregulated expression. Following the selection process, five DEcircRNAs, eight target miRNAs, and 105 target DEmRNAs were identified. The protein-protein interaction (PPI) network revealed close relationships among the mRNAs, with E2F2, E2F3, CCND1, TNRC6A, and KAT2B identified as key genes. Notably, CCND1 emerged as a critical gene in the PPI network. Through the upregulation of has-circ-0087862, which binds to miR-892b, the translation inhibition of CCND1 by miR-892b was attenuated, leading to enhanced CCND1 expression. Functional enrichment analysis indicated that CCND1 was involved in protein binding and positive regulation of cellular processes, among other functions.

CONCLUSION

The differentially expressed genes (DEGs) in CRC markedly affected the survival time of patients. CircRNAs could be utilized as diagnostic markers of CRC, and the key genes in CRC could be screened out by bioinformatics, which would be helpful to understand the drug targets for the treatment of human immunodeficiency virus (HIV)-related CRC patients.

Subcellular Expression Patterns of FKBP Prolyl Isomerase 10 (FKBP10) in Colorectal Cancer and Its Clinical Significance

FKBP10, a member of the FK506-binding protein (FKBP) family, has been implicated in cancer development, although its prognostic function remains controversial. In this study, we analyzed the expression of FKBP10 in tumor tissues using online databases (TCGA) as well as our CRC cohort, and investigated the relationship between its subcellular expression pattern and patient outcomes. Cox regression analysis was used to determine the associations between different subcellular expression patterns of FKBP10 and clinical features of patients. We also discussed the expression level of FKBP10 based on different subcellular expression patterns. Our results showed that FKBP10 was significantly elevated in CRC tissues and exhibited three different subcellular expression patterns which were defined as 'FKBP10-C' (concentrated), 'FKBP10-T' (transitional) and 'FKBP10-D' (dispersive). The FKBP10-D expression pattern was only found in tumor tissues and was associated with unfavorable disease-free survival in CRC patients. High expression levels of FKBP10-C predicted an unfavorable prognosis of recurrence of CRC, while FKBP10-D did not. Our findings suggest that the subcellular expression patterns and expression level of FKBP10 play crucial prognostic roles in CRC, which revealed that FKBP10 may be a viable prognostic and therapeutic target for the diagnosis and treatment of CRC.

Ectopic CH60 mediates HAPLN1-induced cell survival signaling in multiple myeloma

Multiple myeloma (MM), the second most common hematological malignancy, is generally considered incurable because of the development of drug resistance. We previously reported that hyaluronan and proteoglycan link protein 1 (HAPLN1) produced by stromal cells induces activation of NF-κB, a tumor-supportive transcription factor, and promotes drug resistance in MM cells. However, the identity of the cell surface receptor that detects HAPLN1 and thereby engenders pro-tumorigenic signaling in MM cells remains unknown. Here, we performed an unbiased cell surface biotinylation assay and identified chaperonin 60 (CH60) as the direct binding partner of HAPLN1 on MM cells. Cell surface CH60 specifically interacted with TLR4 to evoke HAPLN1-induced NF-κB signaling, transcription of anti-apoptotic genes, and drug resistance in MM cells. Collectively, our findings identify a cell surface CH60-TLR4 complex as a HAPLN1 receptor and a potential molecular target to overcome drug resistance in MM cells.

Bioinformatic analysis of the obesity paradox and possible associated factors in colorectal cancer using TCGA cohorts

Colorectal cancer (CRC) is a common malignancy worldwide and the second leading cause of cancer-related deaths. Obesity is an important determinant of CRC incidence; however, obese patients have also shown better long-term survival than non-obese patients, suggesting that the development and progression of CRC are associated with different mechanisms. This study compares the expression of genes, tumor-infiltrating immune cells, and intestinal microbiota between high- and low-body mass index (BMI) patients at the time of CRC diagnosis. The results revealed that high-BMI patients with CRC have better prognosis, higher levels of resting CD4+ T cells, lower levels of T follicular helper cells, and different levels of intratumoral microbiota than low-BMI patients. Our study highlights that tumor-infiltrating immune cells and intratumoral microbe diversity are major features of the obesity paradox in CRC.

Weighted gene co-expression network analysis combined with machine learning validation to identify key hub biomarkers in colorectal cancer

Colorectal cancer (CRC) is one of the most common malignancies worldwide; however, the potentially possible molecular biological mechanism of CRC is still not completely comprehended. This study aimed to confirm candidate key hub genes involved in the growth and development of CRC and their connection with immune infiltration as well as the related pathways. Gene expression data were selected from the GEO dataset. Hub genes for CRC were identified on the basis of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and LASSO regression. Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA) were applied to reveal possible functions of the differential genes. Single-sample GSEA (ssGSEA) was implemented to identify the relationship between immune cells infiltration and hub genes. Two hundred and sixty-two differentially expressed genes (DEGs) were identified. Three modules were acquired based on WGCNA, and the blue module presented the highest relevance with CRC. Ten hub genes (AQP8, B3GALT5, CDH3, CEMIP, CPM, FOXQ1, PLAC8, SCNN1B, SPINK5, and SST) were acquired with LASSO analysis as underlying biomarkers for CRC. Compared with normal tissues, CRC tissues presented significantly higher numbers of CD4 T cells, CD8 T cells, B cells, natural regulatory T (Treg) cells, and monocytes. The functional enrichment analyses demonstrated that hub genes were primarily enriched in metabolic process, inflammatory-related, and immune-related response. Ten hub genes were identified to be involved in the occurrence and development of CRC and may be deemed as novel biomarkers for clinical diagnosis and treatment.

Identification of biomarkers in nonalcoholic fatty liver disease: A machine learning method and experimental study

Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease. However, the early diagnosis of NAFLD is challenging. Thus, the purpose of this study was to identify diagnostic biomarkers of NAFLD using machine learning algorithms. Differentially expressed genes between NAFLD and normal samples were identified separately from the GEO database. The key DEGs were selected through a protein‒protein interaction network, and their biological functions were analysed. Next, three machine learning algorithms were selected to construct models of NAFLD separately, and the model with the smallest sample residual was determined to be the best model. Then, logistic regression analysis was used to judge the accuracy of the five genes in predicting the risk of NAFLD. A single-sample gene set enrichment analysis algorithm was used to evaluate the immune cell infiltration of NAFLD, and the correlation between diagnostic biomarkers and immune cell infiltration was analysed. Finally, 10 pairs of peripheral blood samples from NAFLD patients and normal controls were collected for RNA isolation and quantitative real-time polymerase chain reaction for validation. Taken together, CEBPD, H4C11, CEBPB, GATA3, and KLF4 were identified as diagnostic biomarkers of NAFLD by machine learning algorithms and were related to immune cell infiltration in NAFLD. These key genes provide novel insights into the mechanisms and treatment of patients with NAFLD.

Fc receptor-like 5 gene polymorphisms and mRNA expression are associated with liver fibrosis in chronic hepatitis B

Objective: To investigate the associations of Fc receptor-like 5 (FCRL5) gene polymorphisms and mRNA expression with liver fibrosis in chronic hepatitis B (CHB).

Methods: A total of 114 CHB patients with liver fibrosis and 120 CHB patients without liver fibrosis were selected for this study. The gender, age, body mass index (BMI), alanine transaminase (ALT) value, aspartate aminotransferase (AST) value, aspartate aminotransferase-to-platelet ratio index (APRI), and fibrosis index based on 4 factors (FIB-4) were recorded. Two polymorphisms of the FCRL5 gene (rs6427384 and rs6692977) were genotyped. The mRNA expression level of FCRL5 in peripheral blood monocytes was determined.

Results: ALT, AST, APRI, and FIB-4 in patients with fibrosis were significantly higher than those in non-fibrosis patients. There was statistically significant difference between fibrosis and non-fibrosis groups in the genotype distribution (χ2 = 7.805, p = 0.020) and allele frequencies (χ2 = 13.252, p &lt; 0.001) at FCRL5 rs6692977. When compared with CC genotype, the genotype CT or TT at rs6692977 was significantly associated with a increased risk of liver fibrosis in CHB patients (CT vs. CC: OR = 1.921, 95% CI = 1.093–3.375, p = 0.023; TT vs. CC: OR = 2.598, 95% CI = 1.067–6.324, p = 0.031). The mRNA relative expression levels of FCRL5 in patients with liver fibrosis were significantly higher than those in the non-fibrosis group (t = 13.456, p &lt; 0.001). The fibrosis patients carried TT or CT genotype of rs6692977 had significantly higher FCRL5 mRNA expression levels than those carried CC genotype (t = 2.859, p = 0.005). The mRNA expression levels of FCRL5, APRI, and FIB-4 index showed predictive efficacy in liver fibrosis with cut-off values of 0.75 (AUC = 0.896, 95% CI = 0.856–0.935), 0.45 (AUC = 0.852, 95% CI = 0.802–0.902) and 1.84 (AUC = 0.765, 95% CI = 0.703–0.826), respectively.

Conclusion: FCRL5 gene rs6692977 polymorphisms and mRNA expression levels are associated with liver fibrosis in CHB patients.