Significance of Tumor-Infiltrating Immune Cells in the Prognosis of Colon Cancer

Overexpression of ST8Sia1 inhibits tumor progression by TGF-β1 signaling in rectal adenocarcinoma and promotes the tumoricidal effects of CD8+ T cells by granzyme B and perforin

BACKGROUND

Rectal adenocarcinoma (READ) involves the dysregulated expression of alpha 2,8-Sialyltransferase1 (ST8Sia1) although its role during READ's progression is unclear.

METHODS

The mRNA level of ST8Sia1 was analyzed based on The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Tumor Immune Estimation Resource (TIMER) 2.0. Furthermore, the prognostic and significance of ST8Sia1 in READ was assessed through Kaplan-Meier curve, univariate, multivariate Cox regression, and receiver operating characteristic (ROC) methods. The role of ST8Sia1 in the READ immune microenvironment was explored using ESTIMATE analysis and TIMER databases. Furthermore, the expression of ST8Sia1 in tissues was analyzed using real-time quantitative polymerase chain reaction (RT-qPCR), western blotting (WB), and immunohistochemistry (IHC). Perforin and Granzyme B secretion by CD8+ T cells, as well as tumor cell apoptosis, were detected after co-culturing CD8+ T cells with READ tumor cells and ST8Sia1-overexpression (ST8Sia1-OE) tumor cells. Furthermore, we examined the interaction between ST8Sia1 and TGF-β1 in READ cells.

RESULTS

ST8Sia1 exhibited excellent diagnostic capability for READ, with positive correlations to immune response and negative correlations to tumor purity. Increased levels of perforin and Granzyme B from CD8+ T cells were observed in vitro, enhancing tumor cell apoptosis. ST8Sia1 interacts with TGF-β1, mediating its inhibitory effects on READ development.

CONCLUSIONS

ST8Sia1 is a potential diagnostic biomarker and therapeutic target for READ, enhancing CD8+ T cell function and possibly improving patient outcomes through cellular immunotherapy.

Unveiling the prognostic significance of RNA editing-related genes in colon cancer: evidence from bioinformatics and experiment

BACKGROUND

RNA editing is recognized as a crucial factor in cancer biology. Its potential application in predicting the prognosis of colon adenocarcinoma (COAD) remains unexplored.

METHODS

RNA editing data of COAD patients were downloaded from the Synapse database. LASSO regression was used to construct the risk model and verified by the receiver operating characteristic (ROC) curve. GO and KEGG enrichment analyses were performed to delineate the biological significance of the differentially expressed genes. Finally, differential analysis and immunohistochemistry were used to verify the expression of adenosine deaminase 1 (ADAR1).

RESULTS

We evaluated a total of 4079 RNA editing sites in 514 COAD patients from Synapse database. A prognostic signature was constructed based on five genes were significantly associated with the prognosis of COAD patients including GNL3L, NUP43, MAGT1, EMP2, and ARSD. Univariate and multivariate Cox regression analysis revealed that RNA editing-related genes (RERGs)-related signature was an independent risk factor for COAD. Moreover, Experimental evidence shows that ADAR1 is highly expressed in colon adenocarcinoma and silencing ADAR1 can inhibit cancer cell proliferation.

CONCLUSION

We established a prognostic model based on five RERGs with strong predictive value. This model not only serves as a foundation for a novel prognostic tool but also facilitates the identification of potential drug candidates for treating COAD.

Integrative analysis of single-cell and transcriptome sequencing with experimental validation reveals PKHD1L1 as a novel biomarker in lung adenocarcinoma

Polycystic kidney and hepatic disease 1-like protein 1 (PKHD1L1) is predicted to encode a large type I transmembrane protein involved in hearing transmission and mediating cellular immunity under physiological conditions. However, its role in cancer progression, especially in lung adenocarcinoma (LUAD), has not been fully elucidated. In this study, we observed significantly lower expression of PKHD1L1 in LUAD tissues than in normal lung tissues on the basis of the integration of public datasets from the TCGA and GEO cohorts. Furthermore, we found that low PKHD1L1 expression was a strong predictor of poor prognosis in patients with LUAD. Pathway enrichment analyses revealed that PKHD1L1 is associated primarily with asthma and multiple immune processes. Through meticulous analysis of immune cell infiltrates and single-cell datasets, we discerned a notable correlation between the expression of PKHD1L1 and the presence of B cells, with a particularly strong association observed in plasma cells. This finding led us to believe that the role of PKHD1L1 may extend beyond its previously reported involvement in cellular immunity, potentially impacting humoral immunity as well. In vitro experiments revealed that the over-expression of PKHD1L1 significantly inhibited the proliferation and migration ability of LUAD cell lines. These findings suggest that PKHD1L1 is an important prognostic indicator and a potential therapeutic target for LUAD.

Transforming growth factor-β (TGF-β) signaling pathway-related genes in predicting the prognosis of colon cancer and guiding immunotherapy

Background

Colon cancer is a malignant tumor with high malignancy and a low survival rate whose heterogeneity limits systemic immunotherapy. Transforming growth factor-β (TGF-β) signaling pathway-related genes are associated with multiple tumors, but their role in prognosis prediction and tumor microenvironment (TME) regulation in colon cancer is poorly understood. Using bioinformatics, this study aimed to construct a risk prediction signature for colon cancer, which may provide a means for developing new effective treatment strategies.

Methods

Using consensus clustering, patients in The Cancer Genome Atlas (TCGA) with colon adenocarcinoma were classified into several subtypes based on the expression of TGF-β signaling pathway-related genes, and differences in survival, molecular, and immunological TME characteristics and drug sensitivity were examined in each subtype. Ten genes that make up a TGF-β-related predictive signature were found by least absolute shrinkage and selector operation (LASSO) regression using colon cancer data from the TCGA database and confirmed using a Gene Expression Omnibus (GEO) dataset. A nomogram incorporating risk scores and clinicopathologic factors was developed to stratify the prognosis of patients with colon cancer for accurate clinical diagnosis and therapy.

Results

Two TGF-β subtypes were identified, with the TGF-β-high subtype being associated with a poorer prognosis and superior sensitivity to immunotherapy. Mutation analyses showed a high incidence of gene mutations in the TGF-β-high subtype. After completing signature construction, patients with colon cancer were categorized into high- and low-risk subgroups based on the median risk score of the TGF-β-related predictive signature. The risk score exhibited superior predictive performance relative to age, gender, and stage, as evidenced by its AUC of 0.686. Patients in the high-risk subgroup had higher levels of immunosuppressive cell infiltration and immune checkpoints in the TME, suggesting that these patients had better responses to immunotherapy.

Conclusions

Patients with colon cancer were divided into two subtypes with different survival and immune characteristics using consensus clustering analysis based on TGF-β signaling pathway-related genes. The constructed risk prediction signature may show promise as a biomarker for evaluating the prognosis of colon cancer, with potential utility for screening individuals for immunotherapy.

CSER: a gene regulatory network construction method based on causal strength and ensemble regression

Introduction

Gene regulatory networks (GRNs) reveal the intricate interactions between and among genes, and understanding these interactions is essential for revealing the molecular mechanisms of cancer. However, existing algorithms for constructing GRNs may confuse regulatory relationships and complicate the determination of network directionality.

Methods

We propose a new method to construct GRNs based on causal strength and ensemble regression (CSER) to overcome these issues. CSER uses conditional mutual inclusive information to quantify the causal associations between genes, eliminating indirect regulation and marginal genes. It considers linear and nonlinear features and uses ensemble regression to infer the direction and interaction (activation or regression) from regulatory to target genes.

Results

Compared to traditional algorithms, CSER can construct directed networks and infer the type of regulation, thus demonstrating higher accuracy on simulated datasets. Here, using real gene expression data, we applied CSER to construct a colorectal cancer GRN and successfully identified several key regulatory genes closely related to colorectal cancer (CRC), including ADAMDEC1, CLDN8, and GNA11.

Discussion

Importantly, by integrating immune cell and microbial data, we revealed the complex interactions between the CRC gene regulatory network and the tumor microenvironment, providing additional new biomarkers and therapeutic targets for the early diagnosis and prognosis of CRC.

CILP2: A prognostic biomarker associated with immune infiltration in colorectal cancer

The function played by cartilage intermediate layer protein 2 (CILP2) between colorectal cancer (CRC) progression and immune response remains unclear, especially with respect to immune cell infiltration and checkpoints. Materials and Methods: We examined CILP2 expression in The Cancer Genome Atlas (TCGA) COAD-READ cohort and analyzed its relationship with clinicopathological features, mutations, survival, and immunity. Gene ontology, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and gene set enrichment analyses (GSEA) were performed to determine CILP2 related pathways. To further investigate the results of TCGA analysis, validation was performed using CRC cell lines, fresh pathological tissues, and a CRC tissue microarray (TMA). Results: In both TCGA and TMA cohorts, CILP2 expression was increased in CRC tissues and was associated with patient T stage (T3 and T4), N stage (N1), pathological stage (III and IV), and overall survival. Immune cell infiltration and checkpoint analysis revealed that CILP2 expression is highly correlated with multiple immune marker genes, including PD-1. In addition, results of enrichment analysis indicated that CILP2 related genes was mainly enriched in extracellular matrix related functions. Conclusion: Elevated CILP2 expression is associated with adverse CRC clinical features and immune cells, it has potential as a biomarker detrimental to CRC survival.

A novel immune checkpoint score system for prognostic evaluation in pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PAAD) remains a lethal malignancy making the detection of novel prognostic biomarkers urgent. Limited studies have investigated the predictive capability of immune checkpoints in PAAD.

METHOD

Gene expression data and correlative clinical information of PAAD cohort were obtained from public databases, including TCGA, ICGC, GTEX and GEO databases. Risk factors were screened and used to establish a risk score model through LASSO and Cox regression analyses. The prognostic ability of the risk score model was demonstrated. The association between risk score with immune cells infiltration, immune checkpoint genes expression, immunogenic cell death, somatic mutations and signaling pathways enrichment were analysed. scRNA-seq data were collected to confirmed the immune checkpoints expression in PAAD samples. The prognosis prediction ability of OX40/TNFRSF4 was identified. The mRNA and protein expression of OX40 in our clinical specimens were examined by RT-PCR and IHC method and its prognosis ability was verified.

RESULTS

First of all, the difference of immune microenvironment between pancreatic cancer and adjacent tissues was shown. A risk score system based on three immune checkpoints (OX40, TNFSF14 and KIR3DL1) was established. The risk score model was an independent prognostic factor and performed well regarding overall survival (OS) predictions among PAAD patients. A nomogram was established to facilitate the risk model application in clinical prognosis. Immune cells including naive B cells, CD8+ T cells and Tregs were negatively correlated with the risk score. The risk score was associated with expression of immune checkpoint genes, immunogenic cell death related genes and somatic mutations. Glycolysis processes, IL-2-STAT5, IL-6-STAT3, and mTORC1 signaling pathways were enriched in the high-risk score group. Furthermore, scRNA-seq data confirmed that TNFRSF4, TNFSF14 and KIR3DL1 were expressed on immune cells in PAAD samples. We then identified OX40 as an independent prognosis-related gene, and a higher OX40 expression was associated with increased survival rate and immune environment change. In 84 PAAD clinical specimens collected from our center, we confirmed that higher OX40 mRNA expression levels were related to a good prognosis. The protein expression of OX40 on tumor-infiltrating immune cells (TIICs), endothelial cells and tumor cells was verified in PAAD tissues by immunohistochemistry (IHC) method.

CONCLUSIONS

Overall, our findings strongly suggested that the three-immune checkpoints score system might be useful in the prognosis and design of personalized treatments for PAAD patients. Finally, we identified OX40 as an independent potential biomarker for PAAD prognosis prediction.

Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients

BACKGROUND

Previous investigations of transcriptomic signatures of cancer patient survival and post-therapy relapse have focused on tumor tissue. In contrast, here we show that in colorectal cancer (CRC) transcriptomes derived from normal tissues adjacent to tumors (NATs) are better predictors of relapse.

RESULTS

Using the transcriptomes of paired tumor and NAT specimens from 80 Korean CRC patients retrospectively determined to be in recurrence or nonrecurrence states, we found that, when comparing recurrent with nonrecurrent samples, NATs exhibit a greater number of differentially expressed genes (DEGs) than tumors. Training two prognostic elastic net-based machine learning models-NAT-based and tumor-based in our Samsung Medical Center (SMC) cohort, we found that NAT-based model performed better in predicting the survival when the model was applied to the tumor-derived transcriptomes of an independent cohort of 450 COAD patients in TCGA. Furthermore, compositions of tumor-infiltrating immune cells in NATs were found to have better prognostic capability than in tumors. We also confirmed through Cox regression analysis that in both SMC-CRC as well as in TCGA-COAD cohorts, a greater proportion of genes exhibited significant hazard ratio when NAT-derived transcriptome was used compared to when tumor-derived transcriptome was used.

CONCLUSIONS

Taken together, our results strongly suggest that NAT-derived transcriptomes and immune cell composition of CRC are better predictors of patient survival and tumor recurrence than the primary tumor.

Exploration of an Prognostic Signature Related to Endoplasmic Reticulum Stress in Colorectal Adenocarcinoma and Their Response Targeting Immunotherapy

Background: Endoplasmic reticulum (ER) stress plays a pro-apoptotic role in colorectal adenocarcinoma (COAD). This study aimed to develop a novel ER-stress-related prognostic risk model for COAD and provide support for COAD cohorts with different risk score responses to immune checkpoint inhibitor therapies. Methods: TCGA-COAD and GSE39582 were included in this prospective study. Univariate and multivariate Cox analyses were performed to identify prognostic ER stress-related genes (ERSGs). Accordingly, the immune infiltration landscape and immunotherapy response in different risk groups were assessed. Finally, the expression of prognostic genes in 10 normal and 10 COAD tissue samples was verified using reverse transcription-quantitative polymerase chain reaction. Results: Eight prognostic genes were selected to establish an ERSG-based signature in the training set of the TCGA-COAD cohort. The accuracy of this was confirmed using a testing set of TCGA-COAD and GSE39582 cohorts. Gene set variation analysis indicated that differential functionality in high-low-risk groups was related to immune-related pathways. Corresponding to this, CD36, TIMP1, and PTGIS were significantly associated with 19 immune cells with distinct proportions between the different risk groups, such as central memory CD4T cells and central memory CD8T cells. Moreover, the risk score was considered effective for predicting the clinical response to immunotherapy, and the immunotherapy response was significantly and negatively correlated with the risk score of individuals with COAD. Furthermore, the immune checkpoint inhibitor treatment was less effective in the high-risk group, where the expression levels of PD-L1 and tumor immune dysfunction and exclusion scores in the high-risk group were significantly increased. Finally, the experimental results demonstrated that the expression trends of prognostic genes in clinical samples were consistent with the results from public databases. Conclusion: Our study established a novel risk signature to predict the COAD prognosis of patients and provide theoretical support for the clinical treatment of COAD.

Relationship between Tumor Infiltrating Immune Cells and Tumor Metastasis and Its Prognostic Value in Cancer

Tumor metastasis is an important reason for the difficulty of tumor treatment. Besides the tumor cells themselves, the tumor microenvironment plays an important role in the process of tumor metastasis. Tumor infiltrating immune cells (TIICs) are one of the main components of TME and plays an important role in every link of tumor metastasis. This article mainly reviews the role of tumor-infiltrating immune cells in epithelial mesenchymal transformation, extracellular matrix remodeling, tumor angiogenesis and formation of pre-metastatic niche. The value of TIICs in the prognosis of cervical cancer, lung cancer and breast cancer was also discussed. We believe that accurate prognosis of cancer treatment outcomes is conducive to further improving treatment regimens, determining personalized treatment strategies, and ultimately achieving successful cancer treatment. This paper elucidates the relationship between tumor and TIICs in order to explore the function of immune cells in different diseases and provide new ideas for the treatment of cancer.

A Prognostic Ferroptosis-Related lncRNA Model Associated With Immune Infiltration in Colon Cancer

Colon cancer (CC) is a common malignant tumor worldwide, and ferroptosis plays a vital role in the pathology and progression of CC. Effective prognostic tools are required to guide clinical decision-making in CC. In our study, gene expression and clinical data of CC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We identified the differentially expressed ferroptosis-related lncRNAs using the differential expression and gene co-expression analysis. Then, univariate and multivariate Cox regression analyses were used to identify the effective ferroptosis-related lncRNAs for constructing the prognostic model for CC. Gene set enrichment analysis (GSEA) was conducted to explore the functional enrichment analysis. CIBERSORT and single-sample GSEA were performed to investigate the association between our model and the immune microenvironment. Finally, three ferroptosis-related lncRNAs (XXbac-B476C20.9, TP73-AS1, and SNHG15) were identified to construct the prognostic model. The results of the validation showed that our model was effective in predicting the prognosis of CC patients, which also was an independent prognostic factor for CC. The GSEA analysis showed that several ferroptosis-related pathways were significantly enriched in the low-risk group. Immune infiltration analysis suggested that the level of immune cell infiltration was significantly higher in the high-risk group than that in the low-risk group. In summary, we established a prognostic model based on the ferroptosis-related lncRNAs, which could provide clinical guidance for future laboratory and clinical research on CC.

MTUS1 is a promising diagnostic and prognostic biomarker for colorectal cancer

BACKGROUND

The morbidity and mortality of colorectal cancer (CRC) remain high, posing a serious threat to human life and health. The early diagnosis and prognostic evaluation of CRC are two major challenges in clinical practice. MTUS1 is considered a tumour suppressor and can play an important role in inhibiting cell proliferation, migration, and tumour growth. Moreover, the expression of MTUS1 is decreased in different human cancers, including CRC. However, the biological functions and molecular mechanisms of MTUS1 in CRC remain unclear.

METHODS

In the present study, data from The Cancer Genome Atlas (TCGA) database were analysed using R statistical software (version 3.6.3.) to evaluate the expression of MTUS1 in tumour tissues and adjacent normal tissues using public databases such as the TIMER and Oncomine databases. Then, 38 clinical samples were collected, and qPCR was performed to verify MTUS1 expression. We also investigated the relationship between MTUS1 expression and clinicopathological characteristics and elucidated the diagnostic and prognostic value of MTUS1 in CRC. In addition, the correlation between MTUS1 expression and immune infiltration levels was identified using the TIMER and GEPIA databases. Furthermore, we constructed and analysed a PPI network and coexpression modules of MTUS1 to explore its molecular functions and mechanisms.

RESULTS

CRC tissues exhibited lower levels of MTUS1 than normal tissues. The logistic regression analysis indicated that the expression of MTUS1 was associated with N stage, TNM stage, and neoplasm type. Moreover, CRC patients with low MTUS1 expression had poor overall survival (OS). Multivariate analysis revealed that the downregulation of MTUS1 was an independent prognostic factor and was correlated with poor OS in CRC patients. MTUS1 expression had good diagnostic value based on ROC analysis. Furthermore, we identified a group of potential MTUS1-interacting proteins and coexpressed genes. GO and KEGG enrichment analyses showed that MTUS1 was involved in multiple cancer-related signalling pathways. Moreover, the expression of MTUS1 was significantly related to the infiltration levels of multiple cells. Finally, MTUS1 expression was strongly correlated with various immune marker sets.

CONCLUSIONS

Our results indicated that MTUS1 is a promising biomarker for predicting the diagnosis and prognosis of CRC patients. MTUS1 can also become a new molecular target for tumour immunotherapy.

Tumor microenvironment involvement in colorectal cancer progression via Wnt/β-catenin pathway: Providing understanding of the complex mechanisms of chemoresistance

Colorectal cancer (CRC) continues to be one of the main causes of death from cancer because patients progress unfavorably due to resistance to current therapies. Dysregulation of the Wnt/β-catenin pathway plays a fundamental role in the genesis and progression of several types of cancer, including CRC. In many subtypes of CRC, hyperactivation of the β-catenin pathway is associated with mutations of the adenomatous polyposis coli gene. However, it can also be associated with other causes. In recent years, studies of the tumor microenvironment (TME) have demonstrated its importance in the development and progression of CRC. In this tumor nest, several cell types, structures, and biomolecules interact with neoplastic cells to pave the way for the spread of the disease. Cross-communications between tumor cells and the TME are then established primarily through paracrine factors, which trigger the activation of numerous signaling pathways. Crucial advances in the field of oncology have been made in the last decade. This Minireview aims to actualize what is known about the central role of the Wnt/β-catenin pathway in CRC chemoresistance and aggressiveness, focusing on cross-communication between CRC cells and the TME. Through this analysis, our main objective was to increase the understanding of this complex disease considering a more global context. Since many treatments for advanced CRC fail due to mechanisms involving chemoresistance, the data here exposed and analyzed are of great interest for the development of novel and effective therapies.

Nuclear receptor subfamily 3 group c member 2 (NR3C2) is downregulated due to hypermethylation and plays a tumor-suppressive role in colon cancer

Nuclear receptor subfamily 3 group c member 2 (NR3C2) has been reported to function as a tumor suppressor in several tumors. However, the clinical significance and potential action mechanisms of NR3C2 in colon cancer (COAD) remain unclear. NR3C2 expression and its correlation with clinicopathological features in COAD were analyzed based on the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Receiver operating characteristic (ROC) curves and Human Protein Atlas (HPA) database were used to evaluate the diagnostic and prognostic values of NR3C2 in COAD. Immune infiltration and DNA methylation analyses were performed by Gene Set Cancer Analysis (GSCA) database. NR3C2-correlated genes were identified by UALCAN database and subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses. Cell apoptosis and proliferation were evaluated using TUNEL and CCK-8 assays, respectively. NR3C2 was downregulated in COAD based on TCGA and GEO databases, which may be due to promoter hypermethylation. NR3C2 expression was correlated with prognosis and immune infiltration of COAD. High NR3C2 expression displayed good diagnostic value in COAD. KEGG pathway analysis presented that NR3C2-correlated genes were mainly clustered in choline metabolism in cancer and apoptosis. In vitro experiments confirmed that NR3C2 overexpression induced apoptosis and suppressed proliferation in COAD cells. In conclusion, our study revealed the potential prognostic and diagnostic values of NR3C2 and provided insights into understanding the tumor-suppressive role of NR3C2 in COAD progression.

INSC Is Down-Regulated in Colon Cancer and Correlated to Immune Infiltration

Background: Previous studies have verified that Inscuteable Spindle Orientation Adaptor Protein (INSC) can regulate cell proliferation and differentiation in the developing nervous system. It also plays an important role in spindle orientation during mitosis and asymmetric division of fibroblasts and participates in the process of stratification of the squamous epithelium. The role and potential mechanism of INSC in the development of colonic adenocarcinoma (COAD) have not been fully understood. This study aimed at exploring the prognostic value of INSC in COAD and the correlation of its expression with immune infiltration.

Methods: The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) project, Gene Expression Profiling Interactive Analysis (GEPIA), and Gene Expression Omnibus (GEO) database were used to analyze the expression of INSC in COAD. The INSC protein expression level was analyzed by immunohistochemistry staining and the Human Protein Atlas (HPA) database. The diagnostic and prognostic values of INSC in COAD patients were analyzed using receiver operating characteristic (ROC) and Kaplan–Meier (KM) survival curves. In order to understand whether INSC is an independent prognostic factor, we used univariable and multivariate Cox analyses to analyze INSC expression and several clinical characteristics with survival. We use STRING analysis to find INSC-related proteins and related biological events analyzed by Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. At last, GEPIA and the Tumor Immune Estimation Resource (TIMER) were employed to explore the relationship between INSC and immune infiltrates and its marker gene set.

Results: INSC was lower expressed in COAD tissues than in normal colon tissues, which was correlated with tumor stage. Patients with lower expression of INSC had shorter overall survival (OS). Moreover, univariable Cox analysis demonstrated that high expression of INSC was an independent prognostic factor for COAD. ROC analysis showed INSC was an accurate marker for identifying tumors from normal colon tissue, and the AUC of the curve was 0.923. Significant GO term analysis by GSEA showed that genes correlated with INSC were found to be enriched in several immune-related pathways. Specifically, INSC expression showed significant negative correlations with infiltration levels of B cells, CD4+ T cells, macrophages, DCs, and their marker sets in COAD.

Conclusion: INSC was provided with prognostic value in COAD and related to immune invasion.

Machine-Learning-Based m5C Score for the Prognosis Diagnosis of Osteosarcoma

Background

Osteosarcoma is a common and highly metastatic malignant tumor, and m5C RNA methylation regulates various biological processes. The purpose of this study was to explore the prognostic role of m5C in osteosarcoma using machine learning.

Methods

Osteosarcoma gene data and the corresponding clinical information were downloaded from the GEO database. Machine learning methods were used to screen m5C-related genes and construct m5C scores. In addition, the clusterProfiler package was used to predict the m5C-related functional pathways. xCell and CIBERSORT were used to calculate the immune microenvironment cells. GSVA was applied to analyze different categories of m5C genes, and the correlation between the GSVA and m5C scores was evaluated.

Results

Twenty m5C genes were identified, and 54 related genes were screened. The m5C score was constructed based on the PCA score. With an increase in the m5C score, the expression of m5C genes and their related genes changed. Functional analysis indicated that the focal adhesion, cell-substrate adherens junction, cell adhesion molecule binding, and E2F targets might change with the m5C score. The naive B cells and CD4⁺ memory T cell also changed with the m5C score. The results of the correlation analysis showed that the m5C score was significantly correlated with the reader and eraser genes.

Conclusion

The m5C score might be a prognostic index for osteosarcoma.

A Novel Local Tumor Progression Prediction Method for Multimode Ablation Treatment

OBJECTIVE

The multimode ablation of liver cancer, which uses radio-frequency heating after a pre-freezing process to treat the tumor, has shown significantly improved therapeutic effects and enhanced anti-tumor immune response. Unlike open surgery, the ablated lesions remain in the body after treatment, so it is critical to assess the immediate outcome and to monitor disease status over time. Here we propose a novel tumor progression prediction method for simultaneous postoperative evaluation and prognosis analysis.

METHODS

We propose to leverage the intraoperative therapeutic information extracted from thermal dose distribution. For tumors with specific sensitivity reflected in medical images, different thermal doses implicitly indicate the degree of instant damage and long-term inhibition excited under specific ablation energy. We further propose a survival analysis framework for the multimode ablation treatment. It extracts carefully designed features from clinical, preoperative, intraoperative, and postoperative data, then uses random survival forest for feature selection and deep neural networks for survival prediction.

RESULTS

We evaluated the proposed methods using clinical data. The results show that our method outperforms the state-of-the-art survival analysis methods with a C-index of 0.8550.090. The thermal dose information contributes significantly to the prediction accuracy by taking up 21.7% of the overall feature importance.

CONCLUSION

The proposed methods have been demonstrated to be a powerful tool in tumor progression prediction of multimode ablation therapy.

SIGNIFICANCE

This kind of data-driven prognosis analysis may benefit personalized medicine and simplify the follow-up process.

ACE2 correlates with immune infiltrates in colon adenocarcinoma: Implication for COVID-19

Novel coronavirus disease (COVID-19) pandemic has become a global health emergency. It has been reported that a few conditions, including cancer, predispose individuals to SARS-CoV-2 infection and severe form of COVID-19. These findings led us to evaluate the susceptibility of colon adenocarcinoma (COAD) patients to SARS-CoV-2 infection by investigating ACE2 expression in their tumor tissues. The expression analysis revealed that both mRNA and protein levels of ACE2 had increased in colon cancer samples than normal group. Next, the prognosis analysis has indicated that the upregulation of ACE2 was not correlated with patient survival outcomes. Further assessment displayed the hypomethylation of the ACE2 gene promoter in COAD patients. This methylation status has a strong negative correlation with ACE2 gene expression. The functional enrichment analysis of the genes that had similar expression patterns with ACE2 in colon cancer tissues demonstrated that they mainly enriched in Vitamin digestion and absorption pathway. Finally, we found that ACE2 gene expression had a significant association with the immune cell infiltration levels in COAD patients. In conclusion, it has plausible that COAD patients are more likely to be infected with SARS-CoV-2 and experience severe injuries. Moreover, COVID-19 would bring unfavorable survival outcomes for patients with colon cancer by way of immune cell infiltration linked process. The present study highlights the importance of preventive actions for COAD patients during the COVID-19 pandemic.