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

Applying image features of proximal paracancerous tissues in predicting prognosis of patients with hepatocellular carcinoma

BACKGROUND

Most of the methods using digital pathological image for predicting Hepatocellular carcinoma (HCC) prognosis have not considered paracancerous tissue microenvironment (PTME), which are potentially important for tumour initiation and metastasis. This study aimed to identify roles of image features of PTME in predicting prognosis and tumour recurrence of HCC patients.

METHODS

We collected whole slide images (WSIs) of 146 HCC patients from Sun Yat-sen Memorial Hospital (SYSM dataset). For each WSI, five types of regions of interests (ROIs) in PTME and tumours were manually annotated. These ROIs were used to construct a Lasso Cox survival model for predicting the prognosis of HCC patients. To make the model broadly useful, we established a deep learning method to automatically segment WSIs, and further used it to construct a prognosis prediction model. This model was tested by the samples of 225 HCC patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC).

RESULTS

In predicting prognosis of the HCC patients, using the image features of manually annotated ROIs in PTME achieved C-index 0.668 in the SYSM testing dataset, which is higher than the C-index 0.648 reached by the model only using image features of tumours. Integrating ROIs of PTME and tumours achieved C-index 0.693 in the SYSM testing dataset. The model using automatically segmented ROIs of PTME and tumours achieved C-index of 0.665 (95% CI: 0.556-0.774) in the TCGA-LIHC samples, which is better than the widely used methods, WSISA (0.567), DeepGraphSurv (0.593), and SeTranSurv (0.642). Finally, we found the Texture SumAverage Skew HV on immune cell infiltration and Texture related features on desmoplastic reaction are the most important features of PTME in predicting HCC prognosis. We additionally used the model in prediction HCC recurrence for patients from SYSM-training, SYSM-testing, and TCGA-LIHC datasets, indicating the important roles of PTME in the prediction.

CONCLUSIONS

Our results indicate image features of PTME is critical for improving the prognosis prediction of HCC. Moreover, the image features related with immune cell infiltration and desmoplastic reaction of PTME are the most important factors associated with prognosis of HCC.

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data

BACKGROUND

Pyroptosis impacts the development of malignant tumors, yet its role in colorectal cancer (CRC) prognosis remains uncertain.

AIM

To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.

METHODS

Gene expression data were obtained from The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus (GEO). Pyroptosis-related gene expression in cell clusters was analyzed, and enrichment analysis was conducted. A pyroptosis-related risk model was developed using the LASSO regression algorithm, with prediction accuracy assessed through K-M and receiver operating characteristic analyses. A nomogram predicting survival was created, and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations. Finally, the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.

RESULTS

An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B, SDHB, BST2, UBE2D2, GJA1, AIM2, PDCD6IP, and SEZ6L2 (P < 0.05). Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis (P < 0.05). Patients with higher risk scores demonstrated increased death risk and reduced overall survival (P < 0.05). Significant differences in immune infiltration were observed between low- and high-risk groups, correlating with pyroptosis-related gene expression.

CONCLUSION

We developed a pyroptosis-related prognostic model for CRC, affirming its correlation with immune infiltration. This model may prove useful for CRC prognostic evaluation.

Biological factors driving colorectal cancer metastasis

Approximately 20% of colorectal cancer (CRC) patients present with metastasis at diagnosis. Among Stage I-III CRC patients who undergo surgical resection, 18% typically suffer from distal metastasis within the first three years following initial treatment. The median survival duration after the diagnosis of metastatic CRC (mCRC) is only 9 mo. mCRC is traditionally considered to be an advanced stage malignancy or is thought to be caused by incomplete resection of tumor tissue, allowing cancer cells to spread from primary to distant organs; however, increasing evidence suggests that the mCRC process can begin early in tumor development. CRC patients present with high heterogeneity and diverse cancer phenotypes that are classified on the basis of molecular and morphological alterations. Different genomic and nongenomic events can induce subclone diversity, which leads to cancer and metastasis. Throughout the course of mCRC, metastatic cascades are associated with invasive cancer cell migration through the circulatory system, extravasation, distal seeding, dormancy, and reactivation, with each step requiring specific molecular functions. However, cancer cells presenting neoantigens can be recognized and eliminated by the immune system. In this review, we explain the biological factors that drive CRC metastasis, namely, genomic instability, epigenetic instability, the metastatic cascade, the cancer-immunity cycle, and external lifestyle factors. Despite remarkable progress in CRC research, the role of molecular classification in therapeutic intervention remains unclear. This review shows the driving factors of mCRC which may help in identifying potential candidate biomarkers that can improve the diagnosis and early detection of mCRC cases.

FHIP1A-DT is a potential novel diagnostic, prognostic, and therapeutic biomarker of colorectal cancer: A pan-cancer analysis

BACKGROUND

FHIP1A-DT is a long non-coding RNA (lncRNA) obtained by divergent transcription whose mechanism in pan-cancer and colorectal cancer (CRC) is unclear. We elucidated the molecular mechanism of FHIP1A-DT through bioinformatics analysis and in vitro experiments.

METHODS

Pan-cancer and CRC data were downloaded from the University of California, Santa Cruz (UCSC) Genome Browser and the Cancer Genome Atlas (TCGA). We analyzed FHIP1A-DT expression and its relationship with clinical stage, diagnosis, prognosis, and immunity characteristics in pan-cancer. We also analyzed FHIP1A-DT expression in CRC and explored the relationship between FHIP1A-DT and CRC diagnosis and prognosis. Then, we analyzed the correlation between FHIP1A-DT and drug sensitivity, immune cell infiltration, and the biological processes involved in FHIP1A-DT. The competing endogenous RNA (ceRNA) regulatory network associated with FHIP1A-DT was explored. External validation was conducted using external data sets GSE17538 and GSE39582 and in vitro experiments.

RESULTS

FHIP1A-DT expression was different in pan-cancer and had excellent diagnostic and prognostic capability for pan-cancer. FHIP1A-DT was also related to the pan-cancer tumor mutation burden (TMB), microsatellite instability (MSI), and immune cell content. FHIP1A-DT was downregulated in CRC, where patients with CRC with low FHIP1A-DT expression had a worse prognosis. A nomogram combined with FHIP1A-DT expression demonstrated excellent predictive ability for prognosis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that FHIP1A-DT was associated with epigenetic modification and regulated many cancer-related pathways. The ceRNA network demonstrated the potential gene regulation of FHIP1A-DT. FHIP1A-DT was related to many chemotherapeutic drug sensitivities and immune cell infiltration such as CD4 memory resting T cells, monocytes, plasma cells, neutrophils, and M2 macrophages. The FHIP1A-DT expression and prognostic analysis of GSE17538 and GSE39582, and qPCR yielded similar external verification results.

CONCLUSION

FHIP1A-DT was a novel CRC-related lncRNA related to CRC diagnosis, prognosis, and treatment sensitivity. It could be used as a significant CRC biomarker in the future.

Molecular subtypes based on metabolic genes are potential biomarkers for predicting prognosis and immune responses of clear cell renal cell carcinoma

Due to the existence of tumor molecular heterogeneity, even patients having similar clinicopathological features could have vastly different survival rates. Hence, we aimed to explore novel metabolism-associated genes (MAGs) related molecular subtypes for clear cell renal cell carcinoma (ccRCC) and their immune landscapes for predicting prognosis and immune responses. Gene matrices and clinical information were downloaded from TCGA and ICGC datasets. Consensus clustering was conducted by the R "ConsensusClusterPlus" package. ccRCC patients were successfully divided into three clusters (MC1, MC2, and MC3) based on MAGs in both TCGA and ICGC datasets. Our established three MAGs were significantly associated with chemokine/chemokine receptor, IFN, CYT, angiogenesis, immune checkpoint molecules, tumor-infiltrating immune cells, oncogenic pathways, pan-cancer immune subtypes, and tumor microenvironment (TME) scores or expressions. Moreover, these three metabolic ccRCC subtypes could predict immunotherapeutic responses. We further constructed a characteristic index (LDAscore) in three metabolic ccRCC subtypes and identified LDAscore-related modules by WGCNA. After deep data mining, 10 hub genes were obtained and seven genes (ATRX, BPTF, DHX9, EP300, POLR2B, SIN3A, UBE3A) were finally validated by qRT-PCR. Our results successfully established a novel ccRCC subtype based on MAGs, providing novel insights into metabolism-related ccRCC tumor heterogeneity and facilitating individualized therapy for future work.

Comprehensive Analysis of Necroptosis-Related Genes as Prognostic Factors and Immunological Biomarkers in Breast Cancer

Breast cancer (BC) is a lethal malignancy with a poor prognosis. Necroptosis is critical in the progression of cancer. However, the expression of genes involved in necroptosis in BC and their association with prognosis remain unclear. We investigated the predictive potential of necroptosis-related genes in BC samples from the TCGA dataset. We used LASSO regression to build a risk model consisting of twelve necroptosis-related genes in BC. Using the necroptosis-related risk model, we were able to successfully classify BC patients into high- and low-risk groups with significant prognostic differences (p = 4.872 × 10 ^-7). Additionally, we developed a matched nomogram predicting 5, 7, and 10-year overall survival in BC patients based on this necroptosis-related risk model. Our next step was to perform multiple GSEA analyses to explore the biological pathways through which these necroptosis-related risk genes influence cancer progression. For these twelve risk model genes, we analyzed CNV, SNV, OS, methylation, immune cell infiltration, and drug sensitivity in pan-cancer. In addition, immunohistochemical data from the THPA database were used to validate the protein expression of these risk model genes in BC. Taken together, we believe that necroptosis-related genes are considered potential therapeutic targets in BC and should be further investigated.

An Immune-Related Prognostic Risk Model in Colon Cancer by Bioinformatics Analysis

Colon cancer is one of the leading malignancies with poor prognosis worldwide. Immune cell infiltration has a potential prognostic value for colon cancer. This study aimed to establish an immune-related prognostic risk model for colon cancer by bioinformatics analysis. A total of 1670 differentially expressed genes (DEGs), including 177 immune-related genes, were identified from The Cancer Genome Atlas (TCGA) dataset. A prognostic risk model was constructed based on six critical immune-related genes (C-X-C motif chemokine ligand 1 (CXCL1), epiregulin (EREG), C-C motif chemokine ligand 24 (CCL24), fatty acid binding protein 4 (FABP4), tropomyosin 2 (TPM2), and semaphorin 3G (SEMA3G)). This model was validated using the microarray dataset GSE35982. In addition, Cox regression analysis showed that age and clinical stage were correlated with prognostic risk scores. Kaplan–Meier survival analysis showed that high risk scores correlated with low survival probabilities in patients with colon cancer. Downregulated TPM2, FABP4, and SEMA3G levels were positively associated with the activated mast cells, monocytes, and macrophages M2. Upregulated CXCL1 and EREG were positively correlated with macrophages M1 and activated T cells CD4 memory, respectively. Based on these results, we can conclude that the proposed prognostic risk model presents promising novel signatures for the diagnosis and prognosis prediction of colon cancer. This model may provide therapeutic benefits for the development of immunotherapy for colon cancer.

Bioinformatic analysis of RNA-seq data from TCGA database reveals prognostic significance of immune-related genes in colon cancer

The tumor immune microenvironment is of crucial importance in cancer progression and anticancer immune responses. Thus, systematic exploration of the expression landscape and prognostic significance of immune-related genes (IRGs) to assist in the prognosis of colon cancer is valuable and significant. The transcriptomic data of 470 colon cancer patients were obtained from The Cancer Genome Atlas database and the differentially expressed genes were analyzed. After an intersection analysis, the hub IRGs were identified and a prognostic index was further developed using multivariable Cox analysis. In addition, the discriminatory ability and prognostic significance of the constructed model were validated and the characteristics of IRGs associated overall survival were analyzed to elucidate the underlying molecular mechanisms. A total of 465 differentially expressed IRGs and 130 survival-associated IRGs were screened. Then, 46 hub IRGs were identified by an intersection analysis. A regulatory network displayed that most of these genes were unfavorable for the prognosis of colon cancer and were regulated by transcription factors. After a least absolute shrinkage and selection operator regression analysis, 14 hub IRGs were ultimately chose to construct a prognostic index. The validation results illustrated that this model could act as an independent indicator to moderately separate colon cancer patients into low- and high-risk groups. This study ascertained the prognostic significance of IRGs in colon cancer and successfully constructed an IRG-based prognostic signature for clinical prediction. Our results provide promising insight for the exploration of diagnostic markers and immunotherapeutic targets in colon cancer.

Comprehensive Analysis of a Cancer-Immunity Cycle-Based Signature for Predicting Prognosis and Immunotherapy Response in Patients With Colorectal Cancer

Immune checkpoint blockade (ICB) has been recognized as a promising immunotherapy for colorectal cancer (CRC); however, most patients have little or no clinical benefit. This study aimed to develop a novel cancer-immunity cycle–based signature to stratify prognosis of patients with CRC and predict efficacy of immunotherapy. CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the RNA data from Gene Expression Omnibus (GEO) data sets and real-time quantitative PCR (RT-qPCR) data from paired frozen tissues were used for validation. We built a least absolute shrinkage and selection operator (LASSO)-Cox regression model of the cancer-immunity cycle–related gene signature in CRC. Patients who scored low on the risk scale had a better prognosis than those who scored high. Notably, the signature was an independent prognostic factor in multivariate analyses, and to improve prognostic classification and forecast accuracy for individual patients, a scoring nomogram was created. The comprehensive results revealed that the low-risk patients exhibited a higher degree of immune infiltration, a higher immunoreactivity phenotype, stronger expression of immune checkpoint–associated genes, and a superior response to ICB therapy. Furthermore, the risk model was closely related to the response to multiple chemotherapeutic drugs. Overall, we developed a reliable cancer-immunity cycle–based risk model to predict the prognosis, the molecular and immune status, and the immune benefit from ICB therapy, which may contribute greatly to accurate stratification and precise immunotherapy for patients with CRC.

Identification of a Novel Immune Landscape Signature for Predicting Prognosis and Response of Colon Cancer to Immunotherapy

Purpose

To construct an immune-related gene prognostic index (IRGPI) for colon cancer and elucidate the molecular and immune characteristics as well as the benefit of immune checkpoint inhibitor (ICI) therapy in IRGPI-defined groups of colon cancer.

Experimental Design

Transcriptional and clinical data of colon cancer samples were obtained from The Cancer Genome Atlas (TCGA) (n = 521). Immune-related genes were obtained from ImmPort and InnateDB databases. 21 immune-related hub genes were identified byweighted gene co-expression network analysis (WGCNA). the Cox regression method was used to construct IRGPI and validated with Gene Expression Omnibus (GEO) dataset (n = 584). Finally, the molecular and immune profiles in the groups defined by IRGPI and the benefit of ICI treatment were analyzed.

Results

8 genes were identified to construct IRGPI. IRGPI-low group had a better overall survival (OS) than IRGPI-high group. And this was well validated in the GEO cohort. Overall results showed that those with low IRGPI scores were enriched in antitumor metabolism, and collated with high infiltration of resting memory CD4 T cells and less aggressive phenotypes, benefiting more from ICI treatment. Conversely, high IRGPI scores were associated with cell adhesion molecules (CAMs) and chemokine signaling pathways, high infiltration of macrophage M1, suppressed immunity, more aggressive colon cancer phenotypes, as well as reduced therapeutic benefit from ICI treatment.

Conclusions

IRGPI is a promising biomarker to differentiate the prognostic and molecular profile of colon cancer, as well as the therapeutic benefits of ICI treatment.

Potential Biological Roles of Exosomal Long Non-Coding RNAs in Gastrointestinal Cancer

Exosomes, a type of extracellular vesicles (EVs), are secreted by almost all cells and contain many cellular constituents, such as nucleic acids, lipids, and metabolites. In addition, they play a crucial role in intercellular communication and have been proved to be involved in the development and treatment of gastrointestinal cancer. It has been confirmed that long non-coding RNAs (lncRNAs) exert a range of biological functions, such as cell metastasis, tumorigenesis, and therapeutic responses. This review mainly focused on the emerging roles and underlying molecular mechanisms of exosome-derived lncRNAs in gastrointestinal cancer in recent years. The biological roles of exosomal lncRNAs in the pathogenesis and therapeutic responses of gastrointestinal cancers were also investigated.

Ferroptosis-Related Gene MT1G as a Novel Biomarker Correlated With Prognosis and Immune Infiltration in Colorectal Cancer

Ferroptosis, a newly discovered way of cell death, has been proved to be involved in the oncogenesis and development of cancers, including colorectal cancer (CRC). Here, by identifying the differentially expressed genes (DEGs) from three CRC transcriptome microarray datasets (GSE20842, GSE23878, and GSE25070), we found that the expression of MT1G was significantly decreased in CRC tissues, and the patients with a high level of MT1G displayed a poor prognosis. Quantitative PCR (qPCR) further confirmed the downregulated MT1G in two CRC cells, HCT8 and HCT116. The colony-forming assay indicated that the MT1G overexpression exhibited a remarkable inhibition of cell proliferation in HCT8 and HCT116 cells. In addition, we explored the co-expressed genes of MT1G to gain a better understanding of its potential signaling pathways. Aberrantly expressed MT1G also affected the immune response of CRC patients. Collectively, these findings might deepen our comprehension on the potential biological implications of MT1G in CRC.

Downregulated exosome-associated gene FGF9 as a novel diagnostic and prognostic target for ovarian cancer and its underlying roles in immune regulation

Exosome has been demonstrated to be secreted from cells and seized by targeted cells. Exosome could transmit signals and exert biological functions in cancer progression. Nevertheless, the underlying mechanisms of exosome in ovarian cancer (OC) have not been fully explored. In this study, we wanted to explore whether Fibroblast growth factor 9 (FGF9), as an exosome-associated gene, was importantly essential in OC progression and prognosis. Firstly, comprehensive bioinformatics platforms were applied to find that FGF9 expression was lower in OC tissues compared to normal ovarian tissues. Meanwhile, downregulated FGF9 displayed favorable prognostic values in OC patients. The gene enrichment of biological functions indicated that abnormally expressed FGF9 could be involved in the OC-related immune signatures, such as immunoinhibitors and chemokine receptors. Taken together, these findings could provide a novel insight into the significance of FGF9 in OC progress and supply a new destination of FGF9-related immunotherapy in clinical treatment.

Molecular Patterns Based on Immunogenomic Signatures Stratify the Prognosis of Colon Cancer

Background: Colon cancer is an aggressive and heterogeneous disease associated with high morbidity and mortality. The immune system is intimately involved in tumorigenesis and can influence malignant properties at the protein, epigenetic, and even genomic levels by shaping the tumor immune microenvironment (TIM). However, immune-related molecules that can effectively predict the prognosis of colon cancer remain under exploration.

Methods: A total of 606 patients from TCGA and GEO databases were employed in our study, in which 429 cases were set as the training cohort and 177 were defined as the validation cohort. The immune infiltration was evaluated by ESTIMATE, TIMER, and CIBERSORT algorithms. The risk signature was constructed by LASSO Cox regression analysis. A nomogram model was generated subsequent to the multivariate Cox proportional hazards analysis to predict 1-, 3-, and 5-year survival of patients with colon cancer.

Results: Infiltrating immune cell profiling identified two colon cancer clusters (Immunity_L group and Immunity_H group). The abundances of immune cells were higher in the Immunity_H group, which indicated a better prognosis. Through further statistical analysis, we identified four genes which were highly correlated with prognosis and representative of this gene set, namely ARL4C, SERPINE1, BST2, and AXIN2. When the patients were divided into low- and high-risk groups based on their risk scores, we found that patients in the high-risk group had shorter overall survival time. Moreover, a nomogram including clinicopathologic features and the established risk signature could robustly predict 1-, 3-, and 5-year survival in patients with colon cancer.

Conclusion: We identified two distinct immune patterns by analyzing clinical and transcriptomic information from colon cancer patients. A subsequently constructed immune-related gene-based prognostic model as well as a nomogram model can be used to predict the prognosis of colon cancer, thereby guiding risk stratification and treatment regimen development for colon patients.

TCGA database analysis of the tumor mutation burden and its clinical significance in colon cancer

Background

Colon cancer is one of the most common malignant tumors, with high rates of incidence and death. The tumor mutational burden (TMB), which is characterized by microsatellite instability, has been becoming a powerful predictor which can show tumor behavior and response to immunotherapy.

Methods

In this study, we analyzed 437 mutation data of colon cancer samples obtained from The Cancer Genome Atlas (TCGA) and divided patients into low- and high-TMB groups according to the TMB value. Then we identified differentially-expressed genes (DEGs), conducted immune cell infiltration and survival analyses between groups.

Results

The higher TMB of the patients with colon cancer predicts a poorer prognosis. Functional analysis was performed to assess the prognostic value of the top 30 core genes. The CIBER-SORT algorithm was used to investigate the correlation between the immune cells and TMB subtypes. An immune prognosis model was constructed to screen out immune genes related to prognosis, and the tumor immunity assessment resource (TIMER) was then used to determine the correlation between gene expression and the abundance of tumor-infiltrating immune cell subsets in colon cancer. We observed that APC, TP53, TTN, KRAS, MUC16, SYNE1, PIK3CA have higher somatic mutations. DEGs enrichment analysis showed that they are involved in the regulation of neuroactive ligand-receptor interaction, the Cyclic adenosine monophosphate (cAMP) signaling pathway, the calcium signaling pathway, and pantothenate and Coenzyme A (CoA) biosynthesis. The difference in the abundance of various white blood cell subtypes showed that Cluster of Differentiation 8 (CD8) T cells (P=0.008), activated CD4 memory T cells (P=0.019), M1 macrophages (P=0.002), follicular helper T cells (P=0.034), activated Natural killer (NK cell) cells (P=0.017) increased remarkably, while M0 macrophages significantly reduced (P=0.025). The two immune model genes showed that secretin (SCT) was negatively correlated with survival, while Guanylate cyclase activator 2A (GUCA2A) was positively correlated.

Conclusions

This study conducted a systematically comprehensive analysis of the prediction and clinical significance of TMB in colon cancer in identification, monitoring, and prognosis of colon cancer, and providing reference information for immunotherapy.