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

Integrated analysis reveals a novel 5-fluorouracil resistance-based prognostic signature with promising implications for predicting the efficacy of chemotherapy and immunotherapy in patients with colorectal cancer

BACKGROUND

5-Fluorouracil (5-FU) has been used as a standard first-line treatment for colorectal cancer (CRC) patients. Although 5-FU-based chemotherapy and immune checkpoint blockade (ICB) have achieved success in treating CRC, drug resistance and low response rates remain substantial limitations. Thus, it is necessary to construct a 5-FU resistance-related signature (5-FRSig) to predict patient prognosis and identify ideal patients for chemotherapy and immunotherapy.

METHODS

Using bulk and single-cell RNA sequencing data, we established and validated a novel 5-FRSig model using stepwise regression and multiple CRC cohorts and evaluated its associations with the prognosis, clinical features, immune status, immunotherapy, neoadjuvant therapy, and drug sensitivity of CRC patients through various bioinformatics algorithms. Unsupervised consensus clustering was performed to categorize the 5-FU resistance-related molecular subtypes of CRC. The expression levels of 5-FRSig, immune checkpoints, and immunoregulators were determined using quantitative real-time polymerase chain reaction (RT‒qPCR). Potential small-molecule agents were identified via Connectivity Map (CMap) and molecular docking.

RESULTS

The 5-FRSig and cluster were confirmed as independent prognostic factors in CRC, as patients in the low-risk group and Cluster 1 had a better prognosis. Notably, 5-FRSig was significantly associated with 5-FU sensitivity, chemotherapy response, immune cell infiltration, immunoreactivity phenotype, immunotherapy efficiency, and drug selection. We predicted 10 potential compounds that bind to the core targets of 5-FRSig with the highest affinity.

CONCLUSION

We developed a valid 5-FRSig to predict the prognosis, chemotherapeutic response, and immune status of CRC patients, thus optimizing the therapeutic benefits of chemotherapy combined with immunotherapy, which can facilitate the development of personalized treatments and novel molecular targeted therapies for patients with CRC.

Immune-related gene-based model predicts the survival of colorectal carcinoma and reflected various biological statuses

Bakcground: Prognosis of colorectal cancer (CRC) varies due to complex genetic-microenviromental interactions, and multiple gene-based prognostic models have been highlighted. Material and Method: In this work, the immune-related genes' expression-based model was developed and the scores of each sample were calculated. The correlation between the model and clinical information, immune infiltration, drug response and biological pathways were analyzed. Results: The high-score samples have a significantly longer survival (overall survival and progression-free survival) period than those with a low score, which was validated across seven datasets containing 1,325 samples (GSE17536 (N = 115), GSE17537 (N = 55), GSE33113 (N = 90), GSE37892 (N = 130), GSE38832 (N = 74), GSE39582 (N = 481), and TCGA (N = 380)). The score is significantly associated with clinical indicators, including age and stage, and further associated with PD-1/PD-L1 gene expression. Furthermore, high-score samples have significantly higher APC and a lower MUC5B mutation rate. The high-score samples show more immune infiltration (including CD4+ and CD8+ T cells, M1/M2 macrophages, and NK cells). Enriched pathway analyses showed that cancer-related pathways, including immune-related pathways, were significantly activated in high-score samples and that some drugs have significantly lower IC50 values than those with low score. Conclusion: The model developed based on immune-related genes is robust and reflected various statuses of CRC and may be a potential clinical indicator.

Cellular and Molecular Mechanisms of the Tumor Stroma in Colorectal Cancer: Insights into Disease Progression and Therapeutic Targets

Colorectal cancer (CRC) is a major health burden worldwide and is the third most common type of cancer. The early detection and diagnosis of CRC is critical to improve patient outcomes. This review explores the intricate interplay between the tumor microenvironment, stromal interactions, and the progression and metastasis of colorectal cancer. The review begins by assessing the gut microbiome's influence on CRC development, emphasizing its association with gut-associated lymphoid tissue (GALT). The role of the Wnt signaling pathway in CRC tumor stroma is scrutinized, elucidating its impact on disease progression. Tumor budding, its effect on tumor stroma, and the implications for patient prognosis are investigated. The review also identifies conserved oncogenic signatures (COS) within CRC stroma and explores their potential as therapeutic targets. Lastly, the seed and soil hypothesis is employed to contextualize metastasis, accentuating the significance of both tumor cells and the surrounding stroma in metastatic propensity. This review highlights the intricate interdependence between CRC cells and their microenvironment, providing valuable insights into prospective therapeutic approaches targeting tumor-stroma interactions.

Construction of a risk model and prediction of prognosis and immunotherapy based on cuproptosis-related LncRNAs in the urinary system pan-cancer

BACKGROUND

Urinary pan-cancer system is a general term for tumors of the urinary system including renal cell carcinoma (RCC), prostate cancer (PRAD), and bladder cancer (BLCA). Their location, physiological functions, and metabolism are closely related, making the occurrence and outcome of these tumors highly similar. Cuproptosis is a new type of cell death that is different from apoptosis and plays an essential role in tumors. Therefore, it is necessary to study the molecular mechanism of cuproptosis-related lncRNAs to urinary system pan-cancer for the prognosis, clinical diagnosis, and treatment of urinary tumors.

METHOD

In our study, we identified 35 co-expression cuproptosis-related lncRNAs (CRLs) from the urinary pan-cancer system. 28 CRLs were identified as prognostic-related CRLs by univariate Cox regression analysis. Then 12 CRLs were obtained using lasso regression and multivariate cox analysis to construct a prognostic model. We divided patients into high- and low-risk groups based on the median risk scores. Next, Kaplan-Meier analysis, principal component analysis (PCA), functional rich annotations, and nomogram were used to compare the differences between the high- and low-risk groups. Finally, the prediction of tumor immune dysfunction and rejection, gene mutation, and drug sensitivity were discussed.

CONCLUSION

Finally, the candidate molecules of the urinary system pan-cancer were identified. This CRLs risk model may be promising for clinical prediction of prognosis and immunotherapy response in urinary system pan-cancer patients.

Comprehensively analysis of immunophenotyping signature in triple-negative breast cancer patients based on machine learning

Immunotherapy is a promising strategy for triple-negative breast cancer (TNBC) patients, however, the overall survival (OS) of 5-years is still not satisfactory. Hence, developing more valuable prognostic signature is urgently needed for clinical practice. This study established and verified an effective risk model based on machine learning methods through a series of publicly available datasets. Furthermore, the correlation between risk signature and chemotherapy drug sensitivity were also performed. The findings showed that comprehensive immune typing is highly effective and accurate in assessing prognosis of TNBC patients. Analysis showed that IL18R1, BTN3A1, CD160, CD226, IL12B, GNLY and PDCD1LG2 are key genes that may affect immune typing of TNBC patients. The risk signature plays a robust ability in prognosis prediction compared with other clinicopathological features in TNBC patients. In addition, the effect of our constructed risk model on immunotherapy response was superior to TIDE results. Finally, high-risk groups were more sensitive to MR-1220, GSK2110183 and temsirolimus, indicating that risk characteristics could predict drug sensitivity in TNBC patients to a certain extent. This study proposes an immunophenotype-based risk assessment model that provides a more accurate prognostic assessment tool for patients with TNBC and also predicts new potential compounds by performing machine learning algorithms.

Deciphering the immune heterogeneity dominated by natural killer cells with prognostic and therapeutic implications in hepatocellular carcinoma

Belonging to type 1 innate lymphoid cells (ILC1), natural killer (NK) cells play an important role not only in fighting microbial infections but also in anti-tumor response. Hepatocellular carcinoma (HCC) represents an inflammation-related malignancy and NK cells are enriched in the liver, making them an essential component of the HCC immune microenvironment. In this study, we performed single-cell RNA-sequencing (scRNA-seq) analysis to identify the NK cell marker genes (NKGs) and uncovered 80 prognosis-related ones by the TCGA-LIHC dataset. Based on prognostic NKGs, HCC patients were categorized into two subtypes with distinct clinical outcomes. Subsequently, we conducted LASSO-COX and stepwise regression analysis on prognostic NKGs to establish a five-gene (UBB, CIRBP, GZMH, NUDC, and NCL) prognostic signature-NKscore. Different mutation statuses of the two risk groups stratified by NKscore were comprehensively characterized. Besides, the established NKscore-integrated nomogram presented enhanced predictive performance. Single sample gene set enrichment analysis (ssGSEA) analysis was used to uncover the landscape of the tumor immune microenvironment (TIME) and the high-NKscore risk group was characterized with an immune-exhausted phenotype while the low-NKscore risk group held relatively strong anti-cancer immunity. T cell receptor (TCR) repertoire, tumor inflammation signature (TIS), and Immunophenoscore (IPS) analyses revealed differences in immunotherapy sensitivity between the two NKscore risk groups. Taken together, we developed a novel NK cell-related signature to predict the prognosis and immunotherapy efficacy for HCC patients.

A novel cuproptosis-related gene model predicts outcomes and treatment responses in pancreatic adenocarcinoma

BACKGROUND

Cuproptosis is recently emerging as a hot spot in cancer research. However, its role in pancreatic adenocarcinoma (PAAD) has not yet been clarified. This study aimed to explore the prognostic and therapeutic implications of cuproptosis-related genes in PAAD.

METHODS

Two hundred thirteen PAAD samples from the International Cancer Genome Consortium (ICGC) were split into training and validation sets in the ratio of 7:3. The Cox regression analyses generated a prognostic model using the ICGC cohort for training (n = 152) and validation (n = 61). The model was externally tested on the Gene Expression Omnibus (GEO) (n = 80) and The Cancer Genome Atlas (TCGA) datasets (n = 176). The clinical characteristics, molecular mechanisms, immune landscape, and treatment responses in model-defined subgroups were explored. The expression of an independent prognostic gene TSC22D2 was confirmed by public databases, real-time quantitative PCR (RT-qPCR), western blot (WB), and immunohistochemistry (IHC).

RESULTS

A prognostic model was established based on three cuproptosis-related genes (TSC22D2, C6orf136, PRKDC). Patients were stratified into high- and low-risk groups using the risk score based on this model. PAAD patients in the high-risk group had a worse prognosis. The risk score was statistically significantly correlated with most clinicopathological characteristics. The risk score based on this model was an independent predictor of overall survival (OS) (HR = 10.7, p < 0.001), and was utilized to create a scoring nomogram with excellent prognostic value. High-risk patients had a higher TP53 mutation rate and a superior response to multiple targeted therapies and chemotherapeutic drugs, but might obtain fewer benefits from immunotherapy. Moreover, elevated TSC22D2 expression was discovered to be an independent prognostic predictor for OS (p < 0.001). Data from public databases and our own experiments showed that TSC22D2 expression was significantly higher in pancreatic cancer tissues/cells compared to normal tissues/cells.

CONCLUSION

This novel model based on cuproptosis-related genes provided a robust biomarker for predicting the prognosis and treatment responses of PAAD. The potential roles and underlying mechanisms of TSC22D2 in PAAD need further explored.

Developing an m5C regulator-mediated RNA methylation modification signature to predict prognosis and immunotherapy efficacy in rectal cancer

Background

Currently, a very small number of patients with colorectal cancer (CRC) respond to immune checkpoint inhibitor (ICI) treatment. Therefore, there is an urgent need to investigate effective biomarkers to determine the responsiveness to ICI treatment. Recently, aberrant 5-methylcytosine (m⁵C) RNA modification has emerged as a key player in the pathogenesis of cancer. Thus, we aimed to explore the predictive signature based on m⁵C regulator-related genes for characterizing the immune landscapes and predicting the prognosis and response to therapies.

Methods

The Cancer Genome Atlas (TCGA) cohort was used as the training set, while GEO data sets, real-time quantitative PCR (RT-qPCR) analysis from paired frozen tissues, and immunohistochemistry (IHC) data from tissue microarray (TMA) were used for validation. We constructed a novel signature based on three m⁵C regulator-related genes in patients with rectal adenocarcinoma (READ) using a least absolute shrinkage and selection operator (LASSO)-Cox regression and unsupervised consensus clustering analyses. Additionally, we correlated the three-gene signature risk model with the tumor immune microenvironment, immunotherapy efficiency, and potential applicable drugs.

Results

The m⁵C methylation-based signature was an independent prognostic factor, where low-risk patients showed a stronger immunoreactivity phenotype and a superior response to ICI therapy. Conversely, the high-risk patients had enriched pathways of cancer hallmarks and presented immune-suppressive state, which demonstrated that they are more insensitive to immunotherapy. Additionally, the signature markedly correlated with drug susceptibility.

Conclusions

We developed a reliable m⁵C regulator-based risk model to predict the prognosis, clarify the molecular and tumor microenvironment status, and identify patients who would benefit from immunotherapy or chemotherapy. Our study could provide vital guidance to improve prognostic stratification and optimize personalized therapeutic strategies for patients with rectal cancer.

Characterization of sialylation-related long noncoding RNAs to develop a novel signature for predicting prognosis, immune landscape, and chemotherapy response in colorectal cancer

Aberrant sialylation plays a key biological role in tumorigenesis and metastasis, including tumor cell survival and invasion, immune evasion, angiogenesis, and resistance to therapy. It has been proposed as a possible cancer biomarker and a potential therapeutic target of tumors. Nevertheless, the prognostic significance and biological features of sialylation-related long noncoding RNAs (lncRNAs) in colorectal cancer (CRC) remain unclear. This study aimed to develop a novel sialylation-related lncRNA signature to accurately evaluate the prognosis of patients with CRC and explore the potential molecular mechanisms of the sialylation-related lncRNAs. Here, we identified sialylation-related lncRNAs using the Pearson correlation analysis on The Cancer Genome Atlas (TCGA) dataset. Univariate and stepwise multivariable Cox analysis were used to establish a signature based on seven sialylation-related lncRNAs in the TCGA dataset, and the risk model was validated in the Gene Expression Omnibus dataset. Kaplan-Meier curve analysis revealed that CRC patients in the low-risk subgroup had a better survival outcome than those in the high-risk subgroup in the training set, testing set, and overall set. Multivariate analysis demonstrated that the sialylation-related lncRNA signature was an independent prognostic factor for overall survival, progression-free survival, and disease-specific survival prediction. The sialylation lncRNA signature-based nomogram exhibited a robust prognostic performance. Furthermore, enrichment analysis showed that cancer hallmarks and oncogenic signaling were enriched in the high-risk group, while inflammatory responses and immune-related pathways were enriched in the low-risk group. The comprehensive analysis suggested that low-risk patients had higher activity of immune response pathways, greater immune cell infiltration, and higher expression of immune stimulators. In addition, we determined the sialylation level in normal colonic cells and CRC cell lines by flow cytometry combined with immunofluorescence, and verified the expression levels of seven lncRNAs using real-time quantitative polymerase chain reaction. Finally, combined drug sensitivity analysis using the Genomics of Drug Sensitivity in Cancer, Cancer Therapeutics Response Portal, and Profiling Relative Inhibition Simultaneously in Mixtures indicated that the sialylation-related lncRNA signature could serve as a potential predictor for chemosensitivity. Collectively, this is the first sialylation lncRNA-based signature for predicting the prognosis, immune landscape, and chemotherapeutic response in CRC, and may provide vital guidance to facilitate risk stratification and optimize individualized therapy for CRC patients.