Distinct tumor microenvironment landscapes of rectal cancer for prognosis and prediction of immunotherapy response

Bu, Fanqin; Zhao, Yu; Zhao, Yong; Yang, Xiaohan; Sun, Lan; Chen, Yang; Zhu, Shengtao; Min, Li

doi:10.1007/s13402-022-00725-1

Cited by 10 publications

(6 citation statements)

References 59 publications

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“…Although we found no new prognostic factors affecting rectal cancer, the prognostic factors that we identified were easily obtained from practical clinical work. Unlike some prediction models, our prognostic factors does not require a difficult-to-obtain gene test to predict prognosis ( 36 , 37 ).With the help of a multivariate Cox model, we constructed and internally validated a nomogram that has relatively high accuracy and discriminative power. It included only significant variables associated with survival outcomes and did not lack accuracy.…”

Section: Discussionmentioning

confidence: 99%

A nomogram and risk classification system forecasting the cancer-specific survival of lymph- node- positive rectal cancer patient after radical proctectomy

Zhong

Ju²,

Liu³

et al. 2023

Front. Oncol.

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BackgroundThe aim of the study was to develop and validate a nomogram for predicting cancer-specific survival (CSS) in lymph- node- positive rectal cancer patients after radical proctectomy.MethodsIn this study, we analyzed data collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. In addition, in a 7:3 randomized design, all patients were split into two groups (development and validation cohorts). CSS predictors were selected via univariate and multivariate Cox regressions. The nomogram was constructed by analyzing univariate and multivariate predictors. The effectiveness of this nomogram was evaluated by concordance index (C-index), calibration plots, and receiver operating characteristic (ROC) curve. Based on the total score of each patient in the development cohort in the nomogram, a risk stratification system was developed. In order to analyze the survival outcomes among different risk groups, Kaplan–Meier method was used.ResultsWe selected 4,310 lymph- node- positive rectal cancer patients after radical proctectomy, including a development cohort (70%, 3,017) and a validation cohort (30%, 1,293). The nomogram correlation C-index for the development cohort and the validation cohort was 0.702 (95% CI, 0.687–0.717) and 0.690 (95% CI, 0.665–0.715), respectively. The calibration curves for 3- and 5-year CSS showed great concordance. The 3- and 5-year areas under the curve (AUC) of ROC curves in the development cohort were 0.758 and 0.740, respectively, and 0.735 and 0.730 in the validation cohort, respectively. Following the establishment of the nomogram, we also established a risk stratification system. According to their nomogram total points, patients were divided into three risk groups. There were significant differences between the low-, intermediate-, and high-risk groups (p< 0.05).ConclusionsAs a result of our research, we developed a highly discriminatory and accurate nomogram and associated risk classification system to predict CSS in lymph-node- positive rectal cancer patients after radical proctectomy. This model can help predict the prognosis of patients with lymph- node- positive rectal cancer.

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Section: Discussionmentioning

confidence: 99%

A nomogram and risk classification system forecasting the cancer-specific survival of lymph- node- positive rectal cancer patient after radical proctectomy

Zhong

Ju²,

Liu³

et al. 2023

Front. Oncol.

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“…Studies have also analyzed single-cell transcriptome data of lung adenocarcinoma for intercellular communication between cell subpopulations and found functionally signi cant interactions between lung adenocarcinoma cells and T cells [33]. In studies of rectal cancer, interactions between epithelial cells and other cells in uence tumor progression, which may provide potential targets for activating or blocking cellular communication [34]. Cellular communication analysis of the single cell transcriptome of hepatocellular carcinoma revealed interactions between hepatocytes and endothelial cells, which play a key role in tumor angiogenesis in the development of hepatocellular carcinoma [35].…”

Section: Discussionmentioning

confidence: 99%

Multiomics reveals the role of macrophage-vascular endothelial cell interactions in the hepatoblastoma tumor microenvironment

Zhu

Xie

Kang

et al. 2023

Preprint

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Background Hepatoblastoma is a common tumor in childhood, characterized by immature histology and diverse cell lineages. The purpose of this study is to identify the genes that are abnormally expressed in hepatoblastoma, and to explore and verify the targets of intercellular communication that affect the tumor immune microenvironment. Methods Through comprehensive analysis of gene expression from GSE133039 and GSE180664 data sets, the differentially expressed genes in cancer tissues and adjacent tissues were obtained. GO and KEGG enrichment analysis is used to predict the biological function and signal transduction pathway of differential expression gene enrichment. Use cytoscape to build PPI network to filter hubgene; Construct correlation analysis of immune cell infiltration to infer the correlation between immune cells. Combined with single-cell transcriptome data, further reveal the relationship between cells and signal targets of cell communication. Results 58 differentially expressed genes with high expression and 94 differentially expressed genes with low expression were obtained from the two data sets of hepatoblastoma. They were mainly involved in the signal transduction related to metastasis. PPI network screened 50 hubgenes. The correlation analysis of immune cell infiltration of different genes showed that macrophages were significantly correlated with endothelial cells. Combined with the analysis of single-cell transcriptome data, hepatoblastoma was divided into 11 cell subpopulations, and 16 genes in hubgene were expressed in different cell subpopulations, in which LFNG was highly expressed in macrophages and monocytes, which served as the target of intercellular communication to promote the development of hepatoblastoma. Conclusion In this study, we identified the genes that were abnormally expressed in hepatoblastoma, and affected the cellular communication of hepatoblastoma through LFNG target, thus affecting the progress of tumor. Therefore, LFNG may become a therapeutic target for hepatoblastoma.

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“…The GSE161277 dataset has initially revealed the heterogeneity of abnormal epithelial cells and the complexity of the tumor microenvironment ( 74 ). It is indicated that the GSE161277 dataset has been utilized for single-cell analysis to examine the immune typing and tumor microenvironment of rectal cancer ( 75 ). This study first classified the COAD cells into two risk groups based on distinct zinc transport states through single-cell analysis of GSE161277, which provided a basis for the subsequent research of zinc transport heterogeneity in COAD and the development of the prognosis-predictive model.…”

Section: Discussionmentioning

confidence: 99%

Construction of a prognostic model for colorectal adenocarcinoma based on Zn transport-related genes identified by single-cell sequencing and weighted co-expression network analysis

Chen,

Zhao,

Fan

et al. 2023

Front. Oncol.

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BackgroundColorectal cancer (CRC) is one of the most prevalent malignancies and the third most lethal cancer globally. The most reported histological subtype of CRC is colon adenocarcinoma (COAD). The zinc transport pathway is critically involved in various tumors, and its anti-tumor effect may be through improving immune function. However, the Zn transport pathway in COAD has not been reported.MethodsThe determination of Zn transport-related genes in COAD was carried out through single-cell analysis of the GSE 161277 obtained from the GEO dataset. Subsequently, a weighted co-expression network analysis of the TCGA cohort was performed. Then, the prognostic model was conducted utilizing univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Functional enrichment, immune microenvironment, and survival analyses were also carried out. Consensus clustering analysis was utilized to verify the validity of the prognostic model and explore the immune microenvironment. Ultimately, cell experiments, including CCK-8,transwell and scratch assays, were performed to identify the function of LRRC59 in COAD.ResultsAccording to the Zn transport-related prognostic model, the individuals with COAD in TCGA and GEO databases were classified into high- and low-risk groups. The group with low risk had a comparatively more favorable prognosis. Two groups had significant variations in the immune infiltration, MHC, and the expression of genes related to the immune checkpoint. The cell experiments indicated that the proliferation, migration, and invasion of the HCT-116, DLD-1, and RKO cell lines were considerably increased after LRRC59 knockdown. It proved that LRRC59 was indeed a protective factor for COAD.ConclusionA prognostic model for COAD was developed using zinc transport-related genes. This model can efficiently assess the immune microenvironment and prognosis of individuals with COAD. Subsequently, the function of LRRC59 in COAD was validated via cell experiments, highlighting its potential as a biomarker.

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Distinct tumor microenvironment landscapes of rectal cancer for prognosis and prediction of immunotherapy response

Cited by 10 publications

References 59 publications

A nomogram and risk classification system forecasting the cancer-specific survival of lymph- node- positive rectal cancer patient after radical proctectomy

A nomogram and risk classification system forecasting the cancer-specific survival of lymph- node- positive rectal cancer patient after radical proctectomy

Multiomics reveals the role of macrophage-vascular endothelial cell interactions in the hepatoblastoma tumor microenvironment

Construction of a prognostic model for colorectal adenocarcinoma based on Zn transport-related genes identified by single-cell sequencing and weighted co-expression network analysis

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