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Hello, everyone, today I will share with you an article "A Chemoresistance lncRNA Signature for Recurrence Risk Stratification of Colon Cancer Patients with Chemotherapy" published in Molecular Therapy-Nucleic Acids magazine (IF:7.
032), which the author constructed in this article The characteristic CRLSig prediction model of chemotherapy resistance lncRNA
.
At first glance at the title, the editor didn’t think it had any peculiarities, but after intensive reading, I found that it’s indescribable~, when the greasy "Meta" meets the pure "Shengxin", especially the author also used a A new type of protein analysis tool "Protemaps" instantly adds a lot of color to the article.
Curious friends and editors can come and learn together~ Data source: TCGA: https://portal.
gdc.
cancer.
govGEO: http:/ / Screening criteria: 1) Contains overall/recurrence-free survival data; 2) Contains information on chemotherapy and other therapies; 3) Sample size exceeds 50 patients; 4) There are clinical data, such as AJCCTNM staging, age And gender
.
Research objects: ★A total of 1171 colon cancer patients were enrolled (715 who did not receive chemotherapy, and 456 received chemotherapy)
.
★Patients receiving chemotherapy are used as training cohorts (GSE103479 (n=60), GSE39582 (n=240) and GSE72970 (n=83))
.
★71 patients in the TCGA_COAD cohort who only received chemotherapy were used as an external verification cohort
.
★Patients who did not receive chemotherapy served as the control group GSE103479 (n=70), GSE39582 (n=326) and TCGA_COAD (n=319)
.
★65362 cells of SMC queue (GSE132465) single-cell sequencing data
.
The CMS subtype of epithelial cells in the scRNA-seq data is also from the SMC cohort
.
Patient baseline data: patient baseline data flow chart.
Figure 1 shows the results 1.
Identify chemotherapy-related lncRNAs in colon cancer patients through Meta-analysis.
Using GENCODE v25 and RefSeq v79 for annotation, a total of 2456 unique ones were obtained from GSE103479, GSE39582 and GSE72970 lncRNA
.
Then, a Cox proportional hazard regression analysis was performed to detect the RFS-related hazard ratio (HR, 95% CI) of 2456 lncRNAs, and adjust it according to the patient's TNM staging
.
In order to obtain the lncRNAs related to RFS in patients receiving chemotherapy, we used Meta-analysis fixed-effects model to summarize the HR of 2456 lncRNAs.
The results showed that 177 lncRNAs were significantly related to RFS in non-chemotherapy patients, and 268 lncRNAs were in chemotherapy patients.
Significantly correlated with RFS (Figure 2A)
.
Among them, among colon cancer patients receiving chemotherapy, 55 lncRNAs were identified as stable chemotherapy-resistant lncRNAs (meta-HR>1, P<0.
00)
.
These 55 lncRNAs were positively correlated with chemotherapy patients (Figure 2B)
.
The prognostic value and β coefficient of chemotherapy-related lncRNA are shown in Figure 2C and Supplementary Table S3
.
Figure 22.
Construction and verification of CRLSig In order to better distinguish chemotherapy-resistant populations, lncRNAs with significantly poor prognosis found in chemotherapy patients were selected to construct a chemoresistant lncRNAs feature called CRLSig, such as where i and j represent chemotherapy The sequence and total number of related lncRNAs, βi is the coefficient of the corresponding lncRNA, and expi is the normalized expression of the lncRNA in the corresponding queue
.
Prognostic analysis showed that the RFS of colon cancer patients in the chemotherapy group could be significantly differentiated in these cohorts by CRLSig (Figure 3A-3C)
.
In addition, in the validation set, a significant difference between the high CRLSig group and the low CRLSig group can be observed (Figure 3D)
.
Studies have shown that CRLSig can distinguish between high and low groups.
Even if patients receive standard adjuvant chemotherapy, this tool can still be used to identify patients with better prognosis or patients at high risk of RFS receiving chemotherapy
.
ROC analysis showed that the AUC of CRLSig predicting the prognosis of colon cancer were GSE39582 (0.
58-0.
65), GSE72790 (0.
73-0.
75), GSE103479 (0.
46-0.
65) and TCGA_COAD (0.
71-0.
83) (Figure 3E-3H)
.
The histogram of the recurrence status increasing with CRLSig also shows the same trend (Figure 3I-3L)
.
Figure 33.
CRLSig subgroup analysis of colon cancer chemotherapy patients further tested the CRLSig prognostic value of different TNM stages in the GSE39582, GSE72970, and TCGA_COAD cohorts (the patients of GSE103479 are all stage II+III, no stage IV) to evaluate the accuracy of CRLSig And effectiveness.
Although CRLSig adjusted based on TNM has been constructed, it was found that in the training cohort (GSE72970 and GSE39582) and the validation cohort (TCGA_COAD) (Figure 4A-4F), the high sum of patients in stage II+III or IV There are still significant differences in the RFS of the low CRLSig group
.
Figure 44.
CRLSig's prediction of response to chemotherapy treatment.
Considering that 55 lncRNAs may reduce the effectiveness of chemotherapy, the authors validated CRLSig's prediction of response to chemotherapy in the TCGA cohort (n=71)
.
The bar graph shows that the CRLSig score increases as SD/PD increases (Figure 5A)
.
The chi-square test of the histogram also found a significant association between CRLSig and chemotherapy response (Figure 5B)
.
The CRLSig score of patients with SD/PD state chemotherapy was higher than that of patients with PR/CR state (Figure 5C)
.
ROC analysis showed that the AUC of PR/CR predicted by CRLSig with 95% CI was 0.
731 (0.
554-0.
878) (Figure 5D)
.
Figure 5A-D5.
Pathway enrichment analysis between low and high CRLSig in TCGA_COAD Here, the author uses a new protein quantitative data analysis tool "protemaps", which can display the quantitative composition of proteins, with a focus on protein Abundance and function
.
In the protein map, each protein is displayed as a polygon, and functionally related proteins appear in adjacent regions.
The size of the region represents the abundance of the protein
.
According to the analysis of the KEGG pathway, the proportion of metabolic genes in patients who respond to chemotherapy is higher than that in patients who do not respond (Figure 5E-5F)
.
In addition, some metabolic genes in patients with low CRLSig are similar to those in patients who respond to chemotherapy (Figure 5G-5H)
.
Therefore, in the response group or the low CRLSig group, higher levels of metabolic RNA dominate
.
In order to observe the metabolic characteristics of these patients, the author further collected 113 metabolic pathways previously studied, and performed GSVA analysis (gene set variation analysis) on TCGA_COAD patients receiving chemotherapy in this study
.
By comparing the high/low CRLSig group or CR, PR/PD, SD group of chemotherapy patients, it was found that 27 and 25 metabolic pathways were activated in PR/CR patients and low CRLSig patients, respectively
.
Among them, 17 metabolic pathways including urea cycle, pyruvate metabolism and lipoic acid metabolism were activated in the PR/CR and low CRLSig groups (Figure 5I and Figure 5J)
.
Figure 5E-J6.
The independence of CRLSig from other clinicopathological factors and its clinical characteristics.
To determine whether the prognostic value of CRLSig is independent of other clinicopathological factors in the training and validation cohort, the authors used univariate Cox regression analysis to test the performance of CRLSig (Supplementary Table S7)
.
At the same time, a multivariate Cox regression analysis was performed, and the results showed that CRLSig was significantly correlated with RFS in the training and validation cohorts (Figure 6A)
.
The authors also tested the relationship between CRLSig and other clinicopathological factors in the TCGA cohort
.
In addition to OS and RFS, CRLSig is also significantly related to the TNM staging of colon cancer patients undergoing chemotherapy (Figure 6B)
.
In the TCGA validation cohort, CRLSig was significantly correlated with OS (Figure 6C)
.
In order to verify the application value of CRLSig in other cancer patients receiving chemotherapy, the author performed CRLSig Cox regression analysis on 18 cancer patients receiving chemotherapy and found that in the PAAD and STAD cohorts, CRLSig scores can be used to divide patients into two groups.
The prognosis is significantly different (Supplementary Figures S6A-S6C)
.
Figure 6A-C7.
Microenvironmental characteristics of CRLSig In the TCGA cohort, the author obtained a large number of immune infiltrating cells from colon cancer patients receiving chemotherapy through the xCellR package
.
It was found that natural killer T (NKT) cells are significantly related to CRLSig
.
Other immune cells, including macrophages, CD8+ naive T cells, CD8+ T cells, and CD8+ Tcm, are negatively correlated with CRLSig (Figure 6D)
.
Some chemokine and cytokine receptor genes, such as CSF2RB, CXCL14, and CXCL9, are also negatively correlated with CRLSig (Figure 6E)
.
Figure 6D-E In addition, the author calculated the CRLSig in 65362 cells in the single-cell sequencing data of GSE132465, and found that the CRLSig value in stromal cells was higher compared with other cells (Figure 6F-6G)
.
Consensus molecular classification (CMS) provides a biologically reasonable stratification for colorectal cancer
.
In order to explore the relationship between CRLSig and CMS subtypes at the single-cell level, the authors used previous scRNAseq data to find that CMS1 and CMS4 subtypes have higher CRLSig than other CMS subtypes in tumor epithelial cells (Figure 6H)
.
Figure 6F-H Conclusion Colon cancer is one of the most common malignant tumors of the gastrointestinal tract.
Colon resection combined with adjuvant chemotherapy and radiotherapy is recognized as the standard treatment for colon cancer
.
Although chemotherapy is beneficial, the results vary widely
.
In addition, effective clinical predictors have not been developed to determine which colon cancer patients will benefit from chemotherapy, which also indicates the importance of appropriate patient stratification
.
In this article, the author proposes a long-chain non-coding RNA (lncRNA)-based chemotherapy prediction model for colon cancer patients, hoping to provide better patient stratification and thereby achieve more effective personalized chemotherapy
.
So, although the analysis methods and routines are similar, the author’s research still has certain clinical significance.
The author also combines two big data analysis tools, meta-analysis and bio-information analysis, to innovate on the basis of classics.
Isn't it what we should pursue? References: Hao Wang, Yuzhen Gao, Somayeh Vafaei, Qiaoyan Yu, Jun Zhang, Liangjing Wang, A Chemoresistance lncRNA Signature for Recurrence Risk Stratification of Colon Cancer Patients with Chemotherapy, Molecular Therapy-Nucleic Acids, 2021, ISSN 2162-2531, https ://doi.
org/10.
1016/j.
omtn.
2021.
12.
015.
Team Introduction The Beijing General Biology (Shengxinren) team was established in June 2014
.
The main business is medical scientific research services, personalized bio-information analysis, software development and platform construction
.
The team's main creators are all front-line scientific research workers with rich experience in the industry
.
In 2020, the team will complete 300+ projects, with a cumulative impact factor of 700+, open letter tools 140+, software and other intellectual property applications 30+, and serve 220+ customers
.
The company has been deeply engaged in the personalized analysis of bio-credit for several years, and has an effective management plan to help everyone shorten the last mile of scientific research
.
From the selection of research direction, program setting, data selection, after-sales processing, and personalized modification, each link is deeply involved, and truly customized according to customer needs
.
The Shengxin people team is committed to creating a new model of scientific research services, based on technical services, with the advantages of technical training and database platform development, focusing on serving medical scientific research users, allowing you to smile at scientific research and make scientific research easier
.
You can also contact directly for questions related to health information: 18501230653 (same number on WeChat)
032), which the author constructed in this article The characteristic CRLSig prediction model of chemotherapy resistance lncRNA
.
At first glance at the title, the editor didn’t think it had any peculiarities, but after intensive reading, I found that it’s indescribable~, when the greasy "Meta" meets the pure "Shengxin", especially the author also used a A new type of protein analysis tool "Protemaps" instantly adds a lot of color to the article.
Curious friends and editors can come and learn together~ Data source: TCGA: https://portal.
gdc.
cancer.
govGEO: http:/ / Screening criteria: 1) Contains overall/recurrence-free survival data; 2) Contains information on chemotherapy and other therapies; 3) Sample size exceeds 50 patients; 4) There are clinical data, such as AJCCTNM staging, age And gender
.
Research objects: ★A total of 1171 colon cancer patients were enrolled (715 who did not receive chemotherapy, and 456 received chemotherapy)
.
★Patients receiving chemotherapy are used as training cohorts (GSE103479 (n=60), GSE39582 (n=240) and GSE72970 (n=83))
.
★71 patients in the TCGA_COAD cohort who only received chemotherapy were used as an external verification cohort
.
★Patients who did not receive chemotherapy served as the control group GSE103479 (n=70), GSE39582 (n=326) and TCGA_COAD (n=319)
.
★65362 cells of SMC queue (GSE132465) single-cell sequencing data
.
The CMS subtype of epithelial cells in the scRNA-seq data is also from the SMC cohort
.
Patient baseline data: patient baseline data flow chart.
Figure 1 shows the results 1.
Identify chemotherapy-related lncRNAs in colon cancer patients through Meta-analysis.
Using GENCODE v25 and RefSeq v79 for annotation, a total of 2456 unique ones were obtained from GSE103479, GSE39582 and GSE72970 lncRNA
.
Then, a Cox proportional hazard regression analysis was performed to detect the RFS-related hazard ratio (HR, 95% CI) of 2456 lncRNAs, and adjust it according to the patient's TNM staging
.
In order to obtain the lncRNAs related to RFS in patients receiving chemotherapy, we used Meta-analysis fixed-effects model to summarize the HR of 2456 lncRNAs.
The results showed that 177 lncRNAs were significantly related to RFS in non-chemotherapy patients, and 268 lncRNAs were in chemotherapy patients.
Significantly correlated with RFS (Figure 2A)
.
Among them, among colon cancer patients receiving chemotherapy, 55 lncRNAs were identified as stable chemotherapy-resistant lncRNAs (meta-HR>1, P<0.
00)
.
These 55 lncRNAs were positively correlated with chemotherapy patients (Figure 2B)
.
The prognostic value and β coefficient of chemotherapy-related lncRNA are shown in Figure 2C and Supplementary Table S3
.
Figure 22.
Construction and verification of CRLSig In order to better distinguish chemotherapy-resistant populations, lncRNAs with significantly poor prognosis found in chemotherapy patients were selected to construct a chemoresistant lncRNAs feature called CRLSig, such as where i and j represent chemotherapy The sequence and total number of related lncRNAs, βi is the coefficient of the corresponding lncRNA, and expi is the normalized expression of the lncRNA in the corresponding queue
.
Prognostic analysis showed that the RFS of colon cancer patients in the chemotherapy group could be significantly differentiated in these cohorts by CRLSig (Figure 3A-3C)
.
In addition, in the validation set, a significant difference between the high CRLSig group and the low CRLSig group can be observed (Figure 3D)
.
Studies have shown that CRLSig can distinguish between high and low groups.
Even if patients receive standard adjuvant chemotherapy, this tool can still be used to identify patients with better prognosis or patients at high risk of RFS receiving chemotherapy
.
ROC analysis showed that the AUC of CRLSig predicting the prognosis of colon cancer were GSE39582 (0.
58-0.
65), GSE72790 (0.
73-0.
75), GSE103479 (0.
46-0.
65) and TCGA_COAD (0.
71-0.
83) (Figure 3E-3H)
.
The histogram of the recurrence status increasing with CRLSig also shows the same trend (Figure 3I-3L)
.
Figure 33.
CRLSig subgroup analysis of colon cancer chemotherapy patients further tested the CRLSig prognostic value of different TNM stages in the GSE39582, GSE72970, and TCGA_COAD cohorts (the patients of GSE103479 are all stage II+III, no stage IV) to evaluate the accuracy of CRLSig And effectiveness.
Although CRLSig adjusted based on TNM has been constructed, it was found that in the training cohort (GSE72970 and GSE39582) and the validation cohort (TCGA_COAD) (Figure 4A-4F), the high sum of patients in stage II+III or IV There are still significant differences in the RFS of the low CRLSig group
.
Figure 44.
CRLSig's prediction of response to chemotherapy treatment.
Considering that 55 lncRNAs may reduce the effectiveness of chemotherapy, the authors validated CRLSig's prediction of response to chemotherapy in the TCGA cohort (n=71)
.
The bar graph shows that the CRLSig score increases as SD/PD increases (Figure 5A)
.
The chi-square test of the histogram also found a significant association between CRLSig and chemotherapy response (Figure 5B)
.
The CRLSig score of patients with SD/PD state chemotherapy was higher than that of patients with PR/CR state (Figure 5C)
.
ROC analysis showed that the AUC of PR/CR predicted by CRLSig with 95% CI was 0.
731 (0.
554-0.
878) (Figure 5D)
.
Figure 5A-D5.
Pathway enrichment analysis between low and high CRLSig in TCGA_COAD Here, the author uses a new protein quantitative data analysis tool "protemaps", which can display the quantitative composition of proteins, with a focus on protein Abundance and function
.
In the protein map, each protein is displayed as a polygon, and functionally related proteins appear in adjacent regions.
The size of the region represents the abundance of the protein
.
According to the analysis of the KEGG pathway, the proportion of metabolic genes in patients who respond to chemotherapy is higher than that in patients who do not respond (Figure 5E-5F)
.
In addition, some metabolic genes in patients with low CRLSig are similar to those in patients who respond to chemotherapy (Figure 5G-5H)
.
Therefore, in the response group or the low CRLSig group, higher levels of metabolic RNA dominate
.
In order to observe the metabolic characteristics of these patients, the author further collected 113 metabolic pathways previously studied, and performed GSVA analysis (gene set variation analysis) on TCGA_COAD patients receiving chemotherapy in this study
.
By comparing the high/low CRLSig group or CR, PR/PD, SD group of chemotherapy patients, it was found that 27 and 25 metabolic pathways were activated in PR/CR patients and low CRLSig patients, respectively
.
Among them, 17 metabolic pathways including urea cycle, pyruvate metabolism and lipoic acid metabolism were activated in the PR/CR and low CRLSig groups (Figure 5I and Figure 5J)
.
Figure 5E-J6.
The independence of CRLSig from other clinicopathological factors and its clinical characteristics.
To determine whether the prognostic value of CRLSig is independent of other clinicopathological factors in the training and validation cohort, the authors used univariate Cox regression analysis to test the performance of CRLSig (Supplementary Table S7)
.
At the same time, a multivariate Cox regression analysis was performed, and the results showed that CRLSig was significantly correlated with RFS in the training and validation cohorts (Figure 6A)
.
The authors also tested the relationship between CRLSig and other clinicopathological factors in the TCGA cohort
.
In addition to OS and RFS, CRLSig is also significantly related to the TNM staging of colon cancer patients undergoing chemotherapy (Figure 6B)
.
In the TCGA validation cohort, CRLSig was significantly correlated with OS (Figure 6C)
.
In order to verify the application value of CRLSig in other cancer patients receiving chemotherapy, the author performed CRLSig Cox regression analysis on 18 cancer patients receiving chemotherapy and found that in the PAAD and STAD cohorts, CRLSig scores can be used to divide patients into two groups.
The prognosis is significantly different (Supplementary Figures S6A-S6C)
.
Figure 6A-C7.
Microenvironmental characteristics of CRLSig In the TCGA cohort, the author obtained a large number of immune infiltrating cells from colon cancer patients receiving chemotherapy through the xCellR package
.
It was found that natural killer T (NKT) cells are significantly related to CRLSig
.
Other immune cells, including macrophages, CD8+ naive T cells, CD8+ T cells, and CD8+ Tcm, are negatively correlated with CRLSig (Figure 6D)
.
Some chemokine and cytokine receptor genes, such as CSF2RB, CXCL14, and CXCL9, are also negatively correlated with CRLSig (Figure 6E)
.
Figure 6D-E In addition, the author calculated the CRLSig in 65362 cells in the single-cell sequencing data of GSE132465, and found that the CRLSig value in stromal cells was higher compared with other cells (Figure 6F-6G)
.
Consensus molecular classification (CMS) provides a biologically reasonable stratification for colorectal cancer
.
In order to explore the relationship between CRLSig and CMS subtypes at the single-cell level, the authors used previous scRNAseq data to find that CMS1 and CMS4 subtypes have higher CRLSig than other CMS subtypes in tumor epithelial cells (Figure 6H)
.
Figure 6F-H Conclusion Colon cancer is one of the most common malignant tumors of the gastrointestinal tract.
Colon resection combined with adjuvant chemotherapy and radiotherapy is recognized as the standard treatment for colon cancer
.
Although chemotherapy is beneficial, the results vary widely
.
In addition, effective clinical predictors have not been developed to determine which colon cancer patients will benefit from chemotherapy, which also indicates the importance of appropriate patient stratification
.
In this article, the author proposes a long-chain non-coding RNA (lncRNA)-based chemotherapy prediction model for colon cancer patients, hoping to provide better patient stratification and thereby achieve more effective personalized chemotherapy
.
So, although the analysis methods and routines are similar, the author’s research still has certain clinical significance.
The author also combines two big data analysis tools, meta-analysis and bio-information analysis, to innovate on the basis of classics.
Isn't it what we should pursue? References: Hao Wang, Yuzhen Gao, Somayeh Vafaei, Qiaoyan Yu, Jun Zhang, Liangjing Wang, A Chemoresistance lncRNA Signature for Recurrence Risk Stratification of Colon Cancer Patients with Chemotherapy, Molecular Therapy-Nucleic Acids, 2021, ISSN 2162-2531, https ://doi.
org/10.
1016/j.
omtn.
2021.
12.
015.
Team Introduction The Beijing General Biology (Shengxinren) team was established in June 2014
.
The main business is medical scientific research services, personalized bio-information analysis, software development and platform construction
.
The team's main creators are all front-line scientific research workers with rich experience in the industry
.
In 2020, the team will complete 300+ projects, with a cumulative impact factor of 700+, open letter tools 140+, software and other intellectual property applications 30+, and serve 220+ customers
.
The company has been deeply engaged in the personalized analysis of bio-credit for several years, and has an effective management plan to help everyone shorten the last mile of scientific research
.
From the selection of research direction, program setting, data selection, after-sales processing, and personalized modification, each link is deeply involved, and truly customized according to customer needs
.
The Shengxin people team is committed to creating a new model of scientific research services, based on technical services, with the advantages of technical training and database platform development, focusing on serving medical scientific research users, allowing you to smile at scientific research and make scientific research easier
.
You can also contact directly for questions related to health information: 18501230653 (same number on WeChat)
