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A : Serum GDF-8 levels; B : Serum CRP levels; C : Serum IL-6 levels; D : Serum <t>FNDC5</t> levels; E : Protein band patterns for RUNX2 and OCN; F : Relative expression levels of RUNX2 protein; G : Relative expression levels of OCN protein; Statistical analysis was performed using one-way ANOVA followed by Tukey’s post hoc test. Data are expressed as mean ± SD ( n = 10 per group). Group abbreviations (LD, MD, HD) are as defined in Table . * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001
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A : Serum GDF-8 levels; B : Serum CRP levels; C : Serum IL-6 levels; D : Serum <t>FNDC5</t> levels; E : Protein band patterns for RUNX2 and OCN; F : Relative expression levels of RUNX2 protein; G : Relative expression levels of OCN protein; Statistical analysis was performed using one-way ANOVA followed by Tukey’s post hoc test. Data are expressed as mean ± SD ( n = 10 per group). Group abbreviations (LD, MD, HD) are as defined in Table . * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001
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Prognostic TMET genes defining TMET subtype phenotypes. (A) Univariate Cox regression analysis of TMET crosstalk genes in CRC. (B) Heatmap of the top 28 prognostically relevant TMET genes with significant differential expression (|logFC| > 0.15). (C,D) KEGG pathway enrichment of C1- and C2-upregulated TMET genes. (E,F) Kaplan–Meier survival curves stratified by CKMT2 and <t>PDE2A</t> expression. (G) PCA plot showing subtype separation driven by CKMT2 and PDE2A expression. (H) ROC curves evaluating the discriminatory performance of CKMT2 and PDE2A.
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Prognostic TMET genes defining TMET subtype phenotypes. (A) Univariate Cox regression analysis of TMET crosstalk genes in CRC. (B) Heatmap of the top 28 prognostically relevant TMET genes with significant differential expression (|logFC| > 0.15). (C,D) KEGG pathway enrichment of C1- and C2-upregulated TMET genes. (E,F) Kaplan–Meier survival curves stratified by CKMT2 and <t>PDE2A</t> expression. (G) PCA plot showing subtype separation driven by CKMT2 and PDE2A expression. (H) ROC curves evaluating the discriminatory performance of CKMT2 and PDE2A.
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Prognostic TMET genes defining TMET subtype phenotypes. (A) Univariate Cox regression analysis of TMET crosstalk genes in CRC. (B) Heatmap of the top 28 prognostically relevant TMET genes with significant differential expression (|logFC| > 0.15). (C,D) KEGG pathway enrichment of C1- and C2-upregulated TMET genes. (E,F) Kaplan–Meier survival curves stratified by <t>CKMT2</t> and PDE2A expression. (G) PCA plot showing subtype separation driven by CKMT2 and PDE2A expression. (H) ROC curves evaluating the discriminatory performance of CKMT2 and PDE2A.
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A : Serum GDF-8 levels; B : Serum CRP levels; C : Serum IL-6 levels; D : Serum FNDC5 levels; E : Protein band patterns for RUNX2 and OCN; F : Relative expression levels of RUNX2 protein; G : Relative expression levels of OCN protein; Statistical analysis was performed using one-way ANOVA followed by Tukey’s post hoc test. Data are expressed as mean ± SD ( n = 10 per group). Group abbreviations (LD, MD, HD) are as defined in Table . * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001

Journal: Journal of Translational Medicine

Article Title: Targeting the PINK1/Parkin-FNDC5 pathway: a novel mechanism of icariin in regulating muscle-bone metabolic coupling in osteosarcopenia

doi: 10.1186/s12967-026-08017-0

Figure Lengend Snippet: A : Serum GDF-8 levels; B : Serum CRP levels; C : Serum IL-6 levels; D : Serum FNDC5 levels; E : Protein band patterns for RUNX2 and OCN; F : Relative expression levels of RUNX2 protein; G : Relative expression levels of OCN protein; Statistical analysis was performed using one-way ANOVA followed by Tukey’s post hoc test. Data are expressed as mean ± SD ( n = 10 per group). Group abbreviations (LD, MD, HD) are as defined in Table . * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001

Article Snippet: After closing, incubate the membrane with anti-PINK1 (1:1000, Bioss, BS-22173R), Parkin (1:1000, Bioss, BS-23687R), LC3 (1:500, Solarbio, K008014P), p62 (1:1000, Bioss, BS-55207R), FNDC5 (1:1000, Bioss, BS-8486R), Runx2 (1:1000, BS-1134R), OCN (1:500, BS-4917R), Actin (1:20,000, Bioss, BS-10966R) as the internal control.

Techniques: Expressing

A : Western blot detection of mitochondrial autophagy-related protein bands in rat gastrocnemius and soleus muscles; B - D : Gray-scale analysis of PINK1, Parkin, and FNDC5 expression levels. Statistical analysis was performed using one-way ANOVA followed by Tukey’s post hoc test. Data are expressed as mean ± SD ( n = 10 per group). Group abbreviations (LD, MD, HD) are as defined in Table . * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001

Journal: Journal of Translational Medicine

Article Title: Targeting the PINK1/Parkin-FNDC5 pathway: a novel mechanism of icariin in regulating muscle-bone metabolic coupling in osteosarcopenia

doi: 10.1186/s12967-026-08017-0

Figure Lengend Snippet: A : Western blot detection of mitochondrial autophagy-related protein bands in rat gastrocnemius and soleus muscles; B - D : Gray-scale analysis of PINK1, Parkin, and FNDC5 expression levels. Statistical analysis was performed using one-way ANOVA followed by Tukey’s post hoc test. Data are expressed as mean ± SD ( n = 10 per group). Group abbreviations (LD, MD, HD) are as defined in Table . * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001

Article Snippet: After closing, incubate the membrane with anti-PINK1 (1:1000, Bioss, BS-22173R), Parkin (1:1000, Bioss, BS-23687R), LC3 (1:500, Solarbio, K008014P), p62 (1:1000, Bioss, BS-55207R), FNDC5 (1:1000, Bioss, BS-8486R), Runx2 (1:1000, BS-1134R), OCN (1:500, BS-4917R), Actin (1:20,000, Bioss, BS-10966R) as the internal control.

Techniques: Western Blot, Muscles, Expressing

A : FNDC5 protein band pattern; B : FNDC5 protein expression histogram; C : FNDC5 mRNA expression; D - F : Densitometric quantification of the protein bands shown in (G)༛ G : Western blot images show the protein expression levels of PINK1, Parkin, and FNDC5 in C2C12 myotubes under different treatment conditions༛ Statistical analysis was performed using one-way ANOVA followed by Tukey’s post hoc test. Data are expressed as mean ± SD. ns (not significant), ** p < 0.01, *** p < 0.001, **** p < 0.0001

Journal: Journal of Translational Medicine

Article Title: Targeting the PINK1/Parkin-FNDC5 pathway: a novel mechanism of icariin in regulating muscle-bone metabolic coupling in osteosarcopenia

doi: 10.1186/s12967-026-08017-0

Figure Lengend Snippet: A : FNDC5 protein band pattern; B : FNDC5 protein expression histogram; C : FNDC5 mRNA expression; D - F : Densitometric quantification of the protein bands shown in (G)༛ G : Western blot images show the protein expression levels of PINK1, Parkin, and FNDC5 in C2C12 myotubes under different treatment conditions༛ Statistical analysis was performed using one-way ANOVA followed by Tukey’s post hoc test. Data are expressed as mean ± SD. ns (not significant), ** p < 0.01, *** p < 0.001, **** p < 0.0001

Article Snippet: After closing, incubate the membrane with anti-PINK1 (1:1000, Bioss, BS-22173R), Parkin (1:1000, Bioss, BS-23687R), LC3 (1:500, Solarbio, K008014P), p62 (1:1000, Bioss, BS-55207R), FNDC5 (1:1000, Bioss, BS-8486R), Runx2 (1:1000, BS-1134R), OCN (1:500, BS-4917R), Actin (1:20,000, Bioss, BS-10966R) as the internal control.

Techniques: Expressing, Western Blot

Forest plots showing FNDC5 and results from different methods. A : Usual walking speed, B : Handgrip strength (left), C : Femoral neck bone mineral density, D : Forearm bone mineral density, E : Lumbar spine bone mineral density, F : Calcaneal bone mineral density

Journal: Journal of Translational Medicine

Article Title: Targeting the PINK1/Parkin-FNDC5 pathway: a novel mechanism of icariin in regulating muscle-bone metabolic coupling in osteosarcopenia

doi: 10.1186/s12967-026-08017-0

Figure Lengend Snippet: Forest plots showing FNDC5 and results from different methods. A : Usual walking speed, B : Handgrip strength (left), C : Femoral neck bone mineral density, D : Forearm bone mineral density, E : Lumbar spine bone mineral density, F : Calcaneal bone mineral density

Article Snippet: After closing, incubate the membrane with anti-PINK1 (1:1000, Bioss, BS-22173R), Parkin (1:1000, Bioss, BS-23687R), LC3 (1:500, Solarbio, K008014P), p62 (1:1000, Bioss, BS-55207R), FNDC5 (1:1000, Bioss, BS-8486R), Runx2 (1:1000, BS-1134R), OCN (1:500, BS-4917R), Actin (1:20,000, Bioss, BS-10966R) as the internal control.

Techniques:

Co-localization analysis of eQTL data at the FNDC5 locus with GWAS data for OS. A : Daily walking speed, B : Handgrip strength (left), C : Femoral neck bone mineral density, D : Forearm bone mineral density, E : Lumbar spine bone mineral density, F : Calcaneal bone mineral density

Journal: Journal of Translational Medicine

Article Title: Targeting the PINK1/Parkin-FNDC5 pathway: a novel mechanism of icariin in regulating muscle-bone metabolic coupling in osteosarcopenia

doi: 10.1186/s12967-026-08017-0

Figure Lengend Snippet: Co-localization analysis of eQTL data at the FNDC5 locus with GWAS data for OS. A : Daily walking speed, B : Handgrip strength (left), C : Femoral neck bone mineral density, D : Forearm bone mineral density, E : Lumbar spine bone mineral density, F : Calcaneal bone mineral density

Article Snippet: After closing, incubate the membrane with anti-PINK1 (1:1000, Bioss, BS-22173R), Parkin (1:1000, Bioss, BS-23687R), LC3 (1:500, Solarbio, K008014P), p62 (1:1000, Bioss, BS-55207R), FNDC5 (1:1000, Bioss, BS-8486R), Runx2 (1:1000, BS-1134R), OCN (1:500, BS-4917R), Actin (1:20,000, Bioss, BS-10966R) as the internal control.

Techniques:

Prognostic TMET genes defining TMET subtype phenotypes. (A) Univariate Cox regression analysis of TMET crosstalk genes in CRC. (B) Heatmap of the top 28 prognostically relevant TMET genes with significant differential expression (|logFC| > 0.15). (C,D) KEGG pathway enrichment of C1- and C2-upregulated TMET genes. (E,F) Kaplan–Meier survival curves stratified by CKMT2 and PDE2A expression. (G) PCA plot showing subtype separation driven by CKMT2 and PDE2A expression. (H) ROC curves evaluating the discriminatory performance of CKMT2 and PDE2A.

Journal: Frontiers in Pharmacology

Article Title: Metabolic profiling of the TME uncovers the contrasting impacts of CKMT2 and PDE2A in CRC progression and therapeutic response

doi: 10.3389/fphar.2026.1732137

Figure Lengend Snippet: Prognostic TMET genes defining TMET subtype phenotypes. (A) Univariate Cox regression analysis of TMET crosstalk genes in CRC. (B) Heatmap of the top 28 prognostically relevant TMET genes with significant differential expression (|logFC| > 0.15). (C,D) KEGG pathway enrichment of C1- and C2-upregulated TMET genes. (E,F) Kaplan–Meier survival curves stratified by CKMT2 and PDE2A expression. (G) PCA plot showing subtype separation driven by CKMT2 and PDE2A expression. (H) ROC curves evaluating the discriminatory performance of CKMT2 and PDE2A.

Article Snippet: Membranes were blocked with 5% bovine serum albumin (BSA) in TBST buffer and incubated overnight at 4 °C with a primary antibody against CKMT2 (Proteintech, #23995-1-AP, Rabbit, 1:1,000) and PDE2A (Proteintech, #55306-1-AP,Rabbit, 1:1,000).

Techniques: Quantitative Proteomics, Expressing

Single-cell resolution of TMET gene expression. (A,B) t-SNE plots showing major cell populations in CRC single-cell datasets EMTAB8107 and GSE166555 . (C,D) Distribution of malignant, immune, and stromal lineages across datasets. (E–H) Expression patterns of C1- and C2-associated TMET genes in EMTAB8107. (I–L) Corresponding expression patterns in GSE166555 . (M–P) Dot plots showing TMET gene expression across cell types. (Q–T) t-SNE plots illustrating PDE2A and CKMT2 expression in both datasets. C1: high-risk; C2: low-risk.

Journal: Frontiers in Pharmacology

Article Title: Metabolic profiling of the TME uncovers the contrasting impacts of CKMT2 and PDE2A in CRC progression and therapeutic response

doi: 10.3389/fphar.2026.1732137

Figure Lengend Snippet: Single-cell resolution of TMET gene expression. (A,B) t-SNE plots showing major cell populations in CRC single-cell datasets EMTAB8107 and GSE166555 . (C,D) Distribution of malignant, immune, and stromal lineages across datasets. (E–H) Expression patterns of C1- and C2-associated TMET genes in EMTAB8107. (I–L) Corresponding expression patterns in GSE166555 . (M–P) Dot plots showing TMET gene expression across cell types. (Q–T) t-SNE plots illustrating PDE2A and CKMT2 expression in both datasets. C1: high-risk; C2: low-risk.

Article Snippet: Membranes were blocked with 5% bovine serum albumin (BSA) in TBST buffer and incubated overnight at 4 °C with a primary antibody against CKMT2 (Proteintech, #23995-1-AP, Rabbit, 1:1,000) and PDE2A (Proteintech, #55306-1-AP,Rabbit, 1:1,000).

Techniques: Single Cell, Gene Expression, Expressing

Risk stratification based on PDE2A and CKMT2. (A,B) Risk stratification of CRC patients using LASSO regression based on PDE2A and CKMT2 expression. (C) Bar plot illustrating the distribution of CRC patients according to risk scores derived from PDE2A and CKMT2. (D) Kaplan-Meier survival curve comparing overall survival (OS) between risk subgroups. (E) Sankey diagram mapping the distribution of CRC patients across molecular clusters, risk subgroups, PDE2A and CKMT2 expression levels, and vital status. (F) PCA plot visualizing the dimensional separation of risk subgroups. (G) PCA plot depicting the segregation of gene-based subgroups. (H) Heatmap displaying PDE2A and CKMT2 expression across risk subgroups and their associations with clinical and molecular features. (I–L) Bar plots showing the distribution of gene-based subtypes across cancer stages and molecular classifications, including MSI, CMS, and CIMP status.

Journal: Frontiers in Pharmacology

Article Title: Metabolic profiling of the TME uncovers the contrasting impacts of CKMT2 and PDE2A in CRC progression and therapeutic response

doi: 10.3389/fphar.2026.1732137

Figure Lengend Snippet: Risk stratification based on PDE2A and CKMT2. (A,B) Risk stratification of CRC patients using LASSO regression based on PDE2A and CKMT2 expression. (C) Bar plot illustrating the distribution of CRC patients according to risk scores derived from PDE2A and CKMT2. (D) Kaplan-Meier survival curve comparing overall survival (OS) between risk subgroups. (E) Sankey diagram mapping the distribution of CRC patients across molecular clusters, risk subgroups, PDE2A and CKMT2 expression levels, and vital status. (F) PCA plot visualizing the dimensional separation of risk subgroups. (G) PCA plot depicting the segregation of gene-based subgroups. (H) Heatmap displaying PDE2A and CKMT2 expression across risk subgroups and their associations with clinical and molecular features. (I–L) Bar plots showing the distribution of gene-based subtypes across cancer stages and molecular classifications, including MSI, CMS, and CIMP status.

Article Snippet: Membranes were blocked with 5% bovine serum albumin (BSA) in TBST buffer and incubated overnight at 4 °C with a primary antibody against CKMT2 (Proteintech, #23995-1-AP, Rabbit, 1:1,000) and PDE2A (Proteintech, #55306-1-AP,Rabbit, 1:1,000).

Techniques: Expressing, Derivative Assay

Validation of PDE2A and CKMT2 in colorectal cancer. (A,C) GEPIA-based comparison of CKMT2 and PDE2A mRNA expression in normal and CRC tissues. (B,D) qPCR validation in FHC, SW480, and HCT116 cell lines. (E–H) Western blot analysis and quantification of CKMT2 and PDE2A protein expression. (I,J) Representative IHC images and quantitative comparison in CRC and adjacent normal tissues (n = 63). (K) Heatmap of protein expression in relation to clinicopathological features. (L,M) Representative IHC images and Pearson correlation analysis showing inverse expression of CKMT2 and PDE2A.

Journal: Frontiers in Pharmacology

Article Title: Metabolic profiling of the TME uncovers the contrasting impacts of CKMT2 and PDE2A in CRC progression and therapeutic response

doi: 10.3389/fphar.2026.1732137

Figure Lengend Snippet: Validation of PDE2A and CKMT2 in colorectal cancer. (A,C) GEPIA-based comparison of CKMT2 and PDE2A mRNA expression in normal and CRC tissues. (B,D) qPCR validation in FHC, SW480, and HCT116 cell lines. (E–H) Western blot analysis and quantification of CKMT2 and PDE2A protein expression. (I,J) Representative IHC images and quantitative comparison in CRC and adjacent normal tissues (n = 63). (K) Heatmap of protein expression in relation to clinicopathological features. (L,M) Representative IHC images and Pearson correlation analysis showing inverse expression of CKMT2 and PDE2A.

Article Snippet: Membranes were blocked with 5% bovine serum albumin (BSA) in TBST buffer and incubated overnight at 4 °C with a primary antibody against CKMT2 (Proteintech, #23995-1-AP, Rabbit, 1:1,000) and PDE2A (Proteintech, #55306-1-AP,Rabbit, 1:1,000).

Techniques: Biomarker Discovery, Comparison, Expressing, Western Blot

External validation of the prognostic impact of PDE2A and CKMT2. (A,B) Kaplan-Meier survival curves illustrating differences in recurrence-free survival (RFS, n = 1,336) and overall survival (OS, n = 1,061) between CRC patients with high and low PDE2A expression. (C,D) Kaplan-Meier survival curves depicting differences in recurrence-free survival (RFS, n = 1,336) and overall survival (OS, n = 1,061) between CRC patients with high and low CKMT2 expression.

Journal: Frontiers in Pharmacology

Article Title: Metabolic profiling of the TME uncovers the contrasting impacts of CKMT2 and PDE2A in CRC progression and therapeutic response

doi: 10.3389/fphar.2026.1732137

Figure Lengend Snippet: External validation of the prognostic impact of PDE2A and CKMT2. (A,B) Kaplan-Meier survival curves illustrating differences in recurrence-free survival (RFS, n = 1,336) and overall survival (OS, n = 1,061) between CRC patients with high and low PDE2A expression. (C,D) Kaplan-Meier survival curves depicting differences in recurrence-free survival (RFS, n = 1,336) and overall survival (OS, n = 1,061) between CRC patients with high and low CKMT2 expression.

Article Snippet: Membranes were blocked with 5% bovine serum albumin (BSA) in TBST buffer and incubated overnight at 4 °C with a primary antibody against CKMT2 (Proteintech, #23995-1-AP, Rabbit, 1:1,000) and PDE2A (Proteintech, #55306-1-AP,Rabbit, 1:1,000).

Techniques: Biomarker Discovery, Expressing

Impact of PDE2A and CKMT2 on immunotherapy response. (A,B) Kaplan-Meier survival curves showing overall survival (OS, n = 1,061) differences between patients with high and low PDE2A and CKMT2 expression. (C,D) Box plot and ROC curves comparing PDE2A expression between immunotherapy responders (n = 355) and non-responders (n = 570). (E,F) Box plot and ROC curves comparing CKMT2 expression between immunotherapy responders (n = 355) and non-responders (n = 570).

Journal: Frontiers in Pharmacology

Article Title: Metabolic profiling of the TME uncovers the contrasting impacts of CKMT2 and PDE2A in CRC progression and therapeutic response

doi: 10.3389/fphar.2026.1732137

Figure Lengend Snippet: Impact of PDE2A and CKMT2 on immunotherapy response. (A,B) Kaplan-Meier survival curves showing overall survival (OS, n = 1,061) differences between patients with high and low PDE2A and CKMT2 expression. (C,D) Box plot and ROC curves comparing PDE2A expression between immunotherapy responders (n = 355) and non-responders (n = 570). (E,F) Box plot and ROC curves comparing CKMT2 expression between immunotherapy responders (n = 355) and non-responders (n = 570).

Article Snippet: Membranes were blocked with 5% bovine serum albumin (BSA) in TBST buffer and incubated overnight at 4 °C with a primary antibody against CKMT2 (Proteintech, #23995-1-AP, Rabbit, 1:1,000) and PDE2A (Proteintech, #55306-1-AP,Rabbit, 1:1,000).

Techniques: Expressing

Prognostic TMET genes defining TMET subtype phenotypes. (A) Univariate Cox regression analysis of TMET crosstalk genes in CRC. (B) Heatmap of the top 28 prognostically relevant TMET genes with significant differential expression (|logFC| > 0.15). (C,D) KEGG pathway enrichment of C1- and C2-upregulated TMET genes. (E,F) Kaplan–Meier survival curves stratified by CKMT2 and PDE2A expression. (G) PCA plot showing subtype separation driven by CKMT2 and PDE2A expression. (H) ROC curves evaluating the discriminatory performance of CKMT2 and PDE2A.

Journal: Frontiers in Pharmacology

Article Title: Metabolic profiling of the TME uncovers the contrasting impacts of CKMT2 and PDE2A in CRC progression and therapeutic response

doi: 10.3389/fphar.2026.1732137

Figure Lengend Snippet: Prognostic TMET genes defining TMET subtype phenotypes. (A) Univariate Cox regression analysis of TMET crosstalk genes in CRC. (B) Heatmap of the top 28 prognostically relevant TMET genes with significant differential expression (|logFC| > 0.15). (C,D) KEGG pathway enrichment of C1- and C2-upregulated TMET genes. (E,F) Kaplan–Meier survival curves stratified by CKMT2 and PDE2A expression. (G) PCA plot showing subtype separation driven by CKMT2 and PDE2A expression. (H) ROC curves evaluating the discriminatory performance of CKMT2 and PDE2A.

Article Snippet: Membranes were blocked with 5% bovine serum albumin (BSA) in TBST buffer and incubated overnight at 4 °C with a primary antibody against CKMT2 (Proteintech, #23995-1-AP, Rabbit, 1:1,000) and PDE2A (Proteintech, #55306-1-AP,Rabbit, 1:1,000).

Techniques: Quantitative Proteomics, Expressing

Single-cell resolution of TMET gene expression. (A,B) t-SNE plots showing major cell populations in CRC single-cell datasets EMTAB8107 and GSE166555 . (C,D) Distribution of malignant, immune, and stromal lineages across datasets. (E–H) Expression patterns of C1- and C2-associated TMET genes in EMTAB8107. (I–L) Corresponding expression patterns in GSE166555 . (M–P) Dot plots showing TMET gene expression across cell types. (Q–T) t-SNE plots illustrating PDE2A and CKMT2 expression in both datasets. C1: high-risk; C2: low-risk.

Journal: Frontiers in Pharmacology

Article Title: Metabolic profiling of the TME uncovers the contrasting impacts of CKMT2 and PDE2A in CRC progression and therapeutic response

doi: 10.3389/fphar.2026.1732137

Figure Lengend Snippet: Single-cell resolution of TMET gene expression. (A,B) t-SNE plots showing major cell populations in CRC single-cell datasets EMTAB8107 and GSE166555 . (C,D) Distribution of malignant, immune, and stromal lineages across datasets. (E–H) Expression patterns of C1- and C2-associated TMET genes in EMTAB8107. (I–L) Corresponding expression patterns in GSE166555 . (M–P) Dot plots showing TMET gene expression across cell types. (Q–T) t-SNE plots illustrating PDE2A and CKMT2 expression in both datasets. C1: high-risk; C2: low-risk.

Article Snippet: Membranes were blocked with 5% bovine serum albumin (BSA) in TBST buffer and incubated overnight at 4 °C with a primary antibody against CKMT2 (Proteintech, #23995-1-AP, Rabbit, 1:1,000) and PDE2A (Proteintech, #55306-1-AP,Rabbit, 1:1,000).

Techniques: Single Cell, Gene Expression, Expressing

Risk stratification based on PDE2A and CKMT2. (A,B) Risk stratification of CRC patients using LASSO regression based on PDE2A and CKMT2 expression. (C) Bar plot illustrating the distribution of CRC patients according to risk scores derived from PDE2A and CKMT2. (D) Kaplan-Meier survival curve comparing overall survival (OS) between risk subgroups. (E) Sankey diagram mapping the distribution of CRC patients across molecular clusters, risk subgroups, PDE2A and CKMT2 expression levels, and vital status. (F) PCA plot visualizing the dimensional separation of risk subgroups. (G) PCA plot depicting the segregation of gene-based subgroups. (H) Heatmap displaying PDE2A and CKMT2 expression across risk subgroups and their associations with clinical and molecular features. (I–L) Bar plots showing the distribution of gene-based subtypes across cancer stages and molecular classifications, including MSI, CMS, and CIMP status.

Journal: Frontiers in Pharmacology

Article Title: Metabolic profiling of the TME uncovers the contrasting impacts of CKMT2 and PDE2A in CRC progression and therapeutic response

doi: 10.3389/fphar.2026.1732137

Figure Lengend Snippet: Risk stratification based on PDE2A and CKMT2. (A,B) Risk stratification of CRC patients using LASSO regression based on PDE2A and CKMT2 expression. (C) Bar plot illustrating the distribution of CRC patients according to risk scores derived from PDE2A and CKMT2. (D) Kaplan-Meier survival curve comparing overall survival (OS) between risk subgroups. (E) Sankey diagram mapping the distribution of CRC patients across molecular clusters, risk subgroups, PDE2A and CKMT2 expression levels, and vital status. (F) PCA plot visualizing the dimensional separation of risk subgroups. (G) PCA plot depicting the segregation of gene-based subgroups. (H) Heatmap displaying PDE2A and CKMT2 expression across risk subgroups and their associations with clinical and molecular features. (I–L) Bar plots showing the distribution of gene-based subtypes across cancer stages and molecular classifications, including MSI, CMS, and CIMP status.

Article Snippet: Membranes were blocked with 5% bovine serum albumin (BSA) in TBST buffer and incubated overnight at 4 °C with a primary antibody against CKMT2 (Proteintech, #23995-1-AP, Rabbit, 1:1,000) and PDE2A (Proteintech, #55306-1-AP,Rabbit, 1:1,000).

Techniques: Expressing, Derivative Assay

Validation of PDE2A and CKMT2 in colorectal cancer. (A,C) GEPIA-based comparison of CKMT2 and PDE2A mRNA expression in normal and CRC tissues. (B,D) qPCR validation in FHC, SW480, and HCT116 cell lines. (E–H) Western blot analysis and quantification of CKMT2 and PDE2A protein expression. (I,J) Representative IHC images and quantitative comparison in CRC and adjacent normal tissues (n = 63). (K) Heatmap of protein expression in relation to clinicopathological features. (L,M) Representative IHC images and Pearson correlation analysis showing inverse expression of CKMT2 and PDE2A.

Journal: Frontiers in Pharmacology

Article Title: Metabolic profiling of the TME uncovers the contrasting impacts of CKMT2 and PDE2A in CRC progression and therapeutic response

doi: 10.3389/fphar.2026.1732137

Figure Lengend Snippet: Validation of PDE2A and CKMT2 in colorectal cancer. (A,C) GEPIA-based comparison of CKMT2 and PDE2A mRNA expression in normal and CRC tissues. (B,D) qPCR validation in FHC, SW480, and HCT116 cell lines. (E–H) Western blot analysis and quantification of CKMT2 and PDE2A protein expression. (I,J) Representative IHC images and quantitative comparison in CRC and adjacent normal tissues (n = 63). (K) Heatmap of protein expression in relation to clinicopathological features. (L,M) Representative IHC images and Pearson correlation analysis showing inverse expression of CKMT2 and PDE2A.

Article Snippet: Membranes were blocked with 5% bovine serum albumin (BSA) in TBST buffer and incubated overnight at 4 °C with a primary antibody against CKMT2 (Proteintech, #23995-1-AP, Rabbit, 1:1,000) and PDE2A (Proteintech, #55306-1-AP,Rabbit, 1:1,000).

Techniques: Biomarker Discovery, Comparison, Expressing, Western Blot

External validation of the prognostic impact of PDE2A and CKMT2. (A,B) Kaplan-Meier survival curves illustrating differences in recurrence-free survival (RFS, n = 1,336) and overall survival (OS, n = 1,061) between CRC patients with high and low PDE2A expression. (C,D) Kaplan-Meier survival curves depicting differences in recurrence-free survival (RFS, n = 1,336) and overall survival (OS, n = 1,061) between CRC patients with high and low CKMT2 expression.

Journal: Frontiers in Pharmacology

Article Title: Metabolic profiling of the TME uncovers the contrasting impacts of CKMT2 and PDE2A in CRC progression and therapeutic response

doi: 10.3389/fphar.2026.1732137

Figure Lengend Snippet: External validation of the prognostic impact of PDE2A and CKMT2. (A,B) Kaplan-Meier survival curves illustrating differences in recurrence-free survival (RFS, n = 1,336) and overall survival (OS, n = 1,061) between CRC patients with high and low PDE2A expression. (C,D) Kaplan-Meier survival curves depicting differences in recurrence-free survival (RFS, n = 1,336) and overall survival (OS, n = 1,061) between CRC patients with high and low CKMT2 expression.

Article Snippet: Membranes were blocked with 5% bovine serum albumin (BSA) in TBST buffer and incubated overnight at 4 °C with a primary antibody against CKMT2 (Proteintech, #23995-1-AP, Rabbit, 1:1,000) and PDE2A (Proteintech, #55306-1-AP,Rabbit, 1:1,000).

Techniques: Biomarker Discovery, Expressing

Impact of PDE2A and CKMT2 on immunotherapy response. (A,B) Kaplan-Meier survival curves showing overall survival (OS, n = 1,061) differences between patients with high and low PDE2A and CKMT2 expression. (C,D) Box plot and ROC curves comparing PDE2A expression between immunotherapy responders (n = 355) and non-responders (n = 570). (E,F) Box plot and ROC curves comparing CKMT2 expression between immunotherapy responders (n = 355) and non-responders (n = 570).

Journal: Frontiers in Pharmacology

Article Title: Metabolic profiling of the TME uncovers the contrasting impacts of CKMT2 and PDE2A in CRC progression and therapeutic response

doi: 10.3389/fphar.2026.1732137

Figure Lengend Snippet: Impact of PDE2A and CKMT2 on immunotherapy response. (A,B) Kaplan-Meier survival curves showing overall survival (OS, n = 1,061) differences between patients with high and low PDE2A and CKMT2 expression. (C,D) Box plot and ROC curves comparing PDE2A expression between immunotherapy responders (n = 355) and non-responders (n = 570). (E,F) Box plot and ROC curves comparing CKMT2 expression between immunotherapy responders (n = 355) and non-responders (n = 570).

Article Snippet: Membranes were blocked with 5% bovine serum albumin (BSA) in TBST buffer and incubated overnight at 4 °C with a primary antibody against CKMT2 (Proteintech, #23995-1-AP, Rabbit, 1:1,000) and PDE2A (Proteintech, #55306-1-AP,Rabbit, 1:1,000).

Techniques: Expressing