Junjun Ma

Colorectal cancer (CRC) metastasis requires coordination between tumor-intrinsic programs and the surrounding microenvironment, yet how proteasomal regulation intersects with the epitranscriptome in this process remains unclear. Here, analyses of TCGA, GEO, and an institutional cohort of 146 CRC patients identified PSMC5 upregulation as associated with metastatic progression and poor prognosis. Mechanistically, PSMC5 promoted SMURF1-dependent K11-linked ubiquitination of METTL14 at K263, leading to METTL14 destabilization, global m⁶A remodeling, and activation of EMT-associated malignant phenotypes. Rescue experiments further supported METTL14 as a functional downstream effector of PSMC5. Integrative single-cell, spatial transcriptomic, and multiplex immunofluorescence analyses showed that PSMC5-high epithelial states were associated with spatially organized “regulatory islands,” defined here as PSMC5-high epithelial nests with peripheral Treg and M2 enrichment together with relative CD8⁺ T-cell exclusion. In vivo, SMURF1 silencing restored METTL14 expression and attenuated PSMC5-driven tumor growth and lung metastasis. Collectively, these findings define a PSMC5/SMURF1/METTL14 axis that links proteasomal regulation to epitranscriptomic remodeling and metastatic progression in CRC, and identify this pathway as a candidate therapeutically actionable axis.

Background The safety and efficacy of neoadjuvant FLOT (Fluorouracil, Leucovorin, Oxaliplatin, Docetaxel) and DOS (Docetaxel, Oxaliplatin, S-1) regimens for locally advanced gastric cancer (LAGC) have not been compared. Methods Patients with histologically confirmed LAGC (stage ≥ cT3 or cN + , no metastasis) treated between 2017–2021 were retrospectively included and propensity-matched into FLOT (4 cycles, n = 72) and DOS (3 cycles, n = 72) groups. Outcomes included RECIST response, grade 3/4 adverse events, surgical/pathological results, and R0 resection rates, and long-term survival (overall survival [OS] and progression-free survival [PFS]). Results RECIST response rates were 41.7% (FLOT) vs. 47.2% (DOS); R0 resection rates were 63.9% vs. 72.2%. No significant differences were observed in operative time, blood loss, hospital stay, histopathological regression (TRG1a: 2.8% vs. 8.3%; TRG1b: 13.9% vs. 16.7%), postoperative morbidity (29.8% vs. 24.5%), or grade 3/4 toxicity (20.8% vs. 13.9%). The 5-year OS rates were 42.7% and 50.4% (p = 0.652), and the PFS rates were 33.7% and 41.4% (p = 0.548) for the FLOT and DOS groups, respectively. Conclusion DOS demonstrated no significant but favorable feasibility, safety, and efficacy compared to FLOT in LAGC. Shorter hospital stay with DOS may enhance patient comfort and reduce healthcare burden.

Metastasis is the leading cause of death in colorectal cancer (CRC) patients. By mediating intercellular communication, exosomes exhibit considerable value in regulating tumor metastasis. Long non-coding RNAs (lncRNAs) are abundant in exosomes and participate in regulating tumor progression. However, it is poorly understood how the cancer-secreted exosomal lncRNAs affect CRC proliferation and metastasis. Here, by analyzing the public databases we identified a lncRNA SNHG3 and demonstrated that SNHG3 was delivered through CRC cells-derived exosomes to promote metastasis in CRC. Mechanistically, exosomal SNHG3 was internalized by CRC cells and afterward upregulated the expression of β-catenin by facilitating the intranuclear transport of hnRNPC. Consequently, the RNA stability of β-catenin was enhanced which led to the activation of EMT and metastasis of CRC cells. Our findings expand the oncogenic mechanisms of exosomal SNHG3 and identify it as a diagnostic marker for CRC.

Multiple linear stapler firings is a risk factor for anastomotic leakage (AL) in laparoscopic low anterior resection (LAR) using double stapling technique (DST) anastomosis. In this study, our objective was to establish the risk factors for ≥ 3 linear stapler firings, and to create and validate a predictive model for ≥ 3 linear stapler firings in laparoscopic LAR using DST anastomosis. We retrospectively enrolled 328 mid–low rectal cancer patients undergoing laparoscopic LAR using DST anastomosis. With a split ratio of 4:1, patients were randomly divided into 2 sets: the training set (n = 260) and the testing set (n = 68). A clinical predictive model of ≥ 3 linear stapler firings was constructed by binary logistic regression. Based on three-dimensional convolutional networks, we built an image model using only magnetic resonance (MR) images segmented by Mask region-based convolutional neural network, and an integrated model based on both MR images and clinical variables. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and Youden index were calculated for each model. And the three models were validated by an independent cohort of 128 patients. There were 17.7% (58/328) patients received ≥ 3 linear stapler firings. Tumor size ≥ 5 cm (odds ratio (OR) = 2.54, 95% confidence interval (CI) = 1.15–5.60, p = 0.021) and preoperative carcinoma embryonic antigen (CEA) level > 5 ng/mL [OR = 2.20, 95% CI = 1.20–4.04, p = 0.011] were independent risk factors associated with ≥ 3 linear stapler firings. The integrated model (AUC = 0.88, accuracy = 94.1%) performed better on predicting ≥ 3 linear stapler firings than the clinical model (AUC = 0.72, accuracy = 86.7%) and the image model (AUC = 0.81, accuracy = 91.2%). Similarly, in the validation set, the integrated model (AUC = 0.84, accuracy = 93.8%) performed better than the clinical model (AUC = 0.65, accuracy = 65.6%) and the image model (AUC = 0.75, accuracy = 92.1%). Our deep-learning model based on pelvic MR can help predict the high-risk population with ≥ 3 linear stapler firings in laparoscopic LAR using DST anastomosis. This model might assist in determining preoperatively the anastomotic technique for mid–low rectal cancer patients.

Introduction: Metabolic dysregulation is a widely acknowledged contributor for the development and tumorigenesis of colorectal cancer (CRC), highlighting the need for reliable prognostic biomarkers in this malignancy.Methods: Herein, we identified key genes relevant to CRC metabolism through a comprehensive analysis of lactate metabolism-related genes from GSEA MsigDB, employing univariate Cox regression analysis and random forest algorithms. Clinical prognostic analysis was performed following identification of three key genes, and consistent clustering enabled the classification of public datasets into three patterns with significant prognostic differences. The molecular pathways and tumor microenvironment (TME) of these patterns were then investigated through correlation analyses. Quantitative PCR was employed to quantify the mRNA expression levels of the three pivotal genes in CRC tissue. Single-cell RNA sequencing data and fluorescent multiplex immunohistochemistry were utilized to analyze relevant T cells and validate the correlation between key genes and CD4⁺ T cells.Results: Our analysis revealed that MPC1, COQ2, and ADAMTS13 significantly stratify the cohort into three patterns with distinct prognoses. Additionally, the immune infiltration and molecular pathways were significantly different for each pattern. Among the key genes, MPC1 and COQ2 were positively associated with good prognosis, whereas ADAMTS13 was negatively associated with good prognosis. Single-cell RNA sequencing (scRNA-seq) data illustrated that the relationship between three key genes and T cells, which was further confirmed by the results of fluorescent multiplex immunohistochemistry demonstrating a positive correlation between MPC1 and COQ2 with CD4⁺ T cells and a negative correlation between ADAMTS13 and CD4⁺ T cells.Discussion: These findings suggest that the three key lactate metabolism genes, MPC1, COQ2, and ADAMTS13, may serve as effective prognostic biomarkers and support the link between lactate metabolism and the immune microenvironment in CRC.

Background: Emerging studies have shown that pyroptosis plays a non-negligible role in the development and treatment of tumors. However, the mechanism of pyroptosis in colorectal cancer (CRC) remains still unclear. Therefore, this study investigated the role of pyroptosis in CRC. Methods: A pyroptosis-related risk model was developed using univariate Cox regression and LASSO Cox regression analyses. Based on this model, pyroptosis-related risk scores (PRS) of CRC samples with OS time > 0 from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database were calculated. The abundance of immune cells in CRC tumor microenvironment (TME) was predicted by single-sample gene-set enrichment analysis (ssGSEA). Then, the responses to chemotherapy and immunotherapy were predicted by pRRophetic algorithm, the tumor immune dysfunction and exclusion (TIDE) and SubMap algorithms, respectively. Moreover, the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing dataset (PRISM) were used to explore novel drug treatment strategies of CRC. Finally, we investigated pyroptosis-related genes in the level of single-cell and validated the expression levels of these genes between normal and CRC cell lines by RT-qPCR. Results: Survival analysis showed that CRC samples with low PRS had better overall survival (OS) and progression-free survival (PFS). CRC samples with low PRS had higher immune-related gene expression and immune cell infiltration than those with high PRS. Besides, CRC samples with low PRS were more likely to benefit from 5-fluorouracil based chemotherapy and anti-PD-1 immunotherapy. In novel drug prediction, some compounds such as C6-ceramide and noretynodrel, were inferred as potential drugs for CRC with different PRS. Single-cell analysis revealed pyroptosis-related genes were highly expressed in tumor cells. RT-qPCR also demonstrated different expression levels of these genes between normal and CRC cell lines. Conclusions: Taken together, this study provides a comprehensive investigation of the role of pyroptosis in CRC at the bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) levels, advances our understanding of CRC characteristics, and guides more effective treatment regimens. Graphical

Background Multiple linear stapler firings is a risk factor for anastomotic leakage (AL) in laparoscopic low anterior resection (LAR) using double stapling technique (DST) anastomosis. In this study, our objective was to establish the risk factors for ≥3 linear stapler firings, and to create and validate a predictive model for ≥3 linear stapler firings in laparoscopic LAR using DST anastomosis. Methods We retrospectively enrolled 328 mid–low rectal cancer patients undergoing laparoscopic LAR using DST anastomosis. With a split ratio of 4:1, patients were randomly divided into 2 sets: the training set (n = 260) and the testing set (n = 68). A clinical predictive model of ≥3 linear stapler firings was constructed by binary logistic regression. Based on three-dimensional convolutional networks, we built an image model using only magnetic resonance (MR) images segmented by Mask region-based convolutional neural network, and an integrated model based on both MR images and clinical variables. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and Youden index were calculated for each model. And the three models were externally validated by an independent cohort of 128 patients. Results There were 17.7% (58/328) patients received ≥3 linear stapler firings. Tumor size ≥5 cm (odds ratio (OR)=2.54, 95% confidence interval (CI)=1.15–5.60, p=0.021) and preoperative carcinoma embryonic antigen (CEA) level >5 ng/mL [OR=2.20, 95% CI=1.20–4.04, p=0.011] were independent risk factors associated with ≥3 linear stapler firings. The integrated model (AUC=0.88, accuracy=94.1%) performed better on predicting ≥3 linear stapler firings than the clinical model (AUC =0.72, accuracy=86.7%) and the image model (AUC=0.81, accuracy=91.2%). Similarly, in the validation set, the integrated model (AUC=0.84, accuracy=93.8%) performed better than the clinical model (AUC =0.65, accuracy=65.6%) and the image model (AUC=0.75, accuracy=92.1%). Conclusion Our deep-learning model based on pelvic MR can help predict the high-risk population with ≥3 linear stapler firings in laparoscopic LAR using DST anastomosis. This model might assist in determining preoperatively the anastomotic technique for mid–low rectal cancer patients.

Magnetic resonance imaging-based deep learning model to predict multiple firings in double-stapled colorectal anastomosis