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Pulmonary infection is one of the most common postoperative complications after radical gastrectomy for gastric cancer (GC) and is associated with a poorer prognosis. This study aimed to investigate potential predictive factors for pulmonary infection in elderly GC patients. This study retrospectively enrolled 346 elderly GC patients undergoing elective radical gastrectomy between January 2017 and December 2020. Pulmonary infection within postoperative 30 days was set as the primary observational endpoint. The baseline demographic, clinicopathological, and laboratory data were compared between patients with or without pulmonary infection. ROC curves were plotted to evaluate the cut-off and predictive values of factors. Binary univariate and multivariate logistic regression analyses were employed to determine risk factors for postoperative pulmonary infection. Of the enrolled 346 patients, pulmonary infection was observed in 51 patients within postoperative 30 days, with an incidence of 14.7%. mFI was a significant predictor for pulmonary infection by ROC curve analysis (AUC: 0.770, P < 0.001). Moreover, preoperative mFI was the only independent risk factor for pulmonary infection (OR: 2.72, 95% CI: 2.02-3.31, P = 0.011) by univariate and multivariate logistic regression analyses. Our study indicates that mFI independently predicts pulmonary infection in elderly GC patients.

Background KRAS gene is the most common type of mutation reported in colorectal cancer (CRC). KRAS mutation-mediated regulation of immunophenotype and immune pathways in CRC remains to be elucidated. Methods 535 CRC patients were used to compare the expression of immune-related genes (IRGs) and the abundance of tumor-infiltrating immune cells (TIICs) in the tumor microenvironment between KRAS -mutant and KRAS wild-type CRC patients. An independent dataset included 566 cases of CRC and an in-house RNA sequencing dataset were served as validation sets. An in-house dataset consisting of 335 CRC patients were used to analyze systemic immune and inflammatory state in the presence of KRAS mutation. An immue risk (Imm-R) model consist of IRG and TIICs for prognostic prediction in KRAS -mutant CRC patients was established and validated. Results NF-κB and T-cell receptor signaling pathways were significantly inhibited in KRAS -mutant CRC patients. Regulatory T cells (Tregs) was increased while macrophage M1 and activated CD4 memory T cell was decreased in KRAS -mutant CRC. Prognosis correlated with enhanced Tregs, macrophage M1 and activated CD4 memory T cell and was validated. Serum levels of hypersensitive C-reactive protein (hs-CRP), CRP, and IgM were significantly decreased in KRAS -mutant compared to KRAS wild-type CRC patients. An immune risk model composed of VGF, RLN3, CT45A1 and TIICs signature classified CRC patients with distinct clinical outcomes. Conclusions KRAS mutation in CRC was associated with suppressed immune pathways and immune infiltration. The aberrant immune pathways and immune cells help to understand the tumor immune microenvironments in KRAS -mutant CRC patients.

Background Immune checkpoint inhibitors have traditionally been understood to exert their effects primarily within the tumor microenvironment, particularly targeting CD8 + T cells. However, recent studies have highlighted a pivotal role of tumor-draining lymph nodes in mediating responses to immune checkpoint inhibitor therapy. This study aimed to elucidate the specific mechanisms by which tumor-draining lymph nodes respond to immune checkpoint inhibitor therapy and regulate the tumor microenvironment in human colorectal cancer. Methods We performed single-cell RNA sequencing and T cell receptor sequencing on tumor-draining lymph nodes and tumor tissues from patients with colorectal cancer. Through in-depth analysis of the single-cell data, we established the connection between TDLNs and tumor, and explored the impact of immune checkpoint inhibitor therapy on the immune microenvironment of tumor-draining lymph nodes. In addition, we conducted animal experiments to validate these findings. Results Our findings revealed that immune checkpoint inhibitor treatment induced the expansion of tumor-specific CD8 + effector memory T cells within tumor-draining lymph nodes, which may serve as a source for the progenitor-exhausted CD8 + T cells in the tumor microenvironment. Moreover, conventional dendritic cells type 1 and macrophages within tumor-draining lymph nodes facilitated this process. We also observed that immune checkpoint inhibitor therapy promoted the expansion of tumor-specific CD4 + follicular helper T cells in tumor-draining lymph nodes, which may explain the increase of CD4 + follicular helper T cells in the tumor microenvironment after immune checkpoint inhibitor therapy. These hypotheses were corroborated through experiments in mice. Conclusions Our findings delineate the critical regulatory function of tumor-draining lymph nodes in modulating the tumor microenvironment during Immune checkpoint inhibitor therapy.

Lymph nodes (LNs) play a pivotal role in colorectal cancer (CRC) progression and immunity, yet their molecular and functional diversity remains poorly understood. By analyzing 630 LNs and 88 primary tumors from 200 CRC patients across four independent cohorts using bulk and single-cell RNA sequencing, we identify four non-metastatic negative LNs (NLN) subtypes (NLN_C1-C4) exhibiting obviously different immune function and stromal expansion. NLN_C3/C4 are characterized by diminished T and B cell activity and fibroblast-driven fibrosis, with follicular dendritic cell loss contributing to B cell dysfunction. Immune checkpoint inhibitors partially reverse these effects, restoring FDC and B cell activity. LNs subtypes demonstrate heterogeneity across patients and within individuals, with higher NLN_C3/C4 proportions associated with advanced tumor stages, poorer survival, and recurrence. Here, we report LNs subtypes as critical manifestations of LN heterogeneity in CRC, providing a basis for improved clinical stratification and LN-targeted therapeutic strategies. Lymph nodes are primarily categorised into metastatic and non-metastatic. Here, the authors perform integrated bulk and single-cell RNA sequencing on lymph nodes from 200 patients with colorectal cancer and identify 4 non-metastatic lymph node subtypes.

Purpose: Vascular invasion (VI) is associated with recurrence and is an indicator of poor prognosis in gastric cancer (GC). Pre-operative identification of VI may guide the selection of the optimal surgical approach and assess the requirement for neoadjuvant therapy. Methods: A total of 271 patients were retrospectively collected and randomly allocated into the training and validation datasets. The least absolute shrinkage and selection operator regression model was used to select potentially relevant features, and multivariable logistic regression analysis was used to develop the nomogram. Results: The nomogram consisted of pre-operative serum complement C3 levels, duration of symptoms, pre-operative computed tomography stage, abdominal distension and undifferentiated carcinoma. The nomogram provided good calibration for both the training and the validation set, with area under the curve values of 0.792 and 0.774. Decision curve analysis revealed that the nomogram was clinically useful. Conclusion: The present study constructed a nomogram for the pre-operative prediction of VI in patients with GC. The nomogram may aid the identification of high-risk patients and aid the optimization of pre-operative decision-making.

Background Deep insights into the metabolic remodelling effects on the immune microenvironment of oesophageal squamous cell carcinoma (ESCC) are crucial for advancing precision immunotherapies and targeted therapies. This study aimed to provide novel insights into the molecular landscape of ESCC and identify clinically actionable targets associated with immunosuppression driven by metabolic changes. Methods We performed metabolomic and proteomic analyses combined with previous genomic and transcriptomic data, identified multi-omics-linked molecular features, and constructed metabolic-immune interaction-based ESCC classifiers in a discovery cohort and an independent validation cohort. We further verified the molecular characteristics and related mechanisms of ESCC subtypes. Results Our integrated multi-omics analysis revealed dysregulated proteins and metabolic imbalances characterizing ESCC, with significant alterations in metabolites and proteins linked to genetic traits. Importantly, ESCC patients were stratified into three subtypes (S1, S2, and S3) on the basis of integrated metabolomic and proteomic data. A robust subtype prediction model was developed and validated across two independent cohorts. Notably, patients classified under the poorest prognosis subtype (S3 subtype) exhibited a significant immunosuppressive microenvironment. We identified key metabolism-related biomarkers for the S3 subtype, specifically creatine and hexokinase 3 ( HK3 ). Creatine accumulation and HK3 protein deficiency synergistically reprogrammed macrophage metabolism, driving M2-like TAM polarization. This metabolic shift fostered an immunosuppressive microenvironment that accelerated tumour progression. These results highlight the potential of targeting creatine metabolism to improve the efficacy of immunotherapy and targeted therapy for ESCC. Conclusions Our analysis reveals molecular variation in multi-omics linkages and identifies targets that reverse the immunosuppressive microenvironment through metabolic remodelling improving immunotherapy and targeted therapy for ESCC.

Background and Purpose: Systemic inflammation and nutritional status have been shown to be associated with the prognosis of colorectal cancer. The purpose of this study was to evaluate the impact of the serum C-reactive protein-to-body mass index ratio on the prognosis of patients with curatively resected colorectal cancer. Methods: We conducted a retrospective analysis of a database of 2,471 eligible patients with colorectal cancer who underwent curative resection at our hospital between 2004 and 2019. The optimal cut-off for CPR-to-BMI ratio was determined using maximally selected rank statistics. Patients were divided into 2 groups according to the cut-off value of the serum C-reactive protein-to-body mass index ratio. Kaplan-Meier curves and Cox regression analysis were used to compare overall survival. A two-sided P-value < 0.05 was considered statistically significant. Results: The proportion of patients with a high C-reactive protein-to-body mass index ratio increased with increasing age, male sex, right-sided colon cancer, poorly differentiated tumors, advanced-stage disease, local/distant metastases, tumor–node–metastasis stage, and microsatellite instability. In subgroup analysis according to tumor–node–metastasis stage, the overall survival of the high C-reactive protein-to-body mass index ratio group was significantly shorter than that of the low C-reactive protein-to-body mass index ratio group (P < 0.001). Multivariate analysis identified age, differentiation, tumor–node–metastasis stage, carcinoembryonic antigen level, and the C-reactive protein-to-body mass index ratio as independent poor prognostic factors for overall survival. Conclusions: The C-reactive protein-to-body mass index ratio predicts the prognosis of patients with curatively resected colorectal cancer and is an independent risk factor for overall survival in patients with colorectal cancer.

While precision medicine driven by genome sequencing has revolutionized cancer care, such as lung cancer, its impact on gastric cancer (GC) has been minimal. GC patients are routinely treated with chemotherapy, but only a fraction of them receive the clinical benefit. There is an urgent need to develop biomarkers or algorithms to select chemo-sensitive patients or apply targeted therapy. Here, we carried out retrospective analyses of 1,020 formalin-fixed, paraffin-embedded GC surgical resection samples from 5 hospitals and developed a mass spectrometry-based workflow for proteomic subtyping of GC. We identified two proteomic subtypes: the chemo-sensitive group (CSG) and the chemo-insensitive group (CIG) in the discovery set. The 5-year overall survival of CSG was significantly improved in patients who had received adjuvant chemotherapy after surgery compared with those who received surgery only (64.2% vs. 49.6%; Cox P -value=0.002), whereas no such improvement was observed in CIG (50.0% vs. 58.6%; Cox P -value=0.495). We validated these results in an independent validation set. Further, differential proteome analysis uncovered 9 FDA-approved drugs that may be applicable for targeted therapy of GC. A prospective study is warranted to test these findings for future GC patient care.

Using artificial intelligence (AI) to prescribe drugs has advanced slowly. Whether a “doctor-in-the-loop” design would increase acceptance of drug-prescribing AI is unknown, as are settings where physicians envision AI-driven drug prescription most likely to be implemented. We surveyed a stratified sample of 2708 physicians throughout China to interrogate their opinions on drug-prescribing AI. Most respondents (78%) are receptive to using drug-prescribing AI and anticipate doing so within 5 years. Respondents suggested initial settings for AI-driven drug prescribing include situations where there are standard guidelines (74%), where the decision is whether to continue a current prescription in someone (55%), and where prescribing decisions rely on high-complexity clinical data (44%). Many (66%) indicated a preference for conditional to fully autonomous drug-prescribing AI. Clustering analysis identified 2 psychological profile-types, “optimists” and “pragmatists”, who have different standards for model efficacy, expediency, explainability, and governance/stewardship for drug-prescribing AI. A high level of using medical AI is the strongest predictor for being an optimist (OR = 2.98 [2.53, 3.51]; P  < 0.0001). In conclusion, our data point to the wide acceptability of conditional autonomous drug-prescribing AI among Chinese physicians. Moreover, disparity in optimism about drug-prescribing AI is caused by disparity in prior exposure to medical AI.

This study aimed to investigate the prognostic impact of lymph node metastasis (LNM) on patients with colorectal cancer liver metastasis (CRLM) and elucidate the underlying immune mechanisms using multiomics profiling. We enrolled patients with CRLM from the US Surveillance, Epidemiology, and End Results (SEER) cohort and a multicenter Chinese cohort, integrating bulk RNA sequencing, single-cell RNA sequencing and proteomics data. The cancer-specific survival (CSS) and immune profiles of the tumor-draining lymph nodes (TDLNs), primary tumors and liver metastasis were compared between patients with and without LNM. Pathological evaluations were used to assess immune cell infiltration and histological features. The CRLM patients with LNM had significantly shorter CSS than patients without LNM in two large cohorts. Our results showed that nonmetastatic TDLNs exhibited a greater abundance of immune cells, including CD4+ T cells, CD8+ T cells, and CD19+ B cells, whereas metastatic TDLNs were enriched with fibroblasts, endothelial cells, and macrophages. Immunohistochemical analysis confirmed elevated levels of CD3+ T cells, CD8+ T cells, and CD19+ B cells in nonmetastatic TDLNs. The presence of nonmetastatic TDLNs was associated with enhanced antitumor immune responses in primary tumors, characterized by a higher Klintrup-Makinen (KM) grade and the presence of tertiary lymphoid structures. Furthermore, liver metastasis in patients with nonmetastatic TDLNs were predominantly of the desmoplastic growth pattern (dHGP), while those with metastatic TDLNs were predominantly of the replacement growth pattern (rHGP). This research highlights the adverse prognostic impact of LNM on patients with CRLM and reveals potential related mechanisms through multiomics analysis. Our research paves the way for further refinement of the AJCC TNM staging system for CRLM in clinical practice.