Predicting response to immunotherapy in gastric cancer via assessing perineural invasion-mediated inflammation in tumor microenvironment

Overcoming immunotherapy resistance in colorectal cancer through nano-selenium probiotic complexes and IL-32 modulation

BACKGROUND AND OBJECTIVE

Colorectal cancer (CRC) is a major global health burden, with immunotherapy often limited by immune tolerance and resistance. This study introduces an innovative approach using Selenium Nanoparticles-Loaded Extracellular Vesicles combined with Interleukin-32 and Engineered Probiotic Escherichia coli Nissle 1917 (SeNVs@NE-IL32-EcN) to enhance CD8+ T cell-mediated immune responses and overcome immunotherapy resistance.

METHODS

Single-cell RNA sequencing (scRNA-seq) and transcriptomic analyses were performed to identify key immune cells and regulators involved in CRC immunotherapy resistance, focusing on CD8+ T cells and the regulatory factor IL32. A humanized xenograft mouse model was used to evaluate the impact of IL32 and SeNVs@NE-IL32-EcN on tumor growth and immune responses. The SeNVs@NE-IL32-EcN complex was synthesized through a reverse micelle method and functionalized using extracellular vesicles. Its morphology, size, antioxidant activity, and safety were characterized using electron microscopy, dynamic light scattering (DLS), and in vitro co-culture assays.

RESULTS

Single-cell analyses revealed a significant reduction in CD8+ T cell infiltration in immunotherapy-resistant CRC patients. IL32 was identified as a key regulator enhancing CD8+ T cell cytotoxic activity through granzyme B and IFN-γ secretion. Treatment with SeNVs@NE-IL32-EcN significantly improved the proliferation and activity of CD8+ T cells and reduced tumor progression in humanized mouse models. In vitro and in vivo results demonstrated the complex's biocompatibility, antioxidant properties, and ability to enhance CRC immunotherapy while mitigating immune tolerance.

CONCLUSION

SeNVs@NE-IL32-EcN offers a novel nano-biomaterial strategy that integrates nanotechnology and probiotic therapy to enhance CD8+ T cell-mediated immunity and overcome CRC immunotherapy resistance. This study lays the foundation for future therapeutic applications in cancer treatment by advancing immune-modulating biomaterials.

Neural-cancer crosstalk: Reciprocal molecular circuits driving gastric tumorigenesis and emerging therapeutic opportunities

The nervous system plays an important role in regulating physiological functions of the stomach, and its abnormal activity often impairs gastric homeostasis. In response to constant exposure to oncogenic stimuli that leads to gastric tumorigenesis, the neural system becomes an essential component of the tumor microenvironment via perineural infiltration, de novo neurogenesis, and axonogenesis, thereby driving cancer initiation and progression. In this review, we highlight emerging discoveries related to neural-cancer crosstalk and discuss how the nervous system is remodeled by tumor cells including neural components and modulators (including neurotransmitters and neuropeptides). Moreover, we provide a systematic analysis of neural control of the cellular hallmarks of cancer. Finally, we propose how the molecular circuits of neural-cancer crosstalk could be exploited as potential targets for novel anti-cancer treatment, providing new insights into a new modality of neural-based cancer therapeutic strategies.

A comprehensive study on the radiomic score derived from perineural invasion in gastric cancer and its correlation with the overall survival of patients

BACKGROUND

Perineural invasion (PNI) is closely related to the prognosis of gastric cancer (GC) patients. However, a noninvasive tool for accurately and reliably predicting the PNI is lacking.

METHODS

The clinical and imaging data of 278 patients from institution I and 39 patients from institution II were retrospectively analyzed. Radiomic features were extracted from the intratumoral and peritumoral regions. Seven independent machine learning (ML) algorithms are used to develop the models. Kaplan-Meier survival analysis and Cox proportional hazards analysis were carried out to compare 3-year and 5-year overall survival (OS) differences among various subgroups based on PNI and radiomic scores.

RESULTS

T stage and lymphovascular invasion (LVI) were significantly correlated with the PNI (P < 0.01). The OS of patients with different PNI status was significantly different (P < 0.05). Gradient boosting tree is the best ML algorithm. The area-under-the-curve (AUC) values of the optimal radiomics model in the internal test set and external test set were 0.901 and 0.886, respectively. After the introduction of clinical variables T stage and LVI, the performance of the model further improved in predicting the PNI of GC patients, with the AUC of 0.904 in the internal test set and 0.886 in the external test set. The difference in 3-year OS (P = 0.005) and 5-year OS (P = 0.015) among patients with varying radiomic scores was statistically significant.

CONCLUSION

Radiomics combined with intratumoral and peritumoral features is feasible for evaluating the PNI of GC patients. The prognosis of patients with different radiomic scores was statistically significant.

Identifying high-risk candidates for prolonging progression-free survival in primary gastric carcinoma subject to "double invasion": an analytical approach utilizing lasso-cox regression

OBJECTIVE

To identify high-risk gastric carcinoma patients with concurrent vascular and neural invasion ("double invasion") who are at heightened risk of progression-free survival (PFS) decline, enabling personalized clinical management.

METHODS

In this multi-center retrospective study, 559 patients with double invasion who underwent curative gastrectomy between May 2002 and December 2020 were analyzed. Prognostic factors for PFS were identified using Lasso-Cox regression. Model validation included internal bootstrapping, calibration plots, and comparison against the American Joint Committee on Cancer(AJCC) 8th edition TNM staging system via Harrell's C-index, decision curve analysis (DCA), and time-dependent receiver operating characteristic (ROC) curves.

RESULTS

The nomogram integrated gender, positive lymph node count, surgical gastrectomy method, PTEN/FHIT expression levels, and maximum tumor diameter. It demonstrated superior predictive accuracy to AJCC staging, with a C-index of 0.651 (95% CI: 0.612-0.691) versus 0.543 (95% CI: 0.517-0.569). Calibration plots showed strong agreement between predicted and observed outcomes. The area under the curve(AUC) for 3- and 5-year PFS predictions were 0.719 (95% CI: 0.655-0.771) and 0.767 (95% CI: 0.670-0.841), respectively. DCA confirmed clinical utility across decision thresholds, and risk stratification effectively differentiated low- and high-risk groups. In the training cohort, the model significantly outperformed AJCC staging (NRI: 0.218, p < 0.01; IDI: 0.085, p < 0.01). However, this superiority was not statistically significant in the validation cohort (NRI: 0.141, p = 0.08; IDI: 0.031, p = 0.239).

CONCLUSION

We developed a Lasso-Cox regression-based nomogram to stratify PFS risk in gastric carcinoma patients with double invasion. While the model outperformed AJCC staging in training, validation cohort results highlight the need for further refinement. This tool holds potential for guiding tailored therapeutic strategies, though broader validation is warranted to confirm clinical applicability.

Central nervous system and immune cells interactions in cancer: unveiling new therapeutic avenues

Cancer remains a leading cause of mortality worldwide. Despite significant advancements in cancer research, our understanding of its complex developmental pathways remains inadequate. Recent research has clarified the intricate relationship between the central nervous system (CNS) and cancer, particularly how the CNS influences tumor growth and metastasis via regulating immune cell activity. The interactions between the central nervous system and immune cells regulate the tumor microenvironment via various signaling pathways, cytokines, neuropeptides, and neurotransmitters, while also incorporating processes that alter the tumor immunological landscape. Furthermore, therapeutic strategies targeting neuro-immune cell interactions, such as immune checkpoint inhibitors, alongside advanced technologies like brain-computer interfaces and nanodelivery systems, exhibit promise in improving treatment efficacy. This complex bidirectional regulatory network significantly affects tumor development, metastasis, patient immune status, and therapy responses. Therefore, understanding the mechanisms regulating CNS-immune cell interactions is crucial for developing innovative therapeutic strategies. This work consolidates advancements in CNS-immune cell interactions, evaluates their potential in cancer treatment strategies, and provides innovative insights for future research and therapeutic approaches.

Mechanisms of Cancer-Induced Bone Pain

Bone is a common site of advanced cancer metastasis, second only to the lungs and liver. Cancer-induced bone pain (CIBP) is a persistent and intense pain that is caused by a combination of inflammatory and neuropathic factors. As CIBP progresses, the degree of pain intensifies. Despite advancements in medical technology, the treatment outcomes of patients with CIBP remain unsatisfactory, and severe pain can typically only be controlled with opioid medications. However, patients treated with opioid medications often develop tolerance. Therefore, they may require dose increases, which can increase the severity of opioid-induced side effects, in turn influencing quality of life. The peripheral mechanisms of CIBP primarily involve bone tissue damage, tumor microenvironment formation, and changes in the dorsal root ganglion. The central mechanisms usually involve biochemical and electrophysiological changes in the spinal cord and brain. The spinal cord is the main processing center for nociceptive signals. When tumor cells produce inflammatory mediators that acidify the microenvironment or damage nerve endings, the spinal cord becomes excessively stimulated, resulting in increased or prolonged pain signals that propagate to the higher central nervous system through the ascending pathway. There are substantial differences in the pain generation mechanisms between CIBP and common inflammatory and neuropathic pain. Therefore, understanding the mechanisms underpinning CIBP development at the level of the spinal cord is crucial for optimizing pain management. This study explores the pathogenesis of CIBP at the level of the spinal cord and describes recently proposed treatment methods for CIBP.

The predictive value of miR-132-3p combined with Prognostic Nutritional Index (PNI) for gastric cancer prognosis

BACKGROUND

Previous studies have demonstrated that PNI can predict the prognosis of gastric cancer (GC) patients. However, few studies have focused on the auxiliary role of miRNA in predicting the prognosis of GC.

OBJECTIVE

This research seeks to clarify the role of the combined use of miR-132-3p and PNI in predicting the prognosis of GC patients.

METHODS

The qRT-PCR was used to assess the expression of miR-132-3p in tumor and adjacent normal tissues with GC patients. The predictive value of miR-132-3p and PNI for postoperative prognosis, and the relationships between miR-132-3p, PNI, and preoperative clinical characteristics, were assessed using ROC, χ², Kaplan-Meier survival analysis, and Cox regression analysis.

RESULTS

miR-132-3p was found to be downregulated in GC tumor tissues and significantly positively correlated with PNI. Both miR-132-3p and PNI were significantly associated with TNM stage and lymph node metastasis. Postoperative GC patients with low miR-132-3p expression and low PNI had lower survival rates, and both were independent risk factors for poor prognosis. The combination of miR-132-3p and PNI demonstrated better sensitivity and specificity in predicting postoperative prognosis than either indicator alone.

CONCLUSION

The combination of miR-132-3p and PNI can effectively improve the predictive value of postoperative prognosis in GC patients.

Treatment strategy and therapy based on immune response in patients with gastric cancers

In this editorial, we highlight the significance of a retrospective study "Analysis of the impact of immunotherapy efficacy and safety in patients with gastric cancer and liver metastasis" performed by Liu et al. The authors utilized data collected from gastric cancer (GC) patients and assessed immunotherapy effectiveness and survival status. They found significant differences in treatment response. Because immunotherapy seems to be a beneficial strategy for advanced GC patients, stratification of the data based on metastasis status may further improve treatment strategies.

Recent developments in immunotherapy for gastrointestinal tract cancers

The past few decades have witnessed the rise of immunotherapy for Gastrointestinal (GI) tract cancers. The role of immune checkpoint inhibitors (ICIs), particularly programmed death protein 1 (PD-1) and PD ligand-1 antibodies, has become increasingly pivotal in the treatment of advanced and perioperative GI tract cancers. Currently, anti-PD-1 plus chemotherapy is considered as first-line regimen for unselected advanced gastric/gastroesophageal junction adenocarcinoma (G/GEJC), mismatch repair deficient (dMMR)/microsatellite instability-high (MSI-H) colorectal cancer (CRC), and advanced esophageal cancer (EC). In addition, the encouraging performance of claudin18.2-redirected chimeric antigen receptor T-cell (CAR-T) therapy in later-line GI tract cancers brings new hope for cell therapy in solid tumour treatment. Nevertheless, immunotherapy for GI tumour remains yet precise, and researchers are dedicated to further maximising and optimising the efficacy. This review summarises the important research, latest progress, and future directions of immunotherapy for GI tract cancers including EC, G/GEJC, and CRC.

The fibrosis-4 index is a prognostic factor for cholangiocarcinoma patients who received immunotherapy

Background

Research of immunotherapy for cholangiocarcinoma has yielded some results, but more clinical data are needed to prove its efficacy and safety. Moreover, there is a need to identify accessible indexes for selecting patients who may benefit from such treatments.

Methods

The medical records of 66 cholangiocarcinoma patients who underwent immunotherapy were retrospectively collected. The effectiveness of immunotherapy was assessed by tumor response, progression-free survival (PFS), and overall survival (OS), while safety was evaluated by adverse events during treatment. Univariate and multivariate Cox regression analyses were performed to identify prognostic risk factors for PFS and OS, and Kaplan-Meier curves of potential prognostic factors were drawn.

Results

Overall, in this study, immunotherapy achieved an objective response rate of 24.2% and a disease control rate of 89.4% for the included patients. The median PFS was 445 days, and the median OS was 772.5 days. Of the 66 patients, 65 experienced adverse events during treatment, but none had severe consequences. Multivariate Cox analysis indicated that tumor number is a prognostic risk factor for disease progression following immunotherapy in cholangiocarcinoma patients, while tumor differentiation and the fibrosis-4 (FIB-4) index are independent risk factors for OS.

Conclusion

In general, immunotherapy for cholangiocarcinoma is safe, with adverse events remaining within manageable limits, and it can effectively control disease progression in most patients. The FIB-4 index may reflect the potential benefit of immunotherapy for patients with cholangiocarcinoma.

Exploring the immune escape mechanisms in gastric cancer patients based on the deep AI algorithms and single-cell sequencing analysis

Gastric cancer is a prevalent and deadly malignancy, and the response to immunotherapy varies among patients. This study aimed to develop a prognostic model for gastric cancer patients and investigate immune escape mechanisms using deep machine learning and single-cell sequencing analysis. Data from public databases were analysed, and a prediction model was constructed using 101 algorithms. The high-AIDPS group, characterized by increased AIDPS expression, exhibited worse survival, genomic variations and immune cell infiltration. These patients also showed immunotherapy tolerance. Treatment strategies targeting the high-AIDPS group identified three potential drugs. Additionally, distinct cluster groups and upregulated AIDPS-associated genes were observed in gastric adenocarcinoma cell lines. Inhibition of GHRL expression suppressed cancer cell activity, inhibited M2 polarization in macrophages and reduced invasiveness. Overall, AIDPS plays a critical role in gastric cancer prognosis, genomic variations, immune cell infiltration and immunotherapy response, and targeting GHRL expression holds promise for personalized treatment. These findings contribute to improved clinical management in gastric cancer.

Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model: A dual-center study

BACKGROUND

Perineural invasion (PNI) has been used as an important pathological indicator and independent prognostic factor for patients with rectal cancer (RC). Preoperative prediction of PNI status is helpful for individualized treatment of RC. Recently, several radiomics studies have been used to predict the PNI status in RC, demonstrating a good predictive effect, but the results lacked generalizability. The preoperative prediction of PNI status is still challenging and needs further study.

AIM

To establish and validate an optimal radiomics model for predicting PNI status preoperatively in RC patients.

METHODS

This retrospective study enrolled 244 postoperative patients with pathologically confirmed RC from two independent centers. The patients underwent pre-operative high-resolution magnetic resonance imaging (MRI) between May 2019 and August 2022. Quantitative radiomics features were extracted and selected from oblique axial T2-weighted imaging (T2WI) and contrast-enhanced T1WI (T1CE) sequences. The radiomics signatures were constructed using logistic regression analysis and the predictive potential of various sequences was compared (T2WI, T1CE and T2WI + T1CE fusion sequences). A clinical-radiomics (CR) model was established by combining the radiomics features and clinical risk factors. The internal and external validation groups were used to validate the proposed models. The area under the receiver operating characteristic curve (AUC), DeLong test, net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA) were used to evaluate the model performance.

RESULTS

Among the radiomics models, the T2WI + T1CE fusion sequences model showed the best predictive performance, in the training and internal validation groups, the AUCs of the fusion sequence model were 0.839 [95% confidence interval (CI): 0.757-0.921] and 0.787 (95%CI: 0.650-0.923), which were higher than those of the T2WI and T1CE sequence models. The CR model constructed by combining clinical risk factors had the best predictive performance. In the training and internal and external validation groups, the AUCs of the CR model were 0.889 (95%CI: 0.824-0.954), 0.889 (95%CI: 0.803-0.976) and 0.894 (95%CI: 0.814-0.974). Delong test, NRI, and IDI showed that the CR model had significant differences from other models (P < 0.05). Calibration curves demonstrated good agreement, and DCA revealed significant benefits of the CR model.

CONCLUSION

The CR model based on preoperative MRI radiomics features and clinical risk factors can preoperatively predict the PNI status of RC noninvasively, which facilitates individualized treatment of RC patients.