Association of the Collagen Signature in the Tumor Microenvironment With Lymph Node Metastasis in Early Gastric Cancer

Multitask machine learning-based tumor-associated collagen signatures predict peritoneal recurrence and disease-free survival in gastric cancer

BACKGROUND

Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironment affect the migration and treatment response of cancer cells. Herein, we proposed multitask machine learning-based tumor-associated collagen signatures (TACS), which are composed of quantitative collagen features derived from multiphoton imaging, to simultaneously predict peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS).

METHODS

Among 713 consecutive patients, with 275 in training cohort, 222 patients in internal validation cohort, and 216 patients in external validation cohort, we developed and validated a multitask machine learning model for simultaneously predicting peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS). The accuracy of the model for prediction of peritoneal recurrence and prognosis as well as its association with adjuvant chemotherapy were evaluated.

RESULTS

The TACSPR and TACSDFS were independently associated with peritoneal recurrence and disease-free survival in three cohorts, respectively (all P < 0.001). The TACSPR demonstrated a favorable performance for peritoneal recurrence in all three cohorts. In addition, the TACSDFS also showed a satisfactory accuracy for disease-free survival among included patients. For stage II and III diseases, adjuvant chemotherapy improved the survival of patients with low TACSPR and low TACSDFS, or high TACSPR and low TACSDFS, or low TACSPR and high TACSDFS, but had no impact on patients with high TACSPR and high TACSDFS.

CONCLUSIONS

The multitask machine learning model allows accurate prediction of peritoneal recurrence and survival for GC and could distinguish patients who might benefit from adjuvant chemotherapy.

Genomic events stratifying prognosis of early gastric cancer

BACKGROUND

The purpose of the study was to conduct a comprehensive genomic characterization of gene alterations, microsatellite instability (MSI), and tumor mutational burden (TMB) in submucosal-penetrating (Pen) early gastric cancers (EGCs) with varying prognoses.

METHODS

Samples from EGC patients undergoing surgery and with 10-year follow-up data available were collected. Tissue genomic alterations were characterized using Trusight Oncology panel (TSO500). Pathway instability (PI) scores for a selection of 218 GC-related pathways were calculated both for the present case series and EGCs from the TCGA cohort.

RESULTS

Higher age and tumor location in the upper-middle tract are significantly associated with an increased hazard of relapse or death from any cause (p = 0.006 and p = 0.032). Even if not reaching a statistical significance, Pen A tumors more frequently present higher TMB values, higher frequency of MSI-subtypes and an overall increase in PI scores, along with an enrichment in immune pathways. ARID1A gene was observed to be significantly more frequently mutated in Pen A tumors (p = 0.006), as well as in patients with high TMB (p = 0.027). Tumors harboring LRP1B alterations seem to have a higher hazard of relapse or death from any cause (p = 0.089), being mutated mainly in relapsed patients (p = 0.093).

CONCLUSIONS

We found that the most aggressive subtype Pen A is characterized by a higher frequency of ARID1A mutations and a higher genetic instability, while LRP1B alterations seem to be related to a lower disease-free survival. Further investigations are needed to provide a rationale for the use of these markers to stratify prognosis in EGC patients.

Clinicopathological Factors and Nomogram Construction for Lymph Node Metastasis in Locally Advanced Gastric Cancer

Background

The research on lymph node metastasis (LNM) in locally advanced gastric cancer (LAGC) infiltrating the subserous tissue and serous membrane (T3-4a) is significantly inadequate. This study aims to explore the clinicopathological factors related to LNM in stages T3 and T4a LAGC, while also developing predictive nomograms.

Methods

After systematic searching and rigorous screening, 1995 T3 and 1244 T4a LAGC cases who underwent surgery without neoadjuvant or perioperative chemotherapy were selected. The risk factors associated with LNM were identified using both univariate and multivariate logistic regression analyses. Subsequently, the independent variables identified through the multivariate analyses were utilized to construct a nomogram.

Results

The incidence of LNM in T3 and T4a LAGC was 77.1% (1539/1995) and 83.8% (1043/1244), respectively. The following factors were found to be independently associated with LNM in T3 LAGC: preoperative serum albumin <41g/L (P=0.007), gastrointestinal obstruction (P<0.001), tumor location (P=0.040), tumor size >4cm (P=0.002), mixed (P=0.001) and undifferentiated histological types (P=0.002), presence of lymphovascular invasion (LVI) (P<0.001) and nerve invasion (P<0.001). Additionally, in T4a LAGC cases, serum albumin < 39g/L (P=0.004), tumor size >6cm (P=0.020), mixed (P<0.001) and undifferentiated histological types (P<0.001), presence of gastrointestinal hemorrhage (P=0.016), neuroendocrine differentiation (P=0.024), and LVI (P<0.001) independently influenced the occurrence of LNM.

Conclusion

This study identified the risk factors associated with LNM in T3-4a LAGC cases and constructed nomograms, thereby providing valuable guidance for formulating and implementing a multidisciplinary perioperative treatment program.

Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy

Diagnostic pathology, historically dependent on visual scrutiny by experts, is essential for disease detection. Advances in digital pathology and developments in computer vision technology have led to the application of artificial intelligence (AI) in this field. Despite these advancements, the variability in pathologists' subjective interpretations of diagnostic criteria can lead to inconsistent outcomes. To meet the need for precision in cancer therapies, there is an increasing demand for accurate pathological diagnoses. Consequently, traditional diagnostic pathology is evolving towards "next-generation diagnostic pathology", prioritizing on the development of a multi-dimensional, intelligent diagnostic approach. Using nonlinear optical effects arising from the interaction of light with biological tissues, multiphoton microscopy (MPM) enables high-resolution label-free imaging of multiple intrinsic components across various human pathological tissues. AI-empowered MPM further improves the accuracy and efficiency of diagnosis, holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic criteria. In this review, we systematically outline the applications of MPM in pathological diagnosis across various human diseases, and summarize common multiphoton diagnostic features. Moreover, we examine the significant role of AI in enhancing multiphoton pathological diagnosis, including aspects such as image preprocessing, refined differential diagnosis, and the prognostication of outcomes. We also discuss the challenges and perspectives faced by the integration of MPM and AI, encompassing equipment, datasets, analytical models, and integration into the existing clinical pathways. Finally, the review explores the synergy between AI and label-free MPM to forge novel diagnostic frameworks, aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems in clinical settings.

Overcoming the Tumor Collagen Barriers: A Multistage Drug Delivery Strategy for DDR1-Mediated Resistant Colorectal Cancer Therapy

The extracellular matrix (ECM) is critical for drug resistance in colorectal cancer (CRC). The abundant collagen within the ECM significantly influences tumor progression and matrix-mediated drug resistance (MMDR) by binding to discoidin domain receptor 1 (DDR1), but the specific mechanisms by which tumor cells modulate ECM via DDR1 and ultimately regulate TME remain poorly understand. Furthermore, overcoming drug resistance by modulating the tumor ECM remains a challenge in CRC treatment. In this study, a novel mechanism is elucidated by which DDR1 mediates the interactions between tumor cells and collagen, enhances collagen barriers, inhibits immune infiltration, promotes drug efflux, and leads to MMDR in CRC. To address this issue, a multistage drug delivery system carrying DDR1-siRNA and chemotherapeutic agents is employed to disrupt collagen barriers by silencing DDR1 in tumor, enhancing chemotherapy drugs diffusion and facilitating immune infiltration. These findings not only revealed a novel role for collagen-rich matrix mediated by DDR1 in tumor resistance, but also introduced a promising CRC treatment strategy.

Label-free imaging diagnosis and collagen-optical evaluation of endometrioid adenocarcinoma with multiphoton microscopy

The assessment of tumor grade and pathological stage plays a pivotal role in determining the treatment strategy and predicting the prognosis of endometrial cancer. In this study, we employed multiphoton microscopy (MPM) to establish distinctive optical pathological signatures specific to endometrioid adenocarcinoma (EAC), while also assessing the diagnostic sensitivity, specificity, and accuracy of MPM for this particular malignancy. The MPM technique exhibits robust capability in discriminating between benign hyperplasia and various grades of cancer tissue, with statistically significant differences observed in nucleocytoplasmic ratio and second harmonic generation/two-photon excited fluorescence intensity. Moreover, by utilizing semi-automated image analysis, we identified notable disparities in six collagen signatures between benign and malignant endometrial stroma. Our study demonstrates that MPM can differentiate between benign endometrial hyperplasia and EAC without labels, while also quantitatively assessing changes in the tumor microenvironment by analyzing collagen signatures in the endometrial stromal tissue.

Identification of collagen subtypes of gastric cancer for distinguishing patient prognosis and therapeutic response

Background

Gastric cancer is a highly heterogeneous disease, presenting a major obstacle to personalized treatment. Effective markers of the immune checkpoint blockade response are needed for precise patient classification. We, therefore, divided patients with gastric cancer according to collagen gene expression to indicate their prognosis and treatment response.

Methods

We collected data for 1250 patients with gastric cancer from four cohorts. For the TCGA-STAD cohort, we used consensus clustering to stratify patients based on expression levels of 44 collagen genes and compared the prognosis and clinical characteristics between collagen subtypes. We then identified distinct transcriptomic and genetic alteration signatures for the subtypes. We analyzed the associations of collagen subtypes with the responses to chemotherapy, immunotherapy, and targeted therapy. We also established a platform-independent collagen-subtype predictor. We verified the findings in three validation cohorts (GSE84433, GSE62254, and GSE15459) and compared the collagen subtyping method with other molecular subtyping methods.

Results

We identified two subtypes of gastric adenocarcinoma: a high-expression collagen subtype (CS-H) and a low-expression collagen subtype (CS-L). Collagen subtype was an independent prognostic factor, with better overall survival in the CS-L subgroup. The inflammatory response, angiogenesis, and phosphoinositide 3-kinase (PI3K)/Akt pathways were transcriptionally active in the CS-H subtype, while DNA repair activity was significantly greater in the CS-L subtype. PIK3CA was frequently amplified in the CS-H subtype, while PIK3C2A, PIK3C2G, and PIK3R1 were frequently deleted in the CS-L subtype. CS-H subtype tumors were more sensitive to fluorouracil, while CS-L subtype tumors were more sensitive to immune checkpoint blockade. CS-L subtype was predicted to be more sensitive to HER2-targeted drugs, and CS-H subtype was predicted to be more sensitive to vascular endothelial growth factor and PI3K pathway-targeting drugs. Collagen subtyping also has the potential to be combined with existing molecular subtyping methods for better patient classification.

Conclusions

We classified gastric cancers into two subtypes based on collagen gene expression and validated these subtypes in three validation cohorts. The collagen subgroups differed in terms of prognosis, clinical characteristics, transcriptome, and genetic alterations. The subtypes were closely related to patient responses to chemotherapy, immunotherapy, and targeted therapy.

N6-methyladenosine modified circPAK2 promotes lymph node metastasis via targeting IGF2BPs/VEGFA signaling in gastric cancer

Circular RNAs (circRNAs) have emerged as key regulators of cancer occurrence and progression, as well as promising biomarkers for cancer diagnosis and prognosis. However, the potential mechanisms of circRNAs implicated in lymph node (LN) metastasis of gastric cancer remain unclear. Herein, we identify a novel N6-methyladenosine (m6A) modified circRNA, circPAK2, which is significantly upregulated in gastric cancer tissues and metastatic LN tissues. Functionally, circPAK2 enhances the migration, invasion, lymphangiogenesis, angiogenesis, epithelial-mesenchymal transition (EMT), and metastasis of gastric cancer in vitro and in vivo. Mechanistically, circPAK2 is exported by YTH domain-containing protein 1 (YTHDC1) from the nucleus to the cytoplasm in an m6A methylation-dependent manner. Moreover, increased cytoplasmic circPAK2 interacts with Insulin-Like Growth Factor 2 mRNA-Binding Proteins (IGF2BPs) and forms a circPAK2/IGF2BPs/VEGFA complex to stabilize VEGFA mRNA, which contributes to gastric cancer vasculature formation and aggressiveness. Clinically, high circPAK2 expression is positively associated with LN metastasis and poor prognosis in gastric cancer. This study highlights m6A-modified circPAK2 as a key regulator of LN metastasis of gastric cancer, thus supporting circPAK2 as a promising therapeutic target and prognostic biomarker for gastric cancer.

Clinicopathologic factors correlated with lymph node metastasis in gastric cancer: a retrospective cohort study involving 5606 patients

BACKGROUND

The identification of risk factors associated with lymph node metastasis (LNM) in gastric cancer will establish a crucial foundation for the implementation of endoscopic operation and multidisciplinary treatment programs.

METHODS

A total of 5606 patients with gastric cancer with comprehensive clinicopathologic data were enrolled through systematic searching and rigorous screening. Of the 5606 patients, 1438 were diagnosed with early gastric cancer (EGC), which would be used for further analysis. Subsequently, univariate and multivariate logistic regression analyses were performed to identify the risk factors.

RESULTS

The rates of LNM in T1a, T1b, T2, T3, T4a, and T4b stage gastric cancer were 7.0%, 19.4%, 48.4%, 77.1%, 83.8%, and 89.6%, respectively. Female (odds ratio [OR], 1.559; P = .032), lower tumor location (OR, 1.773; P = .023), tumor size of >2 cm (OR, 2.007; P < .001), mixed (OR, 2.371; P = .001) and undifferentiated histologic types (OR, 2.952; P < .001), T1b stage (OR, 2.041; P < .001), presence of ulceration (OR, 1.758; P = .027), and lymphovascular invasion (OR, 5.722; P < .001) were identified as independent risk factors for LNM in EGC. A nomogram was constructed using appropriate predictors to preoperatively predict the risk of LNM in patients with EGC.

CONCLUSION

This study identified the clinicopathologic factors associated with LNM in patients with EGC and developed a prediction model, thereby facilitating the integration of diverse treatment modalities in managing patients with EGC.

Immune-related long noncoding RNA zinc finger protein 710-AS1-201 promotes the metastasis and invasion of gastric cancer cells

BACKGROUND

Gastric cancer (GC) is a prevalent malignant tumor of the gastrointestinal system. ZNF710 is a transcription factor (TF), and zinc finger protein 710 (ZNF710)-AS1-201 is an immune-related long noncoding RNA (lncRNA) that is upregulated in GC cells.

AIM

To assess the correlation between ZNF710-AS1-201 and immune microenvironment features and to investigate the roles of ZNF710-AS1-201 in the invasion and metastasis processes of GC cells.

METHODS

We obtained data from The Cancer Genome Atlas and Wujin Hospital. We assessed cell growth, migration, invasion, and programmed cell death using cell counting kit-8, EdU, scratch, Transwell, and flow cytometry assays. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to identify the potential downstream targets of ZNF710-AS1-201.

RESULTS

In GC tissues with low ZNF710-AS1-201 expression, immunoassays detected significant infiltration of various antitumor immune cells, such as memory CD8 T cells and activated CD4 T cells. In the low-expression group, the half-maximal inhibitory concentrations (IC50s) of 5-fluorouracil, cisplatin, gemcitabine, and trametinib were lower, whereas the IC50s of dasatinib and vorinostat were higher. The malignant degree of GC was higher and the stage was later in the high-expression group. Additionally, patients with high expression of ZNF710-AS1-201 had lower overall survival and disease-free survival rates. In vitro, the overexpression of ZNF710-AS1-201 greatly enhanced growth, metastasis, and infiltration while suppressing cell death in HGC-27 cells. In contrast, the reduced expression of ZNF710-AS1-201 greatly hindered cell growth, enhanced apoptosis, and suppressed the metastasis and invasion of MKN-45 cells. The expression changes in ZNF710 were significant, but the corresponding changes in isocitrate dehydrogenase-2, Semaphorin 4B, ARHGAP10, RGMB, hsa-miR-93-5p, and ZNF710-AS1-202 were not consistent or statistically significant after overexpression or knockdown of ZNF710-AS1-201, as determined by qRT-PCR.

CONCLUSION

Immune-related lncRNA ZNF710-AS1-201 facilitates the metastasis and invasion of GC cells. It appears that ZNF710-AS1-201 and ZNF710 have potential as effective targets for therapeutic intervention in GC. Nevertheless, it is still necessary to determine the specific targets of the ZNF710 TF.

Early gastric cancer recurrence after endoscopic submucosal dissection: Not to be ignored!

This editorial comments on the article "Efficacy of multi-slice spiral computed tomography in evaluating gastric cancer recurrence after endoscopic submucosal dissection". We focus on the importance of paying more attention to post-endoscopic submucosal dissection (ESD) gastric cancer recurrence in patients with early gastric cancer (EGC) and how to manage it effectively. ESD has been a well-known treatment and the mainstay for EGC, with the advantages of less invasion and fewer complications when compared with traditional surgical procedures. Despite a lower local recurrence rate after ESD, the problem of postoperative recurrence in patients with EGC has become increasingly non-ignorable with the global popularization of ESD technology and the increasing number of post-ESD patients.

Monitoring the Intracellular pH and Metabolic State of Cancer Cells in Response to Chemotherapy Using a Combination of Phosphorescence Lifetime Imaging Microscopy and Fluorescence Lifetime Imaging Microscopy

The extracellular matrix (ECM), in which collagen is the most abundant protein, impacts many aspects of tumor physiology, including cellular metabolism and intracellular pH (pHi), as well as the efficacy of chemotherapy. Meanwhile, the role of collagen in differential cell responses to treatment within heterogeneous tumor environments remains poorly investigated. In the present study, we simultaneously monitored the changes in pHi and metabolism in living colorectal cancer cells in vitro upon treatment with a chemotherapeutic combination, FOLFOX (5-fluorouracil, oxaliplatin and leucovorin). The pHi was followed using the new pH-sensitive probe BC-Ga-Ir, working in the mode of phosphorescence lifetime imaging (PLIM), and metabolism was assessed from the autofluorescence of the metabolic cofactor NAD(P)H using fluorescence lifetime imaging (FLIM) with a two-photon laser scanning microscope. To model the ECM, 3D collagen-based hydrogels were used, and comparisons with conventional monolayer cells were made. It was found that FOLFOX treatment caused an early temporal intracellular acidification (reduction in pHi), followed by a shift to more alkaline values, and changed cellular metabolism to a more oxidative state. The presence of unstructured collagen markedly reduced the cytotoxic effects of FOLFOX, and delayed and diminished the pHi and metabolic responses. These results support the observation that collagen is a factor in the heterogeneous response of cancer cells to chemotherapy and a powerful regulator of their metabolic behavior.

Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation

The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying fibrillar collagen organization has become a powerful approach for characterizing the topology of collagen fibers and studying the role of collagen fibers in disease progression. We present a deep learning-based pipeline to quantify collagen fibers' topological properties in microscopy-based collagen images from pathological tissue samples. Our method leverages deep neural networks to extract collagen fiber centerlines and deep generative models to create synthetic training data, addressing the current shortage of large-scale annotations. As a part of this effort, we have created and annotated a collagen fiber centerline dataset, with the hope of facilitating further research in this field. Quantitative measurements such as fiber orientation, alignment, density, and length can be derived based on the centerline extraction results. Our pipeline comprises three stages. Initially, a variational autoencoder is trained to generate synthetic centerlines possessing controllable topological properties. Subsequently, a conditional generative adversarial network synthesizes realistic collagen fiber images from the synthetic centerlines, yielding a synthetic training set of image-centerline pairs. Finally, we train a collagen fiber centerline extraction network using both the original and synthetic data. Evaluation using collagen fiber images from pancreas, liver, and breast cancer samples collected via second-harmonic generation microscopy demonstrates our pipeline's superiority over several popular fiber centerline extraction tools. Incorporating synthetic data into training further enhances the network's generalizability. Our code is available at https://github.com/uw-loci/collagen-fiber-metrics.

Collagen-related gene expression level predicts the prognosis and immune therapy response

BACKGROUND

Gastric cancer patients responded differently to the same treatment strategy and had various prognoses for the lack of biomarkers to guide the therapy choice.

METHODS

RNA data of a local gastric cancer cohort with 103 patients were processed and used to explore potential treatment guiding factors. Cluster analysis was performed by non-negative matrix factorization. The expression level of collagen-related genes was evaluated by ssGSEA named collagen score (CS). Data from TCGA, ACRG, and an immune therapy cohort were utilized to explore prognosis and efficacy. Prognostic predictive power of CS was assessed using the nomogram.

RESULTS

In our study, local RNA data were processed by cluster analysis, and it was found that cluster 2 contained a worse tumor infiltration status. The GSEA result showed that collagen-related pathways were differentially activated in two clusters. In TCGA and ACRG cohorts, the CS can be used as an independent prognostic factor (TCGA OS: p = 0.018, HR = 3.5; ACRG OS: p = 0.014, HR = 4.88). An immunotherapy cohort showed that the patients with higher CS had a significantly worse ORR (p = 0.0025). The high CS group contained several cell death pathways down-regulated and contained the worse tumor microenvironment. The nomogram demonstrated the survival prediction capability of collagen score.

CONCLUSION

CS was verified as an independent prognostic factor and potentially reflected the therapeutic effect of immunotherapy. The CS could provide a new way to evaluate the clinical prognosis and response information helping develop the collagen-targeted treatment.

Detection of fibrotic changes in the progression of liver diseases by label-free multiphoton imaging

Collagen fibers play an important role in the progression of liver diseases. The formation and progression of liver fibrosis is a dynamic pathological process accompanied by morphological changes in collagen fibers. In this study, we used multiphoton microscopy for label-free imaging of liver tissues, allowing direct detection of various components including collagen fibers, tumors, blood vessels, and lymphocytes. Then, we developed a deep learning classification model to automatically identify tumor regions, and the accuracy reaches 0.998. We introduced an automated image processing method to extract eight collagen morphological features from various stages of liver diseases. Statistical analysis showed significant differences between them, indicating the potential use of these quantitative features for monitoring fibrotic changes during the progression of liver diseases. Therefore, multiphoton imaging combined with automatic image processing method would hold a promising future in rapid and label-free diagnosis of liver diseases.

A nomogram based on collagen signature for predicting the immunoscore in colorectal cancer

Objectives

The Immunoscore can categorize patients into high- and low-risk groups for prognostication in colorectal cancer (CRC). Collagen plays an important role in immunomodulatory functions in the tumor microenvironment (TME). However, the correlation between collagen and the Immunoscore in the TME is unclear. This study aimed to construct a collagen signature to illuminate the relationship between collagen structure and Immunoscore.

Methods

A total of 327 consecutive patients with stage I-III stage CRC were included in a training cohort. The fully quantitative collagen features were extracted at the tumor center and invasive margin of the specimens using multiphoton imaging. LASSO regression was applied to construct the collagen signature. The association of the collagen signature with Immunoscore was assessed. A collagen nomogram was developed by incorporating the collagen signature and clinicopathological predictors after multivariable logistic regression. The performance of the collagen nomogram was evaluated via calibration, discrimination, and clinical usefulness and then tested in an independent validation cohort. The prognostic values of the collagen nomogram were assessed using Cox regression and the Kaplan-Meier method.

Results

The collagen signature was constructed based on 16 collagen features, which included 6 collagen features from the tumor center and 10 collagen features from the invasive margin. Patients with a high collagen signature were more likely to show a low Immunoscore (Lo IS) in both cohorts (P<0.001). A collagen nomogram integrating the collagen signature and clinicopathological predictors was developed. The collagen nomogram yielded satisfactory discrimination and calibration, with an AUC of 0.925 (95% CI: 0.895-0.956) in the training cohort and 0.911 (95% CI: 0.872-0.949) in the validation cohort. Decision curve analysis confirmed that the collagen nomogram was clinically useful. Furthermore, the collagen nomogram-predicted subgroup was significantly associated with prognosis. Moreover, patients with a low-probability Lo IS, rather than a high-probability Lo IS, could benefit from chemotherapy in high-risk stage II and stage III CRC patients.

Conclusions

The collagen signature is significantly associated with the Immunoscore in the TME, and the collagen nomogram has the potential to individualize the prediction of the Immunoscore and identify CRC patients who could benefit from adjuvant chemotherapy.

Validated Pretreatment Prediction Models for Response to Neoadjuvant Therapy in Patients with Rectal Cancer: A Systematic Review and Critical Appraisal

Pretreatment response prediction is crucial to select those patients with rectal cancer who will benefit from organ preservation strategies following (intensified) neoadjuvant therapy and to avoid unnecessary toxicity in those who will not. The combination of individual predictors in multivariable prediction models might improve predictive accuracy. The aim of this systematic review was to summarize and critically appraise validated pretreatment prediction models (other than radiomics-based models or image-based deep learning models) for response to neoadjuvant therapy in patients with rectal cancer and provide evidence-based recommendations for future research. MEDLINE via Ovid, Embase.com, and Scopus were searched for eligible studies published up to November 2022. A total of 5006 studies were screened and 16 were included for data extraction and risk of bias assessment using Prediction model Risk Of Bias Assessment Tool (PROBAST). All selected models were unique and grouped into five predictor categories: clinical, combined, genetics, metabolites, and pathology. Studies generally included patients with intermediate or advanced tumor stages who were treated with neoadjuvant chemoradiotherapy. Evaluated outcomes were pathological complete response and pathological tumor response. All studies were considered to have a high risk of bias and none of the models were externally validated in an independent study. Discriminative performances, estimated with the area under the curve (AUC), ranged per predictor category from 0.60 to 0.70 (clinical), 0.78 to 0.81 (combined), 0.66 to 0.91 (genetics), 0.54 to 0.80 (metabolites), and 0.71 to 0.91 (pathology). Model calibration outcomes were reported in five studies. Two collagen feature-based models showed the best predictive performance (AUCs 0.83-0.91 and good calibration). In conclusion, some pretreatment models for response prediction in rectal cancer show encouraging predictive potential but, given the high risk of bias in these studies, their value should be evaluated in future, well-designed studies.

Association of collagen deep learning classifier with prognosis and chemotherapy benefits in stage II-III colon cancer

The current tumor-node-metastasis staging system does not provide sufficient prognostic prediction or adjuvant chemotherapy benefit information for stage II-III colon cancer (CC) patients. Collagen in the tumor microenvironment affects the biological behaviors and chemotherapy response of cancer cells. Hence, in this study, we proposed a collagen deep learning (collagenDL) classifier based on the 50-layer residual network model for predicting disease-free survival (DFS) and overall survival (OS). The collagenDL classifier was significantly associated with DFS and OS (P < 0.001). The collagenDL nomogram, integrating the collagenDL classifier and three clinicopathologic predictors, improved the prediction performance, which showed satisfactory discrimination and calibration. These results were independently validated in the internal and external validation cohorts. In addition, high-risk stage II and III CC patients with high-collagenDL classifier, rather than low-collagenDL classifier, exhibited a favorable response to adjuvant chemotherapy. In conclusion, the collagenDL classifier could predict prognosis and adjuvant chemotherapy benefits in stage II-III CC patients.

Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram

Peritoneal recurrence is the most frequent and lethal recurrence pattern in gastric cancer (GC) with serosal invasion after radical surgery. However, current evaluation methods are not adequate for predicting peritoneal recurrence in GC with serosal invasion. Emerging evidence shows that pathomics analyses could be advantageous for risk stratification and outcome prediction. Herein, we propose a pathomics signature composed of multiple pathomics features extracted from digital hematoxylin and eosin-stained images. We found that the pathomics signature was significantly associated with peritoneal recurrence. A competing-risk pathomics nomogram including carbohydrate antigen 19-9 level, depth of invasion, lymph node metastasis, and pathomics signature was developed for predicting peritoneal recurrence. The pathomics nomogram had favorable discrimination and calibration. Thus, the pathomics signature is a predictive indicator of peritoneal recurrence, and the pathomics nomogram may provide a helpful reference for predicting an individual's risk in peritoneal recurrence of GC with serosal invasion.

Label-free detection of invasive micropapillary carcinoma of the breast using multiphoton microscopy

Invasive micropapillary carcinoma of the breast (IMPC) is a rare form of breast cancer with unique histological features, and is associated with high axillary lymph node metastasis and poor clinical prognosis. Thus, IMPC should be diagnosed in time to improve the treatment and management of patients. In this study, multiphoton microscopy (MPM) is used to label-free visualize the morphological features of IMPC. Our results demonstrate that MPM images are well in agreement with hematoxylin and eosin staining and epithelial membrane antigen staining, indicating MPM is comparable to traditional histological analysis in identifying the tissue structure and cell morphology. Statistical analysis shows significant differences in the circumference and area of the glandular lumen and cancer nest between the different IMPC cell clusters with complete glandular lumen morphology, and also shows difference in collagen length, width, and orientation, indicating the invasive ability of different morphologies of IMPC may be different.

Using nomogram, decision tree, and deep learning models to predict lymph node metastasis in patients with early gastric cancer: a multi-cohort study

The accurate assessment of lymph node metastasis (LNM) in patients with early gastric cancer is critical to the selection of the most appropriate surgical treatment. This study aims to develop an optimal LNM prediction model using different methods, including nomogram, Decision Tree, Naive Bayes, and deep learning methods. In this study, we included two independent datasets: the gastrectomy set (n=3158) and the endoscopic submucosal dissection (ESD) set (n=323). The nomogram, Decision Tree, Naive Bayes, and fully convolutional neural networks (FCNN) models were established based on logistic regression analysis of the development set. The predictive power of the LNM prediction models was revealed by time-dependent receiver operating characteristic (ROC) curves and calibration plots. We then used the ESD set as an external cohort to evaluate the models' performance. In the gastrectomy set, multivariate analysis showed that gender (P=0.008), year when diagnosed (2006-2010 year, P=0.265; 2011-2015 year, P=0.001; and 2016-2020 year, P<0.001, respectively), tumor size (2-4 cm, P=0.001; and ≥4 cm, P<0.001, respectively), tumor grade (poorly-moderately, P=0.016; moderately, P<0.001; well-moderately, P<0.001; and well, P<0.001, respectively), vascular invasion (P<0.001), and pT stage (P<0.001) were independent risk factors for LNM in early gastric cancer. The area under the curve (AUC) for the validation set using the nomogram, Decision Tree, Naive Bayes, and FCNN models were 0.78, 0.76, 0.77, and 0.79, respectively. In conclusion, our multi-cohort study systematically investigated different LNM prediction methods for patients with early gastric cancer. These models were validated and shown to be reliable with AUC>0.76 for all. Specifically, the FCNN model showed the most accurate prediction of LNM risks in early gastric cancer patients with AUC=0.79. Based on the FCNN model, patients with LNM rates of >4.77% are strong candidates for gastrectomy rather than ESD surgery.

Construction of a nomogram for preoperative prediction of the risk of lymph node metastasis in early gastric cancer

Background

The status of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) is particularly important for the formulation of clinical treatment. The purpose of this study was to construct a nomogram to predict the risk of LNM in EGC before operation.

Methods

Univariate analysis and logistic regression analysis were used to determine the independent risk factors for LNM. The independent risk factors were included in the nomogram, and the prediction accuracy, discriminant ability and clinical practicability of the nomogram were evaluated by the receiver operating characteristic curve (ROC), calibration curve and clinical decision curve (DCA), and 100 times ten-fold cross-validation was used for internal validation.

Results

33 (11.3%) cases of AGC were pathologically confirmed as LNM. In multivariate analysis, T stage, presence of enlarged lymph nodes on CT examination, carbohydrate antigen 199 (CA199), undifferentiated histological type and systemic inflammatory response index (SIRI) were risk factors for LNM. The area under the ROC curve of the nomogram was 0.86, the average area under the ROC curve of the 100-fold ten-fold cross-validation was 0.85, and the P value of the Hosmer-Lemeshow test was 0.60. In addition, the clinical decision curve, net reclassification index (NRI) and Integrated Discriminant Improvement Index (IDI) showed that the nomogram had good clinical utility.

Conclusions

We found that SIRI is a novel biomarker for preoperative prediction of LNM in EGC, and constructed a nomogram for preoperative prediction of the risk of LNM in EGC, which is helpful for the formulation of the clinical treatment strategies.

A Clinicopathological Feature-Based Nomogram for Predicting the Likelihood of D3 Lymph Node Metastasis in Right-Sided Colon Cancer Patients

BACKGROUND

Despite advancements in treating right-sided colon cancer patients, the ideal scope of lymphadenectomy remains controversial.

OBJECTIVE

Our objective was to investigate the likelihood of D3 lymph node metastasis in right-sided colon cancer patients and develop a clinicopathological feature-based nomogram for D3 lymphadenectomy.

DESIGN

We retrospectively analyzed 286 right-sided colon cancer patients who underwent D3 lymphadenectomy. The patients were divided into 2 groups based on whether D3 lymph node metastasis was positive. Then, univariable and multivariable logistic regression analyses were performed to obtain independent risk factors for predicting D3 lymph node metastasis. Moreover, we performed receiver operating characteristic curve analyses to evaluate the predictive power of the model.

SETTING

This study was conducted at Nanfang Hospital of Southern Medical University in China.

PATIENTS

A total of 286 consecutive patients who underwent right hemicolectomy and D3 lymphadenectomy as a primary treatment for right-sided colon cancer between January 2016 and December 2019 were enrolled in this study.

MAIN OUTCOME MEASURES

The primary measures were independent risk factors for predicting D3 lymph node metastasis in right-sided colon cancer.

RESULTS

The D3 lymph node metastasis rate in right-sided colon cancer patients was 16.1% (46/286). D3 lymphadenectasis on CT, lymphatic invasion, and T4 tumors were filtered out as independent risk factors for D3 lymph node metastasis according to the multivariable logistic regression analysis. We established a nomogram that predicted D3 lymph node metastasis of right-sided colon cancer on the combination of the 3 factors with an area under the curve of 0.717 (95% CI, 0.629-0.806).

LIMITATIONS

This was a retrospective study from a single center.

CONCLUSIONS

We developed a valuable clinicopathological feature-based nomogram to predict the incidence of D3 lymph node metastasis in right-sided colon cancer patients. Patients with D3 lymphadenectasis on CT, preoperative T4 tumors, and lymphatic invasion should undergo D3 lymphadenectomy. See Video Abstract at http://links.lww.com/DCR/B852 .

UN NOMOGRAMA BASADO EN CARACTERSTICAS CLNICOPATOLGICAS PARA PREDECIR LA PROBABILIDAD DE METSTASIS EN GANGLIOS LINFTICOS D EN PACIENTES CON CNCER DE COLON DERECHO

ANTECEDENTES:A pesar de los avances en el tratamiento de pacientes con cáncer de colon derecho, el ámbito ideal de la linfadenectomía sigue siendo controvertido.OBJETIVO:Investigar la probabilidad de metástasis en los ganglios linfáticos D3 en pacientes con cáncer de colon derecho y desarrollar un nomograma basado en características clínico-patológicas basado para la linfadenectomía D3.DISEÑO:Analizamos retrospectivamente a 286 pacientes con cáncer de colon derecho que se sometieron a linfadenectomía D3. Los pacientes se dividieron en dos grupos en función de si eran positivos para metástasis en los ganglios linfáticos D3. Luego, se realizaron análisis de regresión logística univariable y multivariable para obtener factores de riesgo independientes para predecir metástasis en los ganglios linfáticos D3. Además, realizamos análisis de las curvas de características operatorias del receptor para evaluar el poder predictivo del modelo.SEDE:Este estudio se realizó en el Hospital Nanfang de la Universidad Médica del Sur en China.PACIENTES:Un total de 286 pacientes consecutivos que se sometieron a hemicolectomía derecha y linfadenectomía D3 como tratamiento primario para el cáncer de colon derecho entre enero de 2016 y diciembre de 2019 se inscribieron en este estudio.PRINCIPALES MEDIDAS DE RESULTADO:Las medidas primarias fueron factores de riesgo independientes para predecir las metástasis en ganglios linfáticos D3 en el cáncer de colon derecho.RESULTADOS:La tasa de metástasis en los ganglios linfáticos D3 en pacientes con cáncer de colon del lado derecho fue del 16,1% (46/286). El aumento de tamaño de ganglios D3 en la TC, la invasión linfática y los tumores T4 se filtraron como factores de riesgo independientes de metástasis en los ganglios linfáticos D3 de acuerdo con el análisis de regresión logística multivariable. Establecimos un nomograma que predijo metástasis en los ganglios linfáticos D3 del cáncer de colon derecho en la combinación de los tres factores con un área bajo la curva de 0,717 (IC del 95%, 0,629-0,806).LIMITACIONES:Este fue un estudio retrospectivo de un solo centro.CONCLUSIONES:Desarrollamos un valioso nomograma basado en características clínico-patológicas para predecir la incidencia de metástasis en los ganglios linfáticos D3 en pacientes con cáncer de colon derecho. Los pacientes con crecimiento de ganglios D3 en TC, tumores con clasificación preoperatoria T4 e invasión linfática, deben ser sometidos a linfadenectomía D3. Consulte Video Resumen en http://links.lww.com/DCR/B852 . (Traducción-Dr. Juan Carlos Reyes ).

Loss of GNE Predicts Lymph Node Metastasis in Early Gastric Cancer

Endoscopic surgery is increasingly utilized for the treatment of early gastric cancer (EGC) worldwide, whereas lymph node metastasis (LNM) remains a critical risk factor for the relapse of EGC after endoscopic surgery. Therefore, identifying potential predictive factors and understanding the molecular mechanisms are urgently needed for improving the outcome of EGC patients with LNM. UDP-N-acetylglucosamine 2-epimerase/N-acetylmannosamine kinase (GNE) is the key enzyme in the process of biosynthesis of CMP-Neu5Ac from UDP-N-acetylglucosamine (UDP-GlcNAc), which acts as a substrate for several reactions in glycan metabolism. In this study, we found that GNE was down-regulated in EGC patients with LNM. GNE expression as well as localization, tumor size, intravascular tumor thrombi and Lauren's classification were further identified as independent predictive factors for LNM. Combining GNE expression with traditional risk factors, including tumor size and differentiation degrees, could generate a better model for predicting LNM in EGC patients. Overall, our study implies that low GNE expression is a potential predictor of LNM in EGC.

Prognostic and predictive value of a pathomics signature in gastric cancer

The current tumour-node-metastasis (TNM) staging system alone cannot provide adequate information for prognosis and adjuvant chemotherapy benefits in patients with gastric cancer (GC). Pathomics, which is based on the development of digital pathology, is an emerging field that might improve clinical management. Herein, we propose a pathomics signature (PSGC) that is derived from multiple pathomics features of haematoxylin and eosin-stained slides. We find that the PSGC is an independent predictor of prognosis. A nomogram incorporating the PSGC and TNM staging system shows significantly improved accuracy in predicting the prognosis compared to the TNM staging system alone. Moreover, in stage II and III GC patients with a low PSGC (but not in those with a high PSGC), satisfactory chemotherapy benefits are observed. Therefore, the PSGC could serve as a prognostic predictor in patients with GC and might be a potential predictive indicator for decision-making regarding adjuvant chemotherapy.

Post-operative anastomotic leakage and collagen changes in patients with rectal cancer undergoing neoadjuvant chemotherapy vs chemoradiotherapy

Background

A significant difference in the anastomotic leakage (AL) rate has been observed between patients with locally advanced rectal cancer who have undergone preoperative chemotherapy and those undergoing preoperative chemoradiotherapy. This study aimed to quantitatively analyse collagen structural changes caused by preoperative chemoradiotherapy and illuminate the relationship between collagen changes and AL.

Methods

Anastomotic distal and proximal "doughnut" specimens from the Sixth Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) were quantitatively assessed for collagen structural changes between patients with and without preoperative radiotherapy using multiphoton imaging. Then, patients treated with preoperative chemoradiotherapy were used as a training cohort to construct an AL-SVM classifier by the Mann-Whitney U test and support vector machine (SVM). An independent test cohort from the Fujian Province Cancer Hospital (Fuzhou, China) was used to validate the AL-SVM classifier.

Results

A total of 207 patients were included from the Sixth Affiliated Hospital of Sun Yat-sen University. The AL rate in the preoperative chemoradiotherapy group (n = 107) was significantly higher than that in the preoperative chemotherapy group (n = 100) (21.5% vs 7.0%, P = 0.003). A fully quantitative analysis showed notable morphological and spatial distribution feature changes in collagen in the preoperative chemoradiotherapy group. Then, the patients who received preoperative chemoradiotherapy were used as a training cohort to construct the AL-SVM classifier based on five collagen features and the tumor distance from the anus. The AL-SVM classifier showed satisfactory discrimination and calibration with areas under the curve of 0.907 and 0.856 in the training and test cohorts, respectively.

Conclusions

The collagen structure may be notably altered by preoperative radiotherapy. The AL-SVM classifier was useful for the individualized prediction of AL in rectal cancer patients undergoing preoperative chemoradiotherapy.

Establishment of a nomogram for predicting lymph node metastasis in patients with early gastric cancer after endoscopic submucosal dissection

Background

Endoscopic submucosal dissection (ESD) has been accepted as the standard treatment for the appropriate indication of early gastric cancer (EGC). Determining the risk of lymph node metastasis (LNM) is critical for the following treatment selection after ESD. This study aimed to develop a predictive model to quantify the probability of LNM in EGC to help minimize the invasive procedures.

Methods

A total of 952 patients with EGC who underwent radical gastrectomy were retrospectively reviewed. LASSO regression was used to help screen the potential risk factors. Multivariate logistic regression was used to establish a predictive nomogram, which was subjected to discrimination and calibration evaluation, bootstrapping internal validation, and decision curve analysis.

Results

Results of multivariate analyses revealed that gender, fecal occult blood test, CEA, CA19-9, histologic differentiation grade, lymphovascular invasion, depth of infiltration, and Ki67 labeling index were independent prognostic factors for LNM. The nomogram had good discriminatory performance, with a concordance index of 0.816 (95% CI 0.781–0.853). The validation dataset yielded a corrected concordance index of 0.805 (95% CI 0.770–0.842). High agreements between ideal curves and calibration curves were observed.

Conclusions

The nomogram is clinically useful for predicting LNM after ESD in EGC, which is beneficial to identifying patients who are at low risk for LNM and would benefit from avoiding an unnecessary gastrectomy.

Mapping Lymph Node during Indocyanine Green Fluorescence-Imaging Guided Gastric Oncologic Surgery: Current Applications and Future Directions

Huge strides have been made in the navigation of gastric cancer surgery thanks to the improvement of intraoperative techniques. For now, the use of indocyanine green (ICG) enhanced fluorescence imaging has received promising results in detecting sentinel lymph nodes (SLNs) and tracing lymphatic drainages, which make it applicable for limited and precise lymphadenectomy. Nevertheless, issues of the lack of specificity and unpredictable false-negative lymph nodes were encountered in gastric oncologic surgery practice using ICG-enhanced fluorescence imaging (ICG-FI), which restrict its application. Here, we reviewed the current application of ICG-FI and assessed potential approaches to improving ICG-FI.

Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

Objective

To evaluate the diagnostic performance of machine learning (ML) in predicting lymph node metastasis (LNM) in patients with gastric cancer (GC) and to identify predictors applicable to the models.

Methods

PubMed, EMBASE, Web of Science, and Cochrane Library were searched from inception to March 16, 2022. The pooled c-index and accuracy were used to assess the diagnostic accuracy. Subgroup analysis was performed based on ML types. Meta-analyses were performed using random-effect models. Risk of bias assessment was conducted using PROBAST tool.

Results

A total of 41 studies (56182 patients) were included, and 33 of the studies divided the participants into a training set and a test set, while the rest of the studies only had a training set. The c-index of ML for LNM prediction in training set and test set was 0.837 [95%CI (0.814, 0.859)] and 0.811 [95%CI (0.785-0.838)], respectively. The pooled accuracy was 0.781 [(95%CI (0.756-0.805)] in training set and 0.753 [95%CI (0.721-0.783)] in test set. Subgroup analysis for different ML algorithms and staging of GC showed no significant difference. In contrast, in the subgroup analysis for predictors, in the training set, the model that included radiomics had better accuracy than the model with only clinical predictors (F = 3.546, p = 0.037). Additionally, cancer size, depth of cancer invasion and histological differentiation were the three most commonly used features in models built for prediction.

Conclusion

ML has shown to be of excellent diagnostic performance in predicting the LNM of GC. One of the models covering radiomics and its ML algorithms showed good accuracy for the risk of LNM in GC. However, the results revealed some methodological limitations in the development process. Future studies should focus on refining and improving existing models to improve the accuracy of LNM prediction.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022320752

The Role of TAMs in Tumor Microenvironment and New Research Progress

Tumor-associated macrophages (TAMs) are an important part of tumor microenvironment (TME) and play a key role in TME, participating in the process of tumor occurrence, growth, invasion, and metastasis. Among them, metastasis to tumor tissue is the key step of malignant development of tumor. In this paper, the latest progress in the role of TAMs in the formation of tumor microenvironment is summarized. It is particularly noteworthy that cell and animal experiments show that TAMs can provide a favorable microenvironment for the occurrence and development of tumors. At the same time, clinical pathological experiments show that the accumulation of TAMs in tumor is related to poor clinical efficacy. Finally, this paper discusses the feasibility of TAMs-targeted therapy as a new indirect cancer therapy. This paper provides a theoretical basis for finding a potentially effective macrophage-targeted tumor therapy.

A Prospectively Validated Nomogram for Predicting the Risk of PHQ-9 Score ≥15 in Patients With Erectile Dysfunction: A Multi-Center Study

Background

Although erectile dysfunction (ED) often occurs simultaneously with depression, not all patients with ED suffer major depression (MD), with a PHQ-9 score ≥15 indicating MD. Because the PHQ-9 questionnaire includes phrases such as “I think I am a loser” and “I want to commit suicide,” the psychological burdens of ED patients are likely to increase inevitably after using the PHQ-9, which, in turn, may affect ED therapeutic effects. Accordingly, we endeavored to develop a nomogram to predict individual risk of PHQ-9 score ≥15 in these patients.

Methods

The data of 1,142 patients with ED diagnosed in Xijing Hospital and Northwest Women and Children's Hospital from January 2017 to May 2020 were analyzed. While the Least Absolute Shrinkage and Selection Operator regression was employed to screen PHQ-9 score ≥15 related risk factors, multivariate logistic regression analysis was performed to verify these factors and construct the nomogram. The training cohort and an independent cohort that comprised 877 prospectively enrolled patients were used to demonstrate the efficacy of the nomogram.

Results

The IIEF-5 score, PEDT score, physical pain score, frequent urination, and feeling of endless urination were found to be independent factors of PHQ-9 score ≥15 in patients with ED. The nomogram developed by these five factors showed good calibration and discrimination in internal and external validation, with a predictive accuracy of 0.757 and 0.722, respectively. The sensitivity and specificity of the nomogram in the training cohort were 0.86 and 0.52, respectively. Besides, the sensitivity and specificity of the nomogram in the validation cohort were 0.73 and 0.62, respectively. Moreover, based on the nomogram, the sample was divided into low-risk and high-risk groups.

Conclusion

This study established a nomogram to predict individual risk of PHQ-9 score ≥15 in patients with ED. It is deemed that the nomogram may be employed initially to avoid those with a low risk of MD completing questionnaires unnecessarily.

Collagen signature as a novel biomarker to predict axillary lymph node metastasis in breast cancer using multiphoton microscopy

Accurate identification of axillary lymph node (ALN) status is crucial for tumor staging procedure and decision making. This retrospective study of 898 participants from two institutions was conducted. The aim of this study is to evaluate the diagnostic performance of clinical parameters combined with collagen signatures (tumor-associated collagen signatures [TACS] and the TACS corresponding microscopic features [TCMF]) in predicting the probability of ALN metastasis in patients with breast cancer. These findings suggest that TACS and TCMF in the breast tumor microenvironment are both novel and independent biomarkers for the estimation of ALN metastasis. The nomogram based on independent clinical parameters combined with TACS and TCMF yields good diagnostic performance in predicting ALN status.

Association of the Collagen Signature with Pathological Complete Response in Rectal Cancer Patients

Collagen in the tumor microenvironment is recognized as a potential biomarker for predicting treatment response. This study investigated whether the collagen features are associated with pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) and develop and validate a prediction model for individualized prediction of pCR. The prediction model was developed in a primary cohort (353 consecutive patients). In total, 142 collagen features were extracted from the multiphoton image of pretreatment biopsy, and the least absolute shrinkage and selection operator (Lasso) regression was applied for feature selection and collagen signature building. A nomogram was developed using multivariable analysis. The performance of the nomogram was assessed with respect to its discrimination, calibration, and clinical utility. An independent cohort (163 consecutive patients) was used to validate the model. The collagen signature comprised four collagen features significantly associated with pCR both in the primary and validation cohorts (p < 0.001). Predictors in the individualized prediction nomogram included the collagen signature and clinicopathological predictors. The nomogram showed good discrimination with area under the ROC curve (AUC) of 0.891 in the primary cohort and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (AUC = 0.908) and good calibration. Decision curve analysis demonstrated that the nomogram was clinically useful. In conclusion, the collagen signature in the tumor microenvironment of pretreatment biopsy is significantly associated with pCR. The nomogram based on the collagen signature and clinicopathological predictors could be used for individualized prediction of pCR in LARC patients before nCRT.

Multiphoton microscopy providing pathological-level quantification of myocardial fibrosis in transplanted human heart

Multiphoton microscopy (MPM), a high-resolution laser scanning technique, has been shown to provide detailed real-time information on fibrosis assessment in animal models. But the value of MPM in human histology, especially in heart tissue, has not been fully explored. We aimed to evaluate the association between myocardial fibrosis measured by MPM and that measured by histological staining in the transplanted human heart. One hundred and twenty samples of heart tissue were obtained from 20 patients consisting of 10 dilated cardiomyopathies (DCM) and 10 ischemic cardiomyopathies (ICM). MPM and picrosirius red staining were performed to quantify collagen volume fraction (CVF) in explanted hearts postoperatively. Cardiomyocyte and myocardial fibrosis could be clearly visualized by MPM. Although patients with ICM had significantly greater MPM-derived CVF than patients with DCM (25.33  ± 12.65 % vs. 19.82  ± 8.62 %, p = 0.006), there was a substantial overlap of CVF values between them. MPM-derived CVF was comparable to that derived from picrosirius red staining based on all samples (22.58 ± 11.13% vs. 21.19 ± 11.79%, p = 0.348), as well as in DCM samples and ICM samples. MPM-derived CVF was correlated strongly with the magnitude of staining-derived CVF in both all samples and DCM samples and ICM samples (r = 0.972, r = 0.963, r = 0.973, respectively; all p < 0.001). Intra- and inter-observer reproducibility for MPM-derived CVF and staining-derived CVF were 0.995, 0.989, 0.995, and 0.985, respectively. Our data demonstrated that MPM can provide a pathological-level assessment of myocardial microstructure in transplanted human heart.

Tumor-Derived Exosomes Modulate Primary Site Tumor Metastasis

Tumor-derived exosomes (TDEs) are actively produced and released by tumor cells and carry messages from tumor cells to healthy cells or abnormal cells, and they participate in tumor metastasis. In this review, we explore the underlying mechanism of action of TDEs in tumor metastasis. TDEs transport tumor-derived proteins and non-coding RNA to tumor cells and promote migration. Transport to normal cells, such as vascular endothelial cells and immune cells, promotes angiogenesis, inhibits immune cell activation, and improves chances of tumor implantation. Thus, TDEs contribute to tumor metastasis. We summarize the function of TDEs and their components in tumor metastasis and illuminate shortcomings for advancing research on TDEs in tumor metastasis.

A nomogram for decision-making assistance on surgical treatment of chronic osteomyelitis in long bones: Establishment and validation based on a retrospective multicenter cohort

BACKGROUND

Chronic osteomyelitis remains a major challenge for orthopedic surgeons due to its high recurrence rate. Surgeons currently have few tools to estimate the likelihood of individual recurrence. We here aimed to develop a nomogram to better estimate individual recurrence rate after surgical treatment of chronic osteomyelitis in long bones.

METHODS

We first retrospectively identified patients as training cohort who had received surgical treatment of chronic osteomyelitis in long bones between January 2010 and January 2016 from four hospitals. Patient demographic, microbiological, clinical, and therapeutic variables were collected and analyzed. Univariate and multivariate analyses were performed successively to identify independently predictive factors for recurrence. To reduce overfitting, the Bayesian information criterion was used to reduce variables in the original model. Nomograms were created with the reduced model after model selection. The nomogram was then internally validated with bootstrap resampling. We then further validated the performance of the established nomogram in validation cohort (data from two distinct institutions).

RESULTS

Recurrence was found in 136 of 655 (20.8%) and 52 of 201 patients (25.9%) in training and validation cohorts respectively. We included six independent prognostic factors for recurrence in our prediction model: number of previous recurrences, epiphysial involvement, preoperative serum albumin level, axial length of the infectious lesion, lesion-removal method, and application of a muscular flap. After incorporating these six factors, the nomogram achieved good discrimination, with concordance indexes of 0.82 (95% CI, 0.79-0.85) and 0.80 (95% CI, 0.78-0.83) in predicting recurrence in the training and validation cohorts, respectively. Calibration curves were well fitted for both training and validation cohorts.

CONCLUSIONS

Our nomogram achieved good preoperative prediction of recurrence in chronic osteomyelitis of long bones. Using this nomogram, the recurrence risk can be confidently predicted for each patient and treatment plan. After considering and discussing the functional prognosis with patients, physicians can establish a rational therapeutic plan.

LEVEL OF EVIDENCE

Prognostic, Level III.

Somatic mutations in collagens are associated with a distinct tumor environment and overall survival in gastric cancer

BACKGROUND

Gastric cancer is a heterogeneous disease with poorly understood genetic and microenvironmental factors. Mutations in collagen genes are associated with genetic diseases that compromise tissue integrity, but their role in tumor progression has not been extensively reported. Aberrant collagen expression has been long associated with malignant tumor growth, invasion, chemoresistance, and patient outcomes. We hypothesized that somatic mutations in collagens could functionally alter the tumor extracellular matrix.

METHODS

We used publicly available datasets including The Tumor Cancer Genome Atlas (TCGA) to interrogate somatic mutations in collagens in stomach adenocarcinomas. To demonstrate that collagens were significantly mutated above background mutation rates, we used a moderated Kolmogorov-Smirnov test along with combination analysis with a bootstrap approach to define the background accounting for mutation rates. Association between mutations and clinicopathological features was evaluated by Fisher or chi-squared tests. Association with overall survival was assessed by Kaplan-Meier and the Cox-Proportional Hazards Model. Gene Set Enrichment Analysis was used to interrogate pathways. Immunohistochemistry and in situ hybridization tested expression of COL7A1 in stomach tumors.

RESULTS

In stomach adenocarcinomas, we identified individual collagen genes and sets of collagen genes harboring somatic mutations at a high frequency compared to background in both microsatellite stable, and microsatellite instable tumors in TCGA. Many of the missense mutations resemble the same types of loss of function mutations in collagenopathies that disrupt tissue formation and destabilize cells providing guidance to interpret the somatic mutations. We identified combinations of somatic mutations in collagens associated with overall survival, with a distinctive tumor microenvironment marked by lower matrisome expression and immune cell signatures. Truncation mutations were strongly associated with improved outcomes suggesting that loss of expression of secreted collagens impact tumor progression and treatment response. Germline collagenopathy variants guided interpretation of impactful somatic mutations on tumors.

CONCLUSIONS

These observations highlight that many collagens, expressed in non-physiologically relevant conditions in tumors, harbor impactful somatic mutations in tumors, suggesting new approaches for classification and therapy development in stomach cancer. In sum, these findings demonstrate how classification of tumors by collagen mutations identified strong links between specific genotypes and the tumor environment.

Nomograms for predicting difficult airway based on ultrasound assessment

Background

Accurate prediction of the difficult airway (DA) could help to prevent catastrophic consequences in emergency resuscitation, intensive care, and general anesthesia. Until now, there is no nomogram prediction model for DA based on ultrasound assessment. In this study, we aimed to develop a predictive model for difficult tracheal intubation (DTI) and difficult laryngoscopy (DL) using nomogram based on ultrasound measurement. We hypothesized that nomogram could utilize multivariate data to predict DTI and DL.

Methods

A prospective observational DA study was designed. This study included 2254 patients underwent tracheal intubation. Common and airway ultrasound indicators were used for the prediction, including thyromental distance (TMD), modified Mallampati test (MMT) score, upper lip bite test (ULBT) score temporomandibular joint (TMJ) mobility and tongue thickness (TT). Univariate and the Akaike information criterion (AIC) stepwise logistic regression were used to identify independent predictors of DTI and DL. Nomograms were constructed to predict DL and DTL based on the AIC stepwise analysis results. Receiver operating characteristic (ROC) curves were used to evaluate the accuracy of the nomograms.

Results

Among the 2254 patients enrolled in this study, 142 (6.30%) patients had DL and 51 (2.26%) patients had DTI. After AIC stepwise analysis, ULBT, MMT, sex, TMJ, age, BMI, TMD, IID, and TT were integrated for DL nomogram; ULBT, TMJ, age, IID, TT were integrated for DTI nomogram. The areas under the ROC curves were 0.933 [95% confidence interval (CI), 0.912–0.954] and 0.974 (95% CI, 0.954–0.995) for DL and DTI, respectively.

Conclusion

Nomograms based on airway ultrasonography could be a reliable tool in predicting DA.

Trial registration

Chinese Clinical Trial Registry (No. ChiCTR-RCS-14004539), registered on 13th April 2014.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-022-01567-y.

Application of Multiphoton Microscopic Imaging in Study of Gastric Cancer

Multiphoton microscopy (MPM) imaging relies on the nonlinear interaction between ultrashort optical pulses and the samples to achieve image contrast. Featuring larger penetration depth, less phototoxicity, 3-dimensional sectioning capability, no need for labeling, MPM become a powerful medical imaging technique that can identify structural characteristics of tissues at the cellular and subcellular levels. In this review paper, we introduce the working principle of MPM imaging, present the current results of MPM imaging applied to the study of gastric tumors, and discuss the future prospects of this interdisciplinary research field.

Collagen Score in the Tumor Microenvironment Predicts the Prognosis of Rectal Cancer Patients after Neoadjuvant Chemoradiotherapy

BACKGROUND AND PURPOSE

Little is known about the relationship between collagen and the prognosis of patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT). This study aimed to quantitatively analyze collagen alterations, establish a collagen score (CS) in the tumor microenvironment, and evaluate and validate the relationship of the CS with prognosis in these patients.

MATERIALS AND METHODS

A total of 365 primary patients diagnosed with LARC after nCRT between 2011 and 2018 were retrospectively reviewed (training cohort: 210; independent validation cohort: 155). Multiple collagen features of two fields in the tumor microenvironment, the core of the tumor (CT) and the invasive margin (IM), were derived from multiphoton imaging, and the CS_IM-CT was generated using least absolute shrinkage and selection operator (LASSO) Cox regression analysis.

RESULTS

The CS_IM-CT was created based on 3 features: collagen area, number of collagen fibers and a Gabor textural feature. In the training cohort, the CS_IM-CT predicted 3-year disease-free survival (DFS) with an area under the receiver operating characteristic curve (AUROC) of 0.765 (0.675-0.854) and an overall survival (OS) with AUROC of 0.822 (0.734-0.909). Additionally, the CS_IM-CT was significantly associated with DFS and OS in the two cohorts. A nomogram with the CS_IM-CT was developed and showed good prognostic value predicting a 3-year DFS with an AUROC of 0.826 (0.748-0.905) and an OS with AUROC of 0.882 (0.803-0.960).

CONCLUSIONS

The CS_IM-CT is an effective prognostic marker in patients with LARC after nCRT, and the nomogram with the CS_IM-CT can be used to accurately predict the individual prognosis of these patients.

Risk assessment of lymph node metastasis in early gastric cancer: Establishment and validation of a 7-point scoring model

BACKGROUND

Treatment options for early gastric cancer have evolved toward achieving accurate evaluation of lymph node metastasis. This study aimed to investigate risk factors of lymph node metastasis in patients with early gastric cancer and establish a risk score model to guide the selection of optimal treatment.

METHODS

The clinicopathological characteristics of 351 patients with early gastric cancer from January 2016 to December 2018 were reviewed retrospectively. On the basis of the independent risk factors determined by multivariate binary logistic regression analysis, we established a risk score model for predicting lymph node metastasis and then verified it. The receiver operating characteristic curves were plotted using the test and validation sets. The area under the receiver operating characteristic curve was used to assess the discriminant ability of the model.

RESULTS

Lymph node metastasis was observed in 10.5% (37/351) of early gastric cancer cases. Patients with early gastric cancer were grouped based on the independent risk factors for lymph node metastasis (tumor size, depth, histological type, and lymphovascular involvement) determined by multivariate analysis. A 7-point risk score model was established to predict the risk of lymph node metastasis. The area under the receiver operating characteristic curve in the development and validation sets were 0.839 (95% confidence interval, 0.769%-0.910%) and 0.820 (95% confidence interval, 0.711%-0.930%), respectively.

CONCLUSION

A feasible risk score model for lymph node metastasis was established to guide the optimal treatment of patients with early gastric cancer early gastric cancer.

Computer-assisted quantification of tumor-associated collagen signatures to improve the prognosis prediction of breast cancer

BACKGROUND

Collagen fibers play an important role in tumor initiation, progression, and invasion. Our previous research has already shown that large-scale tumor-associated collagen signatures (TACS) are powerful prognostic biomarkers independent of clinicopathological factors in invasive breast cancer. However, they are observed on a macroscale and are more suitable for identifying high-risk patients. It is necessary to investigate the effect of the corresponding microscopic features of TACS so as to more accurately and comprehensively predict the prognosis of breast cancer patients.

METHODS

In this retrospective and multicenter study, we included 942 invasive breast cancer patients in both a training cohort (n = 355) and an internal validation cohort (n = 334) from one clinical center and in an external validation cohort (n = 253) from a different clinical center. TACS corresponding microscopic features (TCMFs) were firstly extracted from multiphoton images for each patient, and then least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust features to build a TCMF-score. Finally, the Cox proportional hazard regression analysis was used to evaluate the association of TCMF-score with disease-free survival (DFS).

RESULTS

TCMF-score is significantly associated with DFS in univariate Cox proportional hazard regression analysis. After adjusting for clinical variables by multivariate Cox regression analysis, the TCMF-score remains an independent prognostic indicator. Remarkably, the TCMF model performs better than the clinical (CLI) model in the three cohorts and is particularly outstanding in the ER-positive and lower-risk subgroups. By contrast, the TACS model is more suitable for the ER-negative and higher-risk subgroups. When the TACS and TCMF are combined, they could complement each other and perform well in all patients. As expected, the full model (CLI+TCMF+TACS) achieves the best performance (AUC 0.905, [0.873-0.938]; 0.896, [0.860-0.931]; 0.882, [0.840-0.925] in the three cohorts).

CONCLUSION

These results demonstrate that the TCMF-score is an independent prognostic factor for breast cancer, and the increased prognostic performance (TCMF+TACS-score) may help us develop more appropriate treatment protocols.

Association of Tumor-Associated Collagen Signature With Prognosis and Adjuvant Chemotherapy Benefits in Patients With Gastric Cancer

IMPORTANCE

The current TNM staging system provides limited information for prognosis prediction and adjuvant chemotherapy benefits for patients with gastric cancer (GC).

OBJECTIVE

To develop a tumor-associated collagen signature of GC (TACSGC) in the tumor microenvironment to predict prognosis and adjuvant chemotherapy benefits in patients with GC.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study included a training cohort of 294 consecutive patients treated between January 1, 2012, and December 31, 2013, from Nanfang Hospital, Southern Medical University, People's Republic of China, and a validation cohort of 225 consecutive patients treated between October 1, 2010, and December 31, 2012, from Fujian Provincial Cancer Hospital, Fujian Medical University, People's Republic of China. In total, 146 collagen features in the tumor microenvironment were extracted with multiphoton imaging. A TACSGC was then constructed using the least absolute shrinkage and selection operator Cox proportional hazards regression model in the training cohort. Data analysis was conducted from October 1, 2020, to April 30, 2021.

MAIN OUTCOMES AND MEASURES

The association of TACSGC with disease-free survival (DFS) and overall survival (OS) was assessed. An independent external cohort was included to validate the results. Interactions between TACSGC and adjuvant chemotherapy were calculated.

RESULTS

This study included 519 patients (median age, 57 years [IQR, 49-65 years]; 360 [69.4%] male). A 9 feature-based TACSGC was built. A higher TACSGC level was significantly associated with worse DFS and OS in both the training (DFS: hazard ratio [HR], 3.57 [95% CI, 2.45-5.20]; OS: HR, 3.54 [95% CI, 2.41-5.20]) and validation (DFS: HR, 3.10 [95% CI, 2.26-4.27]; OS: HR, 3.24 [95% CI, 2.33-4.50]) cohorts (continuous variable, P < .001 for all comparisons). Multivariable analyses found that carbohydrate antigen 19-9, depth of invasion, lymph node metastasis, distant metastasis, and TACSGC were independent prognostic predictors of GC, and 2 integrated nomograms that included the 5 predictors were established for predicting DFS and OS. Compared with clinicopathological models that included only the 4 clinicopathological predictors, the integrated nomograms yielded an improved discrimination for prognosis prediction in a C index comparison (training cohort: DFS, 0.80 [95% CI, 0.73-0.88] vs 0.78 [95% CI, 0.71-0.85], P = .03; OS, 0.81 [95% CI, 0.75-0.88] vs 0.80 [95% CI, 0.73-0.86], P = .03; validation cohort: DFS, 0.78 [95% CI, 0.70-0.87] vs 0.76 [95% CI, 0.67-0.84], P = .006; OS, 0.78 [95% CI, 0.69-0.86] vs 0.75 [95% CI, 0.67-0.84], P = .002). Patients with stage II and III GC and low TACSGC levels rather than high TACSGC levels had a favorable response to adjuvant chemotherapy (DFS: HR, 0.65 [95% CI, 0.43-0.96]; P = .03; OS: HR, 0.55 [95% CI, 0.36-0.82]; P = .004; dichotomized variable, P < .001 for interaction for both comparisons).

CONCLUSIONS AND RELEVANCE

The findings suggest that TACSGC provides additional prognostic information for patients with GC and may distinguish patients with stage II and III disease who are more likely to derive benefits from adjuvant chemotherapy.

Multiphoton Microscopy for Identifying Collagen Signatures Associated with Biochemical Recurrence in Prostate Cancer Patients

Prostate cancer is a heterogeneous disease that remains dormant for long periods or acts aggressively with poor clinical outcomes. Identifying aggressive prostate tumor behavior using current glandular-focused histopathological criteria is challenging. Recent evidence has implicated the stroma in modulating prostate tumor behavior and in predicting post-surgical outcomes. However, the emergence of stromal signatures has been limited, due in part to the lack of adoption of imaging modalities for stromal-specific profiling. Herein, label-free multiphoton microscopy (MPM), with its ability to image tissue with stromal-specific contrast, is used to identify prostate stromal features associated with aggressive tumor behavior and clinical outcome. MPM was performed on unstained prostatectomy specimens from 59 patients and on biopsy specimens from 17 patients with known post-surgery recurrence status. MPM-identified collagen content, organization, and morphological tumor signatures were extracted for each patient and screened for association with recurrent disease. Compared to tumors from patients whose disease did not recur, tumors from patients with recurrent disease exhibited higher MPM-identified collagen amount and collagen fiber intensity signal and width. Our study shows an association between MPM-identified stromal collagen features of prostate tumors and post-surgical disease recurrence, suggesting their potential for prostate cancer risk assessment.

Nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to predict the histologic grade in breast cancer

The purpose of this study is to develop and validate a new nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to differentiate tumor grade in patients with invasive breast cancer. A total of 543 patients were included in this study. We used computer-generated random numbers to assign 328 of these patients to the training cohort and 215 patients to the validation cohort. Macroscopic tumor-associated collagen signatures (TACS1-8) were obtained by multiphoton microscopy at the invasion front and inside of the breast primary tumor. TACS corresponding microscopic features (TCMF) including morphology and texture features were extracted from the segmented regions of interest using Matlab 2016b. Using ridge regression analysis, we obtained a TACS-score for each patient based on the combined TACS1-8, and the least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust TCMF features to build a TCMF-score. Univariate logistic regression analysis demonstrates that the TACS-score and TCMF-score are significantly associated with histologic grade (odds ratio, 2.994; 95% CI, 2.013-4.452; P < 0.001; 4.245, 2.876-6.264, P < 0.001 in the training cohort). The nomogram (collagen) model combining the TACS-score and TCMF-score could stratify patients into Grade1 and Grade2/3 groups with the AUC of 0.859 and 0.863 in the training and validation cohorts. The predictive performance can be further improved by combining the clinical factors, achieving the AUC of 0.874 in both data cohorts. The nomogram model combining the TACS-score and TCMF-score can be useful in differentiating breast tumor patients with Grade1 and Grade2/3.

Development and Validation of a Nomogram based on cell growth-related Biomarkers for Oral Squamous Cell Carcinoma

Purpose: We aimed to develop a prognostic nomogram based on immunohistochemistry (IHC) biomarkers of patients with oral squamous cell carcinoma (OSCC). Methods: A total of 294 patients were enrolled in the study. The least absolute shrinkage and selection operator (LASSO) Cox regression model was performed to develop a combined IHC score (IHCs) classifier. Results: Five biomarkers, specifically c-Met, Vimentin, HIF-2α, VEGF-c, and Bcl-2 were extracted. Then, an IHCs classifier was developed, and patients were stratified into high- and low-IHCs groups. In the training cohort, the 5-year overall survival (OS) was 62.1% in low-IHCs group and 28.2% in high-IHCs group (P<0.001). The 5-year OS was 68.6% for the low-IHCs group and 28.4% for the high-IHCs group in the validation cohort (P<0.001). The area under the ROC curve (AUROC) of the combination of the IHCs classifier and TNM stage was 0.746 (95% CI: 0.658-0.833) in the training cohort and 0.735 (95% CI: 0.651-0.818) in the validation cohort, respectively. Conclusions: The nomogram could effectively predict the prognosis for patients with OSCC and may be employed as a potential tool to guide the individual decision-making process.

A Nomogram Based on a Collagen Feature Support Vector Machine for Predicting the Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients

BACKGROUND

The relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients.

METHODS

In this multicenter, retrospective analysis, 428 patients were included and randomly divided into a training cohort (299 patients) and validation cohort (129 patients) [7:3 ratio]. A total of 11 CFs were extracted from a multiphoton image of pretreatment biopsy, and a support vector machine (SVM) was then used to construct a CFs-SVM classifier. A prediction model was developed and presented with a nomogram using multivariable analysis. Further validation of the nomogram was performed in the validation cohort.

RESULTS

The CFs-SVM classifier, which integrated collagen area, straightness, and crosslink density, was significantly associated with treatment response. Predictors contained in the nomogram included the CFs-SVM classifier and clinicopathological characteristics by multivariable analysis. The CFs nomogram demonstrated good discrimination, with area under the receiver operating characteristic curves (AUROCs) of 0.834 in the training cohort and 0.854 in the validation cohort. Decision curve analysis indicated that the CFs nomogram was clinically useful. Moreover, compared with the traditional clinicopathological model, the CFs nomogram showed more powerful discrimination in determining the response to nCRT.

CONCLUSIONS

The CFs-SVM classifier based on CFs in the tumor microenvironment is associated with treatment response, and the CFs nomogram integrating the CFs-SVM classifier and clinicopathological characteristics is useful for individualized prediction of the treatment response to nCRT among LARC patients.

Prolyl-4-Hydroxylase α Subunit 2 as a Novel Potential Biomarker for Predicting the Prognosis of Epithelial Ovarian Carcinoma

Objective

Epithelial ovarian cancer (EOC) is one of the leading causes of death worldwide. The aim of this study was to explore the prognostic significance of prolyl-4-hydroxylase α subunit 2 (P4HA2) in patients with EOC.

Patients and Methods

A total of 217 clinical samples (EOC tissues, 167 cases; normal ovarian, 50 cases) were collected and pathologically confirmed using hematoxylin and eosin (H&E) staining. P4HA2 expression in clinical samples was stained by immunohistochemistry (IHC). Relationship between P4HA2 expression and clinicopathological characteristics of EOC patients were analyzed using chi-square test. The differential expression of targets was analyzed in Oncomine database. The prognostic value of P4HA2 was investigated in clinical EOC patients and Kaplan–Meier (KM) Plotter database.

Results

IHC staining showed that P4HA2 was significantly up-regulated in EOC tissues, compared to the normal tissues. Two databases retrieved from Oncomine database further confirmed the up-regulation P4HA2 in EOC. Chi-square test demonstrated that P4HA2 expression was associated with clinical stage (p=0.036), tumor grade (p<0.001), and residual disease (p=0.022). Both in clinical samples and KM Plotter database, high P4HA2 expression was significantly associated with worse progression-free survival (PFS) and overall survival (OS). Cox’s proportional hazards regression analysis suggested that high P4HA2 expression were independent risk factors for the survival of EOC patients. Besides, we confirmed the positive correlation between P4HA2 and COL1A1 expression. Moreover, COL1A1 was found to be up-regulated in EOC and also associated with short PFS and OS.

Conclusion

The present study preliminarily proved that P4HA2 expression was associated with clinical outcome in EOC patients. P4HA2 might be a prognostic factor for EOC progression, and has the potential to be a valuable therapeutic target for EOC.

Association of the Collagen Signature in the Tumor Microenvironment With Recurrence and Survival of Patients With T4N0M0 Colon Cancer

BACKGROUND

The current clinicopathological risk factors do not accurately predict disease recurrence in patients with T4N0M0 colon cancer. We hypothesized that the collagen signature combined with clinicopathological risk factors (new model) had a better prognostic value than clinicopathological risk factors alone (clinicopathological model).

OBJECTIVE

This study aimed to establish a collagen signature in the tumor microenvironment and to validate its role in predicting the recurrence of T4N0M0 colon cancer.

DESIGN

This was a retrospective study.

SETTINGS

This study took place at a tertiary medical center.

PATIENTS

Patients with T4N0M0 colon cancer who underwent surgery at our center between 2009 and 2015 (n = 416) were included.

INTERVENTION

A total of 142 collagen features were analyzed in the tumor microenvironment in specimens of colon cancer by using second-harmonic generation imaging. A collagen signature was constructed using a least-absolute shrinkage and selection operator Cox regression model.

MAIN OUTCOME MEASURES

The primary outcomes measured were disease-free survival and overall survival.

RESULTS

The training and testing cohorts consisted of 291 and 125 randomly assigned samples, with recurrence rates of 19.9% and 22.4%. A 3-feature-based collagen signature predicted the recurrence risk at 1, 3, and 5 years, with the area under the receiver-operating characteristic curves of 0.808, 0.832, and 0.791 in the training cohort and 0.836, 0.807, and 0.794 in the testing cohort. Multivariate analysis revealed that the collagen signature could independently predict the disease-free survival (HR = 7.17, p < 0.001) and overall survival rates (HR = 5.03, p < 0.001). The new model had a better prognostic value than the clinicopathological model, which included 4 clinicopathological risk factors: obstruction or perforation, lymphovascular invasion, tumor budding, and no chemotherapy.

LIMITATIONS

This study was limited by its retrospective design.

CONCLUSIONS

The collagen signature in the tumor microenvironment may be a new prognostic marker that can effectively predict the recurrence and survival of patients with T4N0M0 colon cancer. See Video Abstract at http://links.lww.com/DCR/B503. ASOCIACIÓN DE LA RÚBRICA DE COLÁGENO EN EL MICROAMBIENTE TUMORAL CON LA RECIDIVA Y LA SOBREVIDA DE PACIENTES CON CÁNCER DE COLON T4N0M0: Los factores de riesgo clínico-patológicos actuales no predicen con precisión la recurrencia de la enfermedad en pacientes con cáncer de colon estadío T4N0M0. Presumimos que la rúbrica de colágeno combinada con factores de riesgo clínico-patológicos (nuevo modelo) tendrían un mejor valor pronóstico que los factores de riesgo clínico-patológicos solos (modelo clínico-patológico).El establecer una rúbrica de colágeno en el microambiente tumoral y validar su papel en la predicción de la recidiva del cáncer de colon T4N0M0.Estudio retrospectivo.Investigación llevada a cabo en un centro médico terciario.Se incluyeron pacientes con cáncer de colon T4N0M0 operados en nuestro centro entre 2009 y 2015 (n = 416).Se analizaron un total de 142 características de colágeno en el microambiente tumoral en muestras de cáncer de colon utilizando imágenes de segunda generación armónica. Se construyó una rúbrica de colágeno utilizando un modelo de regresión LASSO Cox.Sobrevida libre de enfermedad y sobrevida global.Las cohortes de entrenamiento y prueba consistieron en 291 y 125 muestras asignadas al azar, con tasas de recurrencia del 19,9% y 22,4%, respectivamente. La rúbrica del colágeno basada en 3 características predijo el riesgo de recurrencia a 1, 3 y 5 años, con el área bajo las curvas características operativas del receptor de 0,808, 0,832 y 0,791 en la cohorte de entrenamiento y 0,836, 0,807 y 0,794 en la cohorte de prueba, respectivamente. El análisis multivariado reveló que la firma de colágeno podría predecir de forma independiente la supervivencia libre de enfermedad (HR = 7,17, p <0,001) y las tasas de sobrevida general (HR = 5,03, p <0,001). El nuevo modelo tuvo un mejor valor pronóstico que el modelo clínico-patológico, que incluyó cuatro factores de riesgo clínico-patológicos: obstrucción o perforación, invasión linfovascular, gemación tumoral y ausencia de quimioterapia.Este estudio estuvo limitado por su diseño retrospectivo.La rúbrica de colágeno en el microambiente tumoral puede ser un nuevo marcador pronóstico para predecir eficazmente la recurrencia y la subrevida de los pacientes con cáncer de colon T4N0M0. Consulte Video Resumen en http://links.lww.com/DCR/B503. (Traducción-Dr. Xavier Delgadillo).