P-TNM staging system for colon cancer: combination of P-stage and AJCC TNM staging system for improving prognostic prediction and clinical management

An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology

The application of deep learning to spatial transcriptomics (ST) can reveal relationships between gene expression and tissue architecture. Prior work has demonstrated that inferring gene expression from tissue histomorphology can discern these spatial molecular markers to enable population scale studies, reducing the fiscal barriers associated with large-scale spatial profiling. However, while most improvements in algorithmic performance have focused on improving model architectures, little is known about how the quality of tissue preparation and imaging can affect deep learning model training for spatial inference from morphology and its potential for widespread clinical adoption. Prior studies for ST inference from histology typically utilize manually stained frozen sections with imaging on non-clinical grade scanners. Training such models on ST cohorts is also costly. We hypothesize that adopting tissue processing and imaging practices that mirror standards for clinical implementation (permanent sections, automated tissue staining, and clinical grade scanning) can significantly improve model performance. An enhanced specimen processing and imaging protocol was developed for deep learning-based ST inference from morphology. This protocol featured the Visium CytAssist assay to permit automated hematoxylin and eosin staining (e.g. Leica Bond), 40×-resolution imaging, and joining of multiple patients' tissue sections per capture area prior to ST profiling. Using a cohort of 13 pathologic T Stage-III stage colorectal cancer patients, we compared the performance of models trained on slide prepared using enhanced versus traditional (i.e. manual staining and low-resolution imaging) protocols. Leveraging Inceptionv3 neural networks, we predicted gene expression across serial, histologically-matched tissue sections using whole slide images (WSI) from both protocols. The data Shapley was used to quantify and compare marginal performance gains on a patient-by-patient basis attributed to using the enhanced protocol versus the actual costs of spatial profiling. Findings indicate that training and validating on WSI acquired through the enhanced protocol as opposed to the traditional method resulted in improved performance at lower fiscal cost. In the realm of ST, the enhancement of deep learning architectures frequently captures the spotlight; however, the significance of specimen processing and imaging is often understated. This research, informed through a game-theoretic lens, underscores the substantial impact that specimen preparation/imaging can have on spatial transcriptomic inference from morphology. It is essential to integrate such optimized processing protocols to facilitate the identification of prognostic markers at a larger scale.

Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity

Background

Spatial transcriptomics involves studying the spatial organization of gene expression within tissues, offering insights into the molecular diversity of tumors. While spatial gene expression is commonly amalgamated from 1-10 cells across 50-micron spots, recent methods have demonstrated the capability to disaggregate this information at subspot resolution by leveraging both expression and histological patterns. However, elucidating such information from histology alone presents a significant challenge but if solved can better permit spatial molecular analysis at cellular resolution for instances where Visium data is not available, reducing study costs. This study explores integrating single-cell histological and transcriptomic data to infer spatial mRNA expression patterns in whole slide images collected from a cohort of stage pT3 colorectal cancer patients. A cell graph neural network algorithm was developed to align histological information extracted from detected cells with single cell RNA patterns through optimal transport methods, facilitating the analysis of cellular groupings and gene relationships. This approach leveraged spot-level expression as an intermediary to co-map histological and transcriptomic information at the single-cell level.

Results

Our study demonstrated that single-cell transcriptional heterogeneity within a spot could be predicted from histological markers extracted from cells detected within a spot. Furthermore, our model exhibited proficiency in delineating overarching gene expression patterns across whole-slide images. This approach compared favorably to traditional patch-based computer vision methods as well as other methods which did not incorporate single cell expression during the model fitting procedures. Topological nuances of single-cell expression within a Visium spot were preserved using the developed methodology.

Conclusion

This innovative approach augments the resolution of spatial molecular assays utilizing histology as a sole input through synergistic co-mapping of histological and transcriptomic datasets at the single-cell level, anchored by spatial transcriptomics. While initial results are promising, they warrant rigorous validation. This includes collaborating with pathologists for precise spatial identification of distinct cell types and utilizing sophisticated assays, such as Xenium, to attain deeper subcellular insights.

The Overlooked Role of Specimen Preparation in Bolstering Deep Learning-Enhanced Spatial Transcriptomics Workflows

The application of deep learning methods to spatial transcriptomics has shown promise in unraveling the complex relationships between gene expression patterns and tissue architecture as they pertain to various pathological conditions. Deep learning methods that can infer gene expression patterns directly from tissue histomorphology can expand the capability to discern spatial molecular markers within tissue slides. However, current methods utilizing these techniques are plagued by substantial variability in tissue preparation and characteristics, which can hinder the broader adoption of these tools. Furthermore, training deep learning models using spatial transcriptomics on small study cohorts remains a costly endeavor. Necessitating novel tissue preparation processes enhance assay reliability, resolution, and scalability. This study investigated the impact of an enhanced specimen processing workflow for facilitating a deep learning-based spatial transcriptomics assessment. The enhanced workflow leveraged the flexibility of the Visium CytAssist assay to permit automated H&E staining (e.g., Leica Bond) of tissue slides, whole-slide imaging at 40x-resolution, and multiplexing of tissue sections from multiple patients within individual capture areas for spatial transcriptomics profiling. Using a cohort of thirteen pT3 stage colorectal cancer (CRC) patients, we compared the efficacy of deep learning models trained on slide prepared using an enhanced workflow as compared to the traditional workflow which leverages manual tissue staining and standard imaging of tissue slides. Leveraging Inceptionv3 neural networks, we aimed to predict gene expression patterns across matched serial tissue sections, each stemming from a distinct workflow but aligned based on persistent histological structures. Findings indicate that the enhanced workflow considerably outperformed the traditional spatial transcriptomics workflow. Gene expression profiles predicted from enhanced tissue slides also yielded expression patterns more topologically consistent with the ground truth. This led to enhanced statistical precision in pinpointing biomarkers associated with distinct spatial structures. These insights can potentially elevate diagnostic and prognostic biomarker detection by broadening the range of spatial molecular markers linked to metastasis and recurrence. Future endeavors will further explore these findings to enrich our comprehension of various diseases and uncover molecular pathways with greater nuance. Combining deep learning with spatial transcriptomics provides a compelling avenue to enrich our understanding of tumor biology and improve clinical outcomes. For results of the highest fidelity, however, effective specimen processing is crucial, and fostering collaboration between histotechnicians, pathologists, and genomics specialists is essential to herald this new era in spatial transcriptomics-driven cancer research.

Modeling the survival of colorectal cancer patients based on colonoscopic features in a feature ensemble vision transformer

The prognosis of patients with colorectal cancer (CRC) mostly relies on the classic tumor node metastasis (TNM) staging classification. A more accurate and convenient prediction model would provide a better prognosis and assist in treatment. From May 2014 to December 2017, patients who underwent an operation for CRC were enrolled. The proposed feature ensemble vision transformer (FEViT) used ensemble classifiers to benefit the combinations of relevant colonoscopy features from the pretrained vision transformer and clinical features, including sex, age, family history of CRC, and tumor location, to establish the prognostic model. A total of 1729 colonoscopy images were enrolled in the current retrospective study. For the prediction of patient survival, FEViT achieved an accuracy of 94 % with an area under the receiver operating characteristic curve of 0.93, which was better than the TNM staging classification (90 %, 0.83) in the experiment. FEViT reduced the limited receptive field and gradient disappearance in the conventional convolutional neural network and was a relatively effective and efficient procedure. The promising accuracy of FEViT in modeling survival makes the prognosis of CRC patients more predictable and practical.

In silico development and in vitro validation of a novel five-gene signature for prognostic prediction in colon cancer

Colon cancer is one of the most common cancers in digestive system, and its prognosis remains unsatisfactory. Therefore, this study aimed to identify gene signatures that could effectively predict the prognosis of colon cancer patients by examining the data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. LASSO-Cox regression analysis generated a five-gene signature (DCBLD2, RAB11FIP1, CTLA4, HOXC6 and KRT6A) that was associated with patient survival in the TCGA cohort. The prognostic value of this gene signature was further validated in two independent GEO datasets. GO enrichment revealed that the function of this gene signature was mainly associated with extracellular matrix organization, collagen-containing extracellular matrix, and extracellular matrix structural constituent. Moreover, a nomogram was established to facilitate the clinical application of this signature. The relationships among the gene signature, mutational landscape and immune infiltration cells were also investigated. Importantly, this gene signature also reliably predicted the overall survival in IMvigor210 anti-PD-L1 cohort. In addition to the bioinformatics study, we also conducted a series of in vitro experiments to demonstrate the effect of the signature genes on the proliferation, migration, and invasion of colon cancer cells. Collectively, our data demonstrated that this five-gene signature might serve as a promising prognostic biomarker and shed light on the development of personalized treatment in colon cancer patients.

Use of Personalized Biomarkers in Metastatic Colorectal Cancer and the Impact of AI

Colorectal cancer is a major cause of cancer-related death worldwide and is correlated with genetic and epigenetic alterations in the colonic epithelium. Genetic changes play a major role in the pathophysiology of colorectal cancer through the development of gene mutations, but recent research has shown an important role for epigenetic alterations. In this review, we try to describe the current knowledge about epigenetic alterations, including DNA methylation and histone modifications, as well as the role of non-coding RNAs as epigenetic regulators and the prognostic and predictive biomarkers in metastatic colorectal disease that can allow increases in the effectiveness of treatments. Additionally, the intestinal microbiota's composition can be an important biomarker for the response to strategies based on the immunotherapy of CRC. The identification of biomarkers in mCRC can be enhanced by developing artificial intelligence programs. We present the actual models that implement AI technology as a bridge connecting ncRNAs with tumors and conducted some experiments to improve the quality of the model used as well as the speed of the model that provides answers to users. In order to carry out this task, we implemented six algorithms: the naive Bayes classifier, the random forest classifier, the decision tree classifier, gradient boosted trees, logistic regression and SVM.

Construction of a new clinical staging system for colorectal cancer based on the lymph node ratio: A validation study

Aim

This study aims to construct a new staging system for colorectal cancer (CRC) based on the lymph node ratio (LNR) as a supplement to the American Joint Committee on Cancer (AJCC) tumor node metastasis (TNM) staging system for predicting the prognosis of CRC patients with <12 lymph nodes.

Methods

The data of 26,695 CRC patients with <12 lymph nodes were extracted from the Surveillance, Epidemiology, and End Results (SEER) database as a training set. A total of 635 CRC patients were also enrolled from Northern Jiangsu People's Hospital affiliated with Yangzhou University as an independent validation set. Classification and regression tree analysis was used to obtain the LNR cutoff value. Survival curves were estimated using the Kaplan–Meier method, and the log-rank test was used for comparisons of differences among the survival curves. The monotonic decreasing trend of the overall survival curve in the staging system was expressed by the linear correlation degree R.

Results

The 5-year survival rates of patients in the training set based on the AJCC staging system from stage I to stage IV were 75.6% (95%CI: 74.4–76.8), 59.8% (95%CI: 58.6–61.0), 42.1% (95%CI: 34.5–49.7), 33.2% (95%CI: 24.6–41.8), 72.0% (95%CI: 69.1–74.9), 48.8% (95%CI: 47.4–50.2), 26.5% (95%CI: 23.0–30.0), and 11.3% (95%CI: 10.3–12.3). The 5-year survival rates of patients in the training set from stage I to stage IIIC were 80.4%, 72.9%, 59.8%, 48.4%, 32.5%, and 15.0%, according to the TNM + LNR (TNRM) staging system. According to the AJCC staging system, the 5-year survival rates of patients in the validation set from stage I to stage IIIC were 91.3%, 90.8%, 72.6%, 61.3%, 72.4%, 58.1%, and 32.8%. Based on the TNRM staging system, the 5-year survival rates of patients in the validation set from stage I to stage IIIC were 99.2%, 90.5%, 81.4%, 78.6%, 60.2%, and 35.8%.

Conclusion

The TNRM staging system successfully eliminated “survival paradox” in the AJCC staging system, which might be superior to the AJCC staging system.

Identification and Validation of an Endoplasmic Reticulum Stress-Related lncRNA Signature for Colon Adenocarcinoma Patients

Purpose

Methods

Results

Conclusion

Addition of V-Stage to Conventional TNM Staging to Create the TNVM Staging System for Accurate Prediction of Prognosis in Colon Cancer: A Multi-Institutional Retrospective Cohort Study

We evaluated the prognostic impact of vascular invasion (VI) compared with nodal (N) stage and developed a new staging system including VI in colon cancer. Patients who underwent curative resection with stage II-III colon cancer were assigned to VI and non-VI groups; the latter was subclassified as N0, N1, and N2; a new TNVM staging was devised by adding the V-stage. Among the 2243 study participants, the VI group independently showed worse oncological outcomes than the N1 group (disease-free survival (DFS), hazard-ratio (HR) 1.704, 1.267–2.291; overall survival (OS), HR 2.301, 1.582–3.348). The 5-year DFS in the VI group was 63.4% [N1b (74.6%), p = 0.003; N2a (69.7%), p = 0.126; and N2b (56.8%), p = 0.276], and the 5-year OS was 76.6% [N1b (84.9%), p = 0.004; N2a (83.0%), p = 0.047; and N2b (76.1%), p = 0.906]. Thus, we considered VI as N2a in TNVM staging; 78 patients (3.5%) underwent upstaging. The 5-year OS rates of stage IIB and IIC increased from 88.6% and 65.9% in TNM staging to 90.5% and 85.7% in TNVM staging, respectively. In stage II–III colon cancer, VI had a similar prognostic impact as the N2 stage without VI. The incorporation of the V-stage into the conventional TNM staging facilitates better prediction of prognosis.

Immune-related gene expression signatures in colorectal cancer

The immune system is crucial in regulating colorectal cancer (CRC) tumorigenesis. Identification of immune-related transcriptomic signatures derived from the peripheral blood of patients with CRC would provide insights into CRC pathogenesis, and suggest novel clues to potential immunotherapy strategies for the disease. The present study collected blood samples from 59 patients with CRC and 62 healthy control patients and performed whole blood gene expression profiling using microarray hybridization. Immune-related gene expression signatures for CRC were identified from immune gene datasets, and an algorithmic predictive model was constructed for distinguishing CRC from controls. Model performance was characterized using an area under the receiver operating characteristic curve (ROC AUC). Functional categories for CRC-specific gene expression signatures were determined using gene set enrichment analyses. A Kaplan-Meier plotter survival analysis was also performed for CRC-specific immune genes in order to characterize the association between gene expression and CRC prognosis. The present study identified five CRC-specific immune genes [protein phosphatase 3 regulatory subunit Bα (PPP3R1), amyloid β precursor protein, cathepsin H, proteasome activator subunit 4 and DEAD-Box Helicase 3 X-Linked]. A predictive model based on this five-gene panel showed good discriminatory power (independent test set sensitivity, 83.3%; specificity, 94.7%, accuracy, 89.2%; ROC AUC, 0.96). The candidate genes were involved in pathways associated with ‘adaptive immune responses’, ‘innate immune responses’ and ‘cytokine signaling’. The survival analysis found that a high level of PPP3R1 expression was associated with a poor CRC prognosis. The present study identified five CRC-specific immune genes that were potential diagnostic biomarkers for CRC. The biological function analysis indicated a close association between CRC pathogenesis and the immune system, and may reveal more information about the immunogenic and pathogenic mechanisms driving CRC in the future. Overall, the association between PPP3R1 expression and survival of patients with CRC revealed potential new targets for CRC immunotherapy.

A New Oxaliplatin Resistance-Related Gene Signature With Strong Predicting Ability in Colon Cancer Identified by Comprehensive Profiling

Numerous colon cancer cases are resistant to chemotherapy based on oxaliplatin and suffer from relapse. A number of survival- and prognosis-related biomarkers have been identified based on database mining for patients who develop drug resistance, but the single individual gene biomarker cannot attain high specificity and sensitivity in prognosis prediction. This work was conducted aiming to establish a new gene signature using oxaliplatin resistance-related genes to predict the prognosis for colon cancer. To this end, we downloaded gene expression profile data of cell lines that are resistant and not resistant to oxaliplatin from the Gene Expression Omnibus (GEO) database. Altogether, 495 oxaliplatin resistance-related genes were searched by weighted gene co-expression network analysis (WGCNA) and differential expression analysis. As suggested by functional analysis, the above genes were mostly enriched into cell adhesion and immune processes. Besides, a signature was built based on four oxaliplatin resistance-related genes selected from the training set to predict the overall survival (OS) by stepwise regression and least absolute shrinkage and selection operator (LASSO) Cox analysis. Relative to the low risk score group, the high risk score group had dismal OS (P < 0.0001). Moreover, the area under the curve (AUC) value regarding the 5-year OS was 0.72, indicating that the risk score was accurate in the prediction of OS for colon cancer patients (AUC >0.7). Additionally, multivariate Cox regression suggested that the signature constructed based on four oxaliplatin resistance-related genes predicted the prognosis for colon cancer cases [hazard ratio (HR), 2.77; 95% CI, 2.03–3.78; P < 0.001]. Finally, external test sets were utilized to further validate the stability and accuracy of oxaliplatin resistance-related gene signature for prognosis of colon cancer patients. To sum up, this study establishes a signature based on four oxaliplatin resistance-related genes for predicting the survival of colon cancer patients, which sheds more light on the mechanisms of oxaliplatin resistance and helps identify colon cancer cases with a dismal prognostic outcome.

Perineural Invasion Is a Strong Prognostic Factor but Not a Predictive Factor of Response to Adjuvant Chemotherapy in Node-Negative Colon Cancer

Purpose

To validate the prognostic value and evaluate the predictive value of response to adjuvant chemotherapy of perineural invasion (PNI) in node-negative colon cancer using the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) 18 tumor registry database.

Methods

Patients diagnosed with colon cancer from the SEER database between January 1, 2010 and December 31, 2015 were identified. Chi-square analysis was performed to evaluate different demographic and clinical features of patients between PNI-negative (PNI (-)) and PNI-positive (PNI (+)) groups. Univariate and multivariate Cox proportional hazard regression models were built to examine the relationship of demographic and clinical features and survival outcomes with the hazard ratios (HRs) and 95% confidence intervals (CIs).

Results

In total, 57,255 node-negative colon cancer patients were extracted from the SEER database. The receipt of chemotherapy was not an independent prognostic factor for CSS in T3 colon cancer with or without the presence of PNI (P >0.05). The receipt of chemotherapy was independently associated with 34.0% decreased risk of cancer-specific mortality compared with those without the receipt of chemotherapy in T4 colon cancer without the presence of PNI (HR = 0.660, 95%CI = 0.559-0.779, P <0.001); the receipt of chemotherapy was independently associated with 36.0% decreased risk of cancer-specific mortality compared with those without the receipt of chemotherapy in T4 colon cancer with the presence of PNI (HR = 0.640, 95%CI = 0.438-0.935, P = 0.021).

Conclusions

The present study demonstrated the poor prognosis of PNI (+) in both stage I and II colon cancer. However, the presence of PNI was not a predictive factor of response to adjuvant chemotherapy in node-negative colon cancer.

Colorectal Cancer Surgery During the COVID-19 Pandemic: A Single Center Experience

BACKGROUND/AIM

A notable re-allocation of healthcare resources and specific clinical and organizational measures have been required to prevent COVID-19 infection among hospitalized patients and healthcare workers.

PATIENTS AND METHODS

From March 9th to May 9th 2020 we performed colorectal cancer elective surgery on 25 patients: a pre-hospital screening was carried out in order to avoid hospitalization of patients suspected of COVID-19 infection.

RESULTS

All patients (median age=76 years; range=37-88 years) were considered suitable for admission after telephone triage; the median interval between primary diagnosis and hospital admission was 23.1 days (range=1-55 days). The median hospitalization was 7.8 days (range=4-18 days). One COVID-19-associated death was reported.

CONCLUSION

Our experience demonstrates that safe colorectal cancer elective surgery can be performed during the pandemic COVID-19. Further consensus and guidelines to prevent diffusion of pandemic diseases among hospitalized patients and healthcare workers still need to be implemented.

SLC6A14, a Na+/Cl--coupled amino acid transporter, functions as a tumor promoter in colon and is a target for Wnt signaling

SLC6A14 is a Na⁺/Cl⁻-coupled transporter for neutral and cationic amino acids. It is expressed at basal levels in the normal colon but is up-regulated in colon cancer. However, the relevance of this up-regulation to cancer progression and the mechanisms involved in the up-regulation remain unknown. Here, we show that SLC6A14 is essential for colon cancer and that its up-regulation involves, at least partly, Wnt signaling. The up-regulation of the transporter is evident in most human colon cancer cell lines and also in a majority of patient-derived xenografts. These findings are supported by publicly available TCGA (The Cancer Genome Atlas) database. Treatment of colon cancer cells with α-methyltryptophan (α-MT), a blocker of SLC6A14, induces amino acid deprivation, decreases mTOR activity, increases autophagy, promotes apoptosis, and suppresses cell proliferation and invasion. In xenograft and syngeneic mouse tumor models, silencing of SLC6A14 by shRNA or blocking its function by α-MT reduces tumor growth. Similarly, the deletion of Slc6a14 in mice protects against colon cancer in two different experimental models (inflammation-associated colon cancer and genetically driven colon cancer). In colon cancer cells, expression of the transporter is reduced by Wnt antagonist or by silencing of β-catenin whereas Wnt agonist or overexpression of β-catenin shows the opposite effect. Finally, SLC6A14 as a target for β-catenin is confirmed by chromatin immunoprecipitation. These studies demonstrate that SLC6A14 plays a critical role in the promotion of colon cancer and that its up-regulation in cancer involves Wnt signaling. These findings identify SLC6A14 as a promising drug target for the treatment of colon cancer.

Yellow-Gold Polarized Light Microscopy May Improve Accuracy of Pathological Staging of Colorectal Adenocarcinoma

Introduction Polarized light (PL) has been used in pathology for multiple reasons, including the demonstration of foreign bodies, the evaluation of crystals, and the demonstration of fibrosis. We incidentally found that yellow-gold polarization routinely occurs surrounding desmoplastic scar tissue abutting the invasive glands of colonic adenocarcinoma. We hypothesized that evaluating the use of polarized light over a series of invasive adenocarcinomas of the large intestine might produce evidence of its utility. Methods Large intestinal resections with invasive adenocarcinoma were reviewed with yellow-gold polarized light microscopy by two surgical pathologists postoperatively between January 2017 and March 2019. Specimens were examined under yellow-gold polarized light to evaluate invasion from the submucosa into the muscularis propria, from the muscularis propria into pericolic fat, and to the serosa. The diagnosed location, T stage, history of radiotherapy, mucinous features, and grade were recorded. Photographs were taken when images were deemed to be of value. The two-tailed Fisher's exact test was used to compare the invasion detection of the tumor into fat in scar tissue in colorectal cancer. Results A total of 75 large intestinal resections with invasive adenocarcinoma from 75 patients were accessioned. Concerning the initial stage, three (4%) were T1, nine (12%) were T2, 46 (61%) were T3, and 17 (22%) were T4. A history of previous radiation treatment was seen in 10 (13%). Two (2%) were poorly differentiated. Nine (12%) were mucinous carcinomas; mucinous areas were seen to pose difficulty in 12 (16%). Overall, one out of nine, initially staged as T2, was upstaged to T3 (11%), with the addition of yellow-gold polarized light microscopy. One tumor was downstaged from T2 to T1 (11%). For many T2 and T3 tumors, invasion into the muscularis propria was better defined by yellow-gold polarized light. Conclusion Yellow-gold polarized light microscopy may be a useful adjunct to conventional microscopy in more precisely staged pathological colorectal cancer specimens.

DNA mismatch repair is not disrupted in stage 0 colorectal cancer resected using endoscopic submucosal dissection

The frequency of deficient mismatch repair (dMMR) or microsatellite instability-high colorectal cancer (CRC) is estimated to be ~15% of all patients with CRC; however, the patients reported are limited to surgical cases, and the frequency of patients exhibiting stage 0 disease is not considered, despite the currently increasing use of endoscopic techniques to cure a number of these patients. In the present study, the DNA MMR status for stage 0 patients with CRC treated using endoscopic submucosal dissection or endoscopic mucosal resection was analyzed via immunohistochemical staining of four types of proteins, namely MutL homolog 1 (MLH1), MutS homolog 2 (MSH2), MSH6 and PMS1 homolog 2 MMR system component, in adenocarcinoma specimens. Notably, none of the endoscopically resected specimens exhibited dMMR among the 41 patients diagnosed with stage 0 CRC. Since tumors harboring dMMR progress more rapidly than tumors with chromosomal instability, the present results highlight the importance of tumor resection during very early phases that exist before the promoter region of MLH1 becomes hypermethylated, resulting in a loss of DNA MMR function.

Lnc HAGLR Promotes Colon Cancer Progression Through Sponging miR-185-5p and Activating CDK4 and CDK6 in vitro and in vivo

BACKGROUND/AIM

LncRNA plays a key role in tumor progression. HAGLR functions as an oncogene in many cancers. However, the molecular mechanism of HAGLR in colon cancer is still unclear.

METHODS

qRT-PCR was used to measure the expression of HAGLR, miR-185-5p in colon cancer. The expression of CDK4 and CDK6 was detected by Western blot. CCK-8 assay, EdU staining, transwell and Annexin V-FITC/PI assay were used to analyze the effect of HAGLR and miR-185-5p on cell proliferation, invasion, migration and apoptosis. Bioinformatic analysis and luciferase were used to analyze the target genes of HAGLR and miR-185-5p. Nude mice were used to detect mouse tumor changes.

RESULTS

Compared with normal colon cancer tissues and cells, the expression of HAGLR was increased in colon cancer tissues and cells. In addition, the expression of HAGLR down-regulation inhibited the growth, migration, and invasion of colon cancer cells. MiR-185-5p was reduced in colon cancer, and CDK4 and CDK6 acted as target genes of miR-185-5p to regulate the progress of colon cancer. And CDK4 and CDK6 were predicted as downstream targets of miR-185-5p. Finally, it was demonstrated that HAGLR regulated tumor progression in vivo.

CONCLUSION

Lnc HAGLR promoted the development of colon cancer by miR-185-5p/CDK4/CDK6 axis, and lnc HAGLR might be potential target for colon cancer.

Recurrence Rate in a Patient Treated with Colon Resection Followed by Chemotherapy in Comparison to a Patient Treated with Colon Resection without Chemotherapy

Given that colon cancer is one of the most prevalent cancers worldwide, it is essential to employ strategies to try to reduce its incidence and recurrence rate. Though colon cancer is a sporadic disease in the vast majority of cases, multiple risk factors are linked to this disease, namely, obesity and cigarette smoking. Additionally, not many studies have been done in Saudi Arabia studying the recurrence rate of colon cancer. Therefore, we conducted a retrospective cohort study at King Khalid Hospital, King Abdulaziz Medical City, National Guard Health Affairs (NGHA), Jeddah, Saudi Arabia to investigate the recurrence rate of colon cancer in patients treated with complete colon resection followed by chemotherapy versus patients treated with colon resection alone via electronic and paper medical records. A total of 120 patients were included in this study; 61 were males (50.8%) and 59 were females (49.2%). According to our findings, the recurrence rate in patients who underwent surgical resection with adjuvant chemotherapy was 15.6% (n = 10), while the recurrence rate in patients with surgery alone was 21.4% (n = 12). Cancer recurrence is associated with significant morbidity and mortality. Therefore, further studies should be done to investigate the recurrence rate in patients with risk factors to identify and deal with the causes of recurrence.

Cost-effectiveness Evaluation of Targeted Surgical and Endoscopic Therapies for Early Colorectal Adenocarcinoma Based on Biomarker Profiles

IMPORTANCE

Colorectal cancer (CRC) is the second leading cause of cancer-related mortality in the United States. The prognosis for patients with CRC varies widely, but new prognostic biomarkers provide the opportunity to implement a more individualized approach to treatment selection.

OBJECTIVE

To assess the cost-effectiveness of 3 therapeutic strategies, namely, endoscopic therapy (ET), laparoscopic colectomy (LC), and open colectomy (OC), for patients with T1 CRC with biomarker profiles that prognosticate varying levels of tumor progression in the US payer perspective.

DESIGN, SETTING, AND PARTICIPANTS

In this economic evaluation study, a Markov model was developed for the cost-effectiveness analysis. Risks of all-cause mortality and recurrent cancer after ET, LC, or OC were estimated with a 35-year time horizon. Quality of life was based on EuroQoL 5 Dimensions scores reported in the published literature. Hospital and treatment costs reflected Medicare reimbursement rates. Deterministic and probabilistic sensitivity analyses were performed. Data from patients with T1 CRC and 6 biomarker profiles that included adenomatous polyposis coli (APC), TP53 and/or KRAS, or BRAFV600E were used as inputs for the model. Data analyses were conducted from February 27, 2019, to May 13, 2019.

EXPOSURES

Endoscopic therapy, LC, and OC.

MAIN OUTCOMES AND MEASURES

The primary outcomes were unadjusted life-years, quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio (ICER) between competing treatment strategies.

RESULTS

Endoscopic therapy had the highest QALYs and the lowest cost and was the dominant treatment strategy for T1 CRC with the following biomarker profiles: BRAFV600E, APC(1)/KRAS/TP53, APC(2) or APC(2)/KRAS or APC(2)/TP53, or APC(1) or APC(1)/KRAS or APC(1)/TP53. The QALYs gained ranged from 16.97 to 17.22, with costs between $68 902.75 and $77 784.53 in these subgroups. For the 2 more aggressive biomarker profiles with worse prognoses (APC(2)/KRAS/TP53 and APCwt [wild type]), LC was the most effective strategy (with 16.45 and 16.61 QALYs gained, respectively) but was not cost-effective. Laparoscopic colectomy cost $65 234.87 for APC(2)/KRAS/TP53 and $71 250.56 for APCwt, resulting in ICERs of $113 290 per QALY and $178 765 per QALY, respectively.

CONCLUSIONS AND RELEVANCE

This modeling analysis found that ET was the most effective strategy for patients with T1 CRC with less aggressive biomarker profiles. For patients with more aggressive profiles, LC was more effective but was costly, rendering ET the cost-effective option. This study highlights the potential utility of prognostic biomarkers in T1 CRC treatment selection.

Prognostic value of N1c in colorectal cancer: a large population-based study using propensity score matching

PURPOSE

We conducted this large population-based study to investigate the prognostic significance of N1c.

METHODS

Patients diagnosed with colorectal cancer from the surveillance, epidemiology, and end results (SEER) database between January 1, 2010, and December 31, 2010, were included in the sample. The primary outcome of interest used in our study was cause-specific survival (CSS). Cox proportional hazards models and Kaplan-Meier methods were used to evaluate the prognostic value of N1c. Propensity score matching (PSM) was implemented to reduce the possibility of selection bias using a logistic regression model.

RESULTS

A total of 19,991 patients diagnosed with colorectal cancer were identified from the SEER database. The median follow-up time of the whole cohort was 60 months (0-71 months). Multivariate Cox analysis showed that N1c was associated with significantly higher risk of colorectal cancer-specific mortality compared with N0 (HR = 1.962, 95%CI = 1.642 to 2.343, P < 0.001) and N1a (HR = 0.818, 95%CI = 0.678 to 0.987, P = 0.036); N1c was associated with significantly lower risk of colorectal cancer-specific mortality compared with N2a (HR = 1.296, 95%CI = 1.081 to 1.554, P = 0.005) and N2b (HR = 1.663, 95%CI = 1.391 to 1.989, P < 0.001). Yet the CSS difference between N1b and N1c did not achieve statistical difference (HR = 1.089, 95%CI = 0.909 to 1.304, P = 0.354).

CONCLUSIONS

The large population-based and propensity score-matched study with long follow-up time provides the first evidence that CSS difference between N1b and N1c does not achieve a statistical difference.

Integrative analysis from multi-centre studies identifies a function-derived personalized multi-gene signature of outcome in colorectal cancer

Colorectal cancer (CRC) is highly heterogeneous leading to variable prognosis and treatment responses. Therefore, it is necessary to explore novel personalized and reproducible prognostic signatures to aid clinical decision‐making. The present study combined large‐scale gene expression profiles and clinical data of 1828 patients with CRC from multi‐centre studies and identified a personalized gene prognostic signature consisting of 46 unique genes (called function‐derived personalized gene signature [FunPGS]) from an integrated statistics and function‐derived perspective. In the meta‐training and multiple independent validation cohorts, the FunPGS effectively discriminated patients with CRC with significantly different prognosis at the individual level and remained as an independent factor upon adjusting for clinical covariates in multivariate analysis. Furthermore, the FunPGS demonstrated superior performance for risk stratification with respect to other recently reported signatures and clinical factors. The complementary value of the molecular signature and clinical factors was further explored, and it was observed that the composite signature called IMCPS greatly improved the predictive performance of survival estimation relative to molecular signatures or clinical factors alone. With further prospective validation in clinical trials, the FunPGS may become a promising and powerful personalized prognostic tool for stratifying patients with CRC in order to achieve an optimal systemic therapy.

MicroRNA-124 regulates TRAF6 expression and functions as an independent prognostic factor in colorectal cancer

An increasing number of studies have confirmed that miR-124 exhibits a suppressive role in glioblastoma, cervical cancer and breast cancer; however, the function of miR-124 in colorectal cancer (CRC) has not been completely elucidated. In the present study, miR-124 expression was confirmed by reverse transcription-quantitative PCR in 80 colorectal tissues and para-cancerous tissues. The influence of altered miR-124 expression was analyzed by statistical approaches including Cox multivariate regression analysis and the Kaplan-Meier method, and the target genes of miR-124 were confirmed by luciferase reporter assays. Immunohistochemical techniques were also performed in order to measure the expression levels of target proteins. miR-124 expression was observed to be decreased in colorectal tissue samples, and this phenomenon was correlated with adverse clinical indicators and poor patient survival time. Luciferase reporter assays indicated that miR-124 directly regulated TNF receptor associated factor 6 (TRAF6) 3′-untranslated region (UTR). Hence, it was proposed that miR-124 dysregulation may negatively influence the expression of TRAF6 and therefore serve as a biomarker of epithelial-mesenchymal transition in CRC tissues. In summary, the present study demonstrated that miR-124 regulates the expression of TRAF6, and may potentially function as an independent prognostic factor and therapeutic target in patients with CRC.