Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic “molecular finger-print” of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.

Advanced Raman spectroscopy (RS) systems have gained new interest in the field of medicine as an emerging tool for in vivo tissue discrimination. The coupling of RS with artificial intelligence (AI) algorithms has given a boost to RS to analyze spectral data in real time with high specificity and sensitivity. However, limitations are still encountered due to the large amount of clinical data which are required for the pre-training process of AI algorithms. In this study, human healthy and cancerous colon specimens were surgically resected from different sites of the ascending colon and analyzed by RS. Two transfer learning models, the one-dimensional convolutional neural network (1D-CNN) and the 1D–ResNet transfer learning (1D-ResNet) network, were developed and evaluated using a Raman open database for the pre-training process which consisted of spectra of pathogen bacteria. According to the results, both models achieved high accuracy of 88% for healthy/cancerous tissue discrimination by overcoming the limitation of the collection of a large number of spectra for the pre-training process. This gives a boost to RS as an adjuvant tool for real-time biopsy and surgery guidance.

Raman spectroscopy (RS) techniques are attracting attention in the medical field as a promising tool for real-time biochemical analyses. The integration of artificial intelligence (AI) algorithms with RS has greatly enhanced its ability to accurately classify spectral data in vivo. This combination has opened up new possibilities for precise and efficient analysis in medical applications. In this study, healthy and cancerous specimens from 22 patients who underwent open colorectal surgery were collected. By using these spectral data, we investigate an optimal preprocessing pipeline for statistical analysis using AI techniques. This exploration entails proposing preprocessing methods and algorithms to enhance classification outcomes. The research encompasses a thorough ablation study comparing machine learning and deep learning algorithms toward the advancement of the clinical applicability of RS. The results indicate substantial accuracy improvements using techniques like baseline correction, L2 normalization, filtering, and PCA, yielding an overall accuracy enhancement of 15.8%. In comparing various algorithms, machine learning models, such as XGBoost and Random Forest, demonstrate effectiveness in classifying both normal and abnormal tissues. Similarly, deep learning models, such as 1D-Resnet and particularly the 1D-CNN model, exhibit superior performance in classifying abnormal cases. This research contributes valuable insights into the integration of AI in medical diagnostics and expands the potential of RS methods for achieving accurate malignancy classification.

Colorectal cancer (CRC), one of the most prevalent types of cancer, requires the discovery of new tumor biomarkers for accurate patient prognosis. In this work, the prognostic value of the tRNA fragment i-tRF-Gly^GCC in CRC was examined. Total RNA extraction from 211 CRC patient cancer tissue specimens and 83 adjacent normal tissues was conducted. Each RNA extract was subjected to in vitro polyadenylation and reverse transcription. A real-time quantitative PCR assay was used to quantify i-tRF-Gly^GCC in all samples. Extensive biostatics analysis showed that i-tRF-Gly^GCC levels in CRC tissues were significantly lower than in matched normal colorectal tissues. Additionally, the disease-free survival (DFS) and overall survival (OS) time intervals were considerably shorter in CRC patients with high i-tRF-Gly^GCC expression. i-tRF-Gly^GCC expression maintained its prognostic value independently of other established prognostic factors, as shown by the multivariate Cox regression analysis. Additionally, survival analysis after TNM stage stratification revealed that higher i-tRF-Gly^GCC levels were linked to shorter DFS time intervals in patients with TNM stage II tumors, as well as an increased probability of having a worse OS for patients in TNM stage II. In conclusion, i-tRF-Gly^GCC has the potential to be a useful molecular tissue biomarker in CRC, independent of other clinicopathological variables.

Colorectal cancer (CRC) is the main cause of cancer-related deaths globally, highlighting the importance of accurate biomarkers for early detection and accurate prognosis. MicroRNAs (miRNAs) have emerged as effective cancer biomarkers. The aim of this study was to investigate the prognostic potential of miR-675-5p as a molecular prognostic biomarker in CRC. For this reason, a quantitative PCR assay was developed and applied to determine miR-675-5p expression in cDNAs from 218 primary CRC and 90 paired normal colorectal tissue samples. To assess the significance of miR-675-5p expression and its association with patient outcome, extensive biostatistical analysis was performed. miR-675-5p expression was found to be significantly downregulated in CRC tissue samples compared to that in adjacent normal colorectal tissues. Moreover, high miR-675-5p expression was associated with shorter disease-free (DFS) and overall survival (OS) in CRC patients, while it maintained its unfavorable prognostic value independently of other established prognostic factors. Furthermore, TNM stage stratification demonstrated that higher miR-675-5p levels were associated with shorter DFS and OS intervals, particularly in patients with CRC of TNM stage II or III. In conclusion, our findings suggest that miR-675-5p overexpression constitutes a promising molecular biomarker of unfavorable prognosis in CRC, independent of other established prognostic factors, including TNM staging.

Introduction Liver regeneration is an exceptionally complex process, orchestrated by a multitude of growth factors and cytoldnes. Tumor necrosis factor-alpha (TNF-a) and interleuldn-6 (11-6) have a pivotal role in the initiation of the regenerative response. Ursodeoxycholic acid (UDCA) exhibits a liver protective effect that enhances liver growth after injury. The aim of the present study is to evaluate the effect of UDCA in the circulating levels of TNF-a and II-6 in rats undergoing extended 80% hepatectomy. Materials and methods Twenty-two male Sprague Dawley rats were randomly assigned in an experimental (UDCA group) and a control group. Mice in the UDC:A-group received oral pretreatment of UDCA for two weeks preoperatively at a dosage of 25 mg/kg/day. An 80% hepatic resection was performed in both groups by resecting the middle, inferior right, and left lateral liver lobes. The experiment ended 48 hours postoperatively. Results UDCA pretreatment significantly depressed circulating levels of both TNF-a and Il-6 after the conclusion of the experiment as compared to the control group (p=0.001 and p=0.01, respectively). Furthermore, TNF-a levels were significantly reduced before the institution of liver injury (p=0.02). Mice in the UDCA-group exhibited better liver growth as demonstrated by significantly increased Ki-67 and mitotic rate (p=0.04 and p=0.02, respectively). Finally, the liver regeneration rate (LRR) was significantly elevated in the experimental group (UDCA group, 54.5% vs control group, 35.8%; p=0.002) signifying enhanced liver growth kinetics. Conclusion UDCA reduces the expression of TNF-a and Il-6 during the priming phase of liver regeneration. An 80% hepatectomy model of acute liver failure exhibited enhanced liver regeneration in the experimental group, plausibly due to the immunomodulatory effects of UDCA.