Oral cancer is the sixth most common cancer worldwide. In India, extensive tobacco usage in various forms makes it the leading type of cancer in males and third most common cancer in females. Also, prevalence of oral buccal mucosa cancer type is high in the Indian subcontinent. The treatment modalities of oral cancer are based on various factors including disease stage, access to the oral site, age and physical status of patient. Although surgery is choice of treatment in early stages; radiotherapy holds an important place either alone or as an adjuvant with chemotherapy. Standard radiotherapy protocol involves daily exposure of 2Gy fraction dose for few weeks, where patients receive a cumulative dose of 50Gy to 70Gy during the radiotherapy course. Fractionated radiotherapy kills fast dividing tumour cell population with decreased effects on surrounding normal tissues. Thus this method provides time for normal cells to repopulate and recover while diminishing tumour cells that have aberrantly activated signal transduction pathways. However, sometimes tumour recurs with an acquired radioresistant phenotype posing as an obstruction towards the efficacy of radiotherapy. In order to make radiotherapy more effective; it is important to explore the radioresistant phenotype in cancer cells. Association of several proteins such as p53, Cox- 2, Ras, pAKT, MDM2, Clusterin, Survivin, Bcl-2 and Mcl-1 with radioresistance have been reported earlier. However, so far there is no available tool that can predict radiotherapy response in oral cancer patients leading towards better treatment. Biomedical application of optical spectroscopic techniques like Fluorescence, Fourier transfer infra-red, Diffused reflectance and Raman spectroscopy for classification of different pathological conditions and cancer detection has already been reported. Among these techniques, RS has added advantages like it is label free, sensitive to biochemical variations, applicable to in vitro and in vivo conditions, has minimum interference from water and provides molecular fingerprints. Our previous studies have demonstrated the efficacy of RS in classifying healthy, premalignant and malignant lesions of oral submucosa ; classification of the normal and abnormal exfoliated cells and in the prediction of tumour response towards concurrent chemo-radiotherapy in cervical cancers. We have shown the potential of RS in identifying early transformation changes in oral buccal mucosa, its feasibility in detecting asthma and determining treatment response through serum in asthma patients, in classifying normal and oral cancer serum and in identifying multidrug resistance phenotype in human leukemia and uterine sarcoma cell lines. RS studies related to radiation induced biochemical changes in prostate, lung and breast cancer cell lines irradiated with radiation doses between 15 and 50Gy are reported. These studies were carried out at single doses of radiation that aimed to investigate the in vitro radiation response on human cancer cell lines. On the other hand, we carried out the present study, taking advantage of continuous low dose fractionated irradiation routinely used as standard radiotherapy protocol in clinics for oral cancer treatment. Our aim was to develop in vitro radioresistance character in the cell line over a period of time and then explore the feasibility of Raman spectroscopy to categorize the acquired trait from its parental untreated cells. We have established radioresistant oral cancer sublines of buccal mucosa origin by clinical implementable 2Gy fractionated radiation dose. After establishing the sublines, their radioresistant character was evaluated by clonogenic cell survival assay and Raman spectral profiles were obtained by RS. To the best of our knowledge, we are first to report the utility of RS in acquired radioresistant oral cancer sublines established from parental oral cancer cell line by clinically administered fractionated ionizing radiation. As mentioned above, PCA was used to explore the feasibility of classification among radioresistant 50Gy and 70Gy sublines from the parental cell line. PCA is frequently used method for data compression and visualization to observe the pattern in the data. It is a mathematical analysis by which the features in the whole data set of thousands of points are resolved into a few significant eigenvectors that can express the entire data set with their scores for each spectrum. This can provide imperative clues on biochemical variations among different groups, in our case different classes of macromolecules. Further, the profiles of principal components also known as factor loadings can provide vital clues on biochemical variations among different classes. Loading of factors 1, 2 and 3 that lead to demarcation among groups are presented in Figure-5. Conforming spectral variability as suggested by difference spectra; the loading plots also indicate differences in DNA content, amino acids and protein profiles of parental and radioresistant groups. For visual discrimination, we project each of the spectra in the newly formed coordinate space of selected PCs. First three significant discriminating PCs were selected for three-dimensional visualization of the data. Our data shows that ALIX is recruited transiently into forming HIV-1 as well as EIAV VLPs after polymerization of Gag is complete. ALIX interacts directly with Gag through the p6Gag, NCGag as well as indirectly through ubiquitin binding and TSG101. Since Gag is abundant within the VLP, the sudden recruitment of ALIX into formed VLPs suggests that some or all of interactions between Alix and Gag are modulated during assembly in vivo. The observed ALIX recruitment at the end of Gag assembly supports a model of recruitment where ALIX is recruited to the neck of the formed VLP. It was shown by imaging ESCRT recruitment onto giant unilamellar vesicles that during invagination of membrane, elements of ESCRT I and II proteins are localized to the periphery of the invagination and remain on the cytosolic side of the membrane even after secession of the formed vesicle.